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Berkman ND, Chang E, Seibert J, et al. Management of High-Need, High-Cost Patients: A “Best Fit” Framework Synthesis, Realist Review, and Systematic Review [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2021 Oct. (Comparative Effectiveness Review, No. 246.)

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Management of High-Need, High-Cost Patients: A “Best Fit” Framework Synthesis, Realist Review, and Systematic Review [Internet].

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Appendix BResults

Results of Literature Searches

The electronic search, grey literature, and reference mining identified 2,923 citations. After title and abstract screening, 873 citations were retrieved for full-text review. A total of 110 studies (117 articles) met eligibility criteria. A total of 110 studies (117 articles) were included in the analyses.

Description of Included Studies

For KQ 1, we identified 60 studies (61 articles), of which 33 were cross sectional, 10 latent class, 11 predictive, and 6 qualitative.1676

For KQ 2, we identified we identified 48 studies (51 articles).12, 17, 22, 2729, 31, 49, 53, 55, 58, 61, 62, 77114 As for unique KQ 2 includes, we identified 10 studies (10 articles).12, 78, 91, 93, 94, 104106, 113, 114

For KQ 3, we identified 19 trials and 21 observational studies (46 articles). Five RCTs were assessed as having low risk of bias, and 14 RCTs (15 articles) were assessed as having some concerns for bias,7982, 84, 86, 87, 90, 96, 97, 99, 108112, 115118 No observational studies were assessed as having low risk of bias, 13 observational studies (17 articles) were assessed as having some concerns for bias, and eight observational studies (9 articles) were assessed as having high risk of bias.83, 85, 88, 92, 95, 98, 100103, 107, 119133

Appendix Figure B-1 is titled “Article flow diagram.” The figure is a flow chart that summarizes the search and selection of articles. There were 2,564 unique records identified from electronic database searching. In addition, 359 records were identified from other sources including handsearching. In total, 2,923 titles and abstracts were screened for potential inclusion. Of these, 873 were deemed appropriate for full-text review to determine eligibility. After full-text review, 756 were excluded: 548 for ineligible population; 5 for ineligible for intervention; 59 for ineligible outcome; 18 for ineligible setting; 57 for secondary review of literature or interventions; 4 for not original research for KQ 3, 39 for ineligible study design; 7 for ineligible comparator; 2 for ineligible timeframe; 13 for irretrievable; 1 for not English; and 3 for duplicates. One hundred and seventeen articles representing 110 studies met inclusion criteria. One hundred and seventeen articles representing 110 studies were included in the synthesis. Sixty studies in sixty-one articles were included for Key Question 1. Forty-eight studies in fifty-one articles were included for Key Question 2. Forty studies in forty-six articles were included for Key Question 3.

Figure B-1Article flow diagram

Note: The sum of the number of studies per KQ exceeds the total number of studies because some studies were applicable to multiple KQs.

KQ = Key Question.

List of Excluded Studies

  • X1: Ineligible Population
  • X2: Ineligible Intervention
  • X3: Ineligible Comparator
  • X4: Ineligible Outcome
  • X5: Ineligible Time Frame
  • X6: Setting/Country
  • X7: Secondary Review of Literature or Interventions
  • X8: Not Original Research for KQ 3
  • X9: Study Design
  • X10: Not English
  • X11: Irretrievable
  • X12: Duplicate
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Detailed Results Tables

Key Question 1. What criteria identify or predict that patients will be high-need, high-cost (HNHC)?

KQ 1a. How do criteria incorporate patient clinical characteristics?

Table B-1Descriptive multivariate, ED visits outcome (n=13)

Author, YearPopulationArthritisCanceraCerebrovascular DiseaseaCongestive Heart FailureaCOPDaCardiovascular DiseaseaDementiaaDiabetesaHeart DiseaseHep CHepatobiliary DiseaseHIV SeropositiveHypertensionPancreatic DiseasePulmonary Circulation DisorderSeizureSickle CellaVascular Disease# of ConditionsCharlson Comorbidity IndexCharlson Severity IndexGeneral Health ScoreGlobal Physical Health ScoreHealth StatusHierarchic Categorical Condition ScoreMorse Fall ScoreQuan-Charlson Score
Buhumaid, 201532Hospital/health system+
Hasegawa, 201435Hospital/health system+++
Hasegawa, 201436Hospital/health system++++
Chang, 201440Hospital/health systemNS+NSNS
Chukmaitov, 2012134Hospital/health system+
Milbrett and Halm, 200942Hospital/health systemNS
Mandelberg, 200046Hospital/health system++++++
Surbhi, 202069Hospital/health systemNS+
Vinton, 201434Population based+++++NS+
Hunt, 200643Population based+
Friedman, 200944Population based+
Zuckerman and Shen, 200447Population based+
Kanzaria, 201967Population based++++++
Thakarar, 201533Single program++
Doran, 201359VA+++-+
a

Complex chronic condition

+ denotes significant positive association, - denotes significant negative association, NS denotes no significance, NR denotes not reported, and a blank cell denotes not included in the model.

COPD = chronic obstructive pulmonary disease; ED = emergency department; HIV = human immunodeficiency virus; NS = not statistically significant; n = number; VA = Veterans Health Administration.

Table B-2Predictive, ED visits outcome (n=3)

Author, YearPopulationArthritisaCongestive Heart FailureaCOPDaDementiaaDiabetesHypertensionRenal Disease# of ConditionsCCIHierarchic Categorical Condition Score
Billings and Raven, 201341Medicaid-
Colligan, 201631Medicare++++++NS-+
Brannon, 201877Hospital/health system+
a

Complex chronic condition

+ denotes significant positive association, - denotes significant negative association, NS denotes no significance, NR denotes not reported, and a blank cell denotes not in the model.

CCI = Charlson Comorbidity Index; COPD = chronic obstructive pulmonary disease; ED = emergency department; NS = not statistically significant; n = number.

Table B-3Descriptive multivariate, IP visits outcome (n=2)

Author, YearPopulation# of ConditionsCCIMorse Fall Score
Bell, 201718Hospital/health system+
Porter, 201950Hospital/health system+++
Surbhi, 202069Hospital/health system+

+ denotes significant positive association, - denotes significant negative association, NS denotes no significance, NR denotes not reported, and a blank cell denotes not included in the model.

CCI = Charlson Comorbidity Index ; IP = inpatient; n = number.

Table B-4Predictive, IP visits outcome (n=3)

Author, YearPopulationArthritisCanceraChronic Kidney DiseaseaCongestive Heart FailureaCardiovascular DiseaseaDiabetesHypertension# of Conditions
Hempstead, 2014135Hospital/health system+
Leininger, 2014136; Leininger, 2015137Medicaid+
Chang, 201954VA+++++++
a

Complex chronic condition

+ denotes significant positive association, - denotes significant negative association, NS denotes no significance, NR denotes not reported, and a blank cell denotes not included in the model.

IP = inpatient; n = number; VA = Veterans Health Administration.

Table B-5Descriptive multivariate, all visits outcome (n=2)

Author, YearPopulationCharlson Severity IndexGlobal Physical Health Score
Rohrer, 200820Hospital/health system+
Blumenthal, 201748Hospital/health system+

+ denotes significant positive association, - denotes significant negative association, NS denotes no significance, NR denotes not reported, and a blank cell denotes not included in the model.

n = number.

Table B-6Predictive, all visits outcome (n=3)

Author, YearPopulationaCOPDaDiabetesHypertension# of ConditionsCCIOther
Meek, 200024Commercial (including HMO)++NS
Wherry, 2014138; Leininger and Avery, 2015137Population based+
Reichard, 201523Population based+/NSDisability +
a

Complex chronic condition

+ denotes significant positive association, - denotes significant negative association, NS denotes no significance, NR denotes not reported, and a blank cell denotes not included in the model. +/NS denotes performance across multiple models within the study, ranging from a positive significant association to no significance.

CCI = Charlson Comorbidity Index; COPD = chronic obstructive pulmonary disease; HMO = health maintenance organization; NS = not statistically significant; n = number.

Table B-7Descriptive multivariate, cost outcome (n=3)

Author, YearPopulationCanceraCerebrovascular DiseaseaCardiovascular DiseaseaDiabetesConditionGeneral Health ScoreQuan-Charlson ScoreOther
Bayliss, 201656Commercial (including HMO)++
Robinson, 201619Commercial (including HMO)NSNS+-NS+
Figueroa, 201971Medicare+++
a

Complex chronic condition

+ denotes significant positive association, - denotes significant negative association, NS denotes no significance, NR denotes not reported, and a blank cell denotes not included in the model.

HMO = health maintenance organization; NS = not statistically significant; n = number.

Table B-8Predictive, cost outcome (n=1)

Author, YearPopulationaDiabetesHypertensionPulmonary Circulation Disorder
Yang, 201825Medicaid+++
a

Complex chronic condition

+ denotes significant positive association, - denotes significant negative association, NS denotes no significance, NR denotes not reported, and a blank cell denotes not included in the model.

n = number.

KQ 1b. How do criteria incorporate patient demographic, behavioral health, and social risk factors?

Table B-9Multivariate descriptive results

Author, YearOutcomePopulationFemaleAgeRace/EthnicityDepressionSevere Mental IllnessOther Mental IllnessSubstance UseAlcohol UseHomelessnessEmploymentOther Social Risk Factors
Bayliss, 201656Cost (top 25%)Commercial (including HMO)+Over 55++
Robinson, 201619Cost (top 10% of all-cause total costs)Commercial (including HMO)NSNSNSNS/++NSNS
Sterling, 201821Cost (top 20%)Commercial (including HMO)+Any financial burden +
Walker, 200351Cost (adjusted annual cost ratios of median healthcare costs)Commercial (including HMO)NRNRNRPTSD+
Berkowitz, 201870Cost (top 10%, 5% & 2%)Population basedNS+

Black: +

White: +

Hispanic:

Food insecurity: +

Ed: NS

Income: NS

Figueroa, 201971Cost (top 10%)Medicare--

Black: +

Hispanic: +

+

Income: +

Dual elig: +

Behr, 201630ED visits (2+, 3+, 4+, 5+)Hospital/health system+Black +/NS++Employed +
Bell, 201718Inpatient visits (3+)Hospital/health systemNRNRNS+++Community-level income <48k NS
Blumenthal, 201748All utilization (ED visits, acute hospitalizations)Hospital/health systemNRNRNRLowest quartile in Global Mental Health score NS
Buhumaid, 201432ED visits (4+)Hospital/health systemNSAge (40+) NSBlack +-++
Chang, 201440ED visits (4+ in 12 months)Hospital/health systemNSNSNot in modelPersonality dx +

SUD dx NS

Positive cocaine screen+

NS+
Chukmaitov, 2012134ED visits (1+, 1–3, 4+)Hospital/health system+

Age (30–39)

-

(40–49) NS

(50–64) +

(65–74) +

(75–84) +

(>85) -

Black +

Hispanic +

Other -

Doran, 201438ED visits (3+)Hospital/health systemNRNRNR
Hasegawa, 201435ED visits (3+ ED, 30-day ED revisit, IP visit)Hospital/health system-Age (<74) +

Black +

Hispanic +

Other NS

++Quartiles for median household income of patient’s zip code 1 (lowest) and 2 + 3 NS
Hasegawa, 201436ED visits (3+)Hospital/health system-Over 54++/NS+++Lowest quartile median income +
Hasegawa, 201437ED visits (0, 1–2, 3–5, 6+)Hospital/health systemNS/-40–54 NS/+NSLowest quartile median income NS
Liu, 201339ED visits (4+, 4–7, 8–18, 19+)Hospital/health systemNRNRNR+++
Mandelburg, 200046ED visits (5+)Hospital/health systemNSAge (30–59) +

Black +

Hispanic -

Native American +

Asian -

Other -

++
Surbhi, 202069ED visitsHospital/health systemNSBlack ++
Castillo, 201865ED visits (>5) vs 1–5326 hospitals in CA-75–84 & 85+ vs 65–73 -

Black: +

Hispanic +

Asian/Pacific Islander -

++
Millbrett, 200942ED visits (>6)Hospital/health system-Nonblack +NSPart-time employment retired/unemployed,+
Porter, 201950Inpatient visits (3+)Hospital/health systemNRUnder 40+NR++
Emechebe, 2019Inpatient visits (2+)Medicaid managed care and Medicare Advantage health planNRNRNRNRNRNRNRNRHousing support need +Needs: Financial assistance +; food +; transport-ation +, medication assistance +
Surbhi, 202069Inpatient visitsHospital/health systemNS

Black –

Black: NS

+
Rohrer, 200820All utilization (27+ outpatient visits)Hospital/health systemNSNSNot in model
Ruger, 200445ED visits (1, 2, 20+)Hospital/health systemNRNRNot in model
Kanzaria, 201967ED visits (4–7)Medicaid MCO in one city-Older: -

Black: +

Hispanic: NS

Asian: -

++++++Interaction with county jail system: +
Thakarar, 201533ED visits (2+)Hospital/health system (single program)NS55+ NSMinority NSNSNSNSMore than 1 housing change NS
Freidman, 200944ED visits (4+)Population basedNSNS

Income

<$40k +

<$90k NS

Hunt, 200643ED visits (1–3, 4+)Population basedNS+

Income by poverty threshold

200–399% +

100–199% +

Below threshold +

Vinton, 201434ED visits (0, 1–3, 4–9, 10+)Population based+>44-

Black +

Hispanic -

Asian -

Other NS

+NSCurrently employed -Poverty–income ratio (Ref is ≥4) +
Zuckerman, 200447ED visits (3+)Population based

Black +

Hispanic NS

Asian NS

Near poor and poor + Single parent +
Doran, 201359ED visits (0, 1, 2–4, 5–10, 11–25, >25))VA-++++++
Levinson, 2005139All utilization (3+ ED; 2+ specialty clinic visits)VANRNRNR

+ denotes significant positive association, - denotes significant negative association, NS denotes no significance, NR denotes not reported, and a blank cell denotes not included in the model.

CA = California; dx = diagnosis; Ed = education; ED = emergency department; HMO = health maintenance organization; IP = inpatient; k = thousand; NR = not reported; NS = not statistically significant; PTSD = post-traumatic stress disorder; Ref = reference group; SUD = substance use disorder; VA = Veterans Health Administration; vs = versus.

Table B-10Predictive results

Author, YearOutcomePopulationFemaleAgeRace/EthnicityDepressionSevere Mental IllnessOther Mental IllnessSubstance UseAlcohol UseHomelessnessEmploymentOther Social Risk Factors
Meek, 200024All utilization (6+ visits in 6 months)Commercial (including HMO)+Under 40 -NR+/NS/-
Brannon, 201877ED visits (1+, 2+, 3+, 4+, 5+)Hospital/health systemAge + but not definedZip code+ but not defined
Hempsted, 2014135Inpatient visits (1+, 2, 3+)Hospital/health system-

Age (Ref is 80+)

18–34 +

35–49+

50–64 +

65–79 +

Race/ethnicity (Ref is white)

Asian/PI -

Black -

Hispanic -

Other +

++HH income < $34.999k +
Hwang, 2014140ED visits (4+ visits in both years; 4+visits either year; <4 visits in both years)Hospital/health system-Nonwhite+NRNRLow median household income+
Billings, 200762Inpatient visits (1+ hospital admission in next 12 months)MedicaidNRNRNR+++
Billings, 201341ED visits (3+)MedicaidNot in model
Leninger, 2014, 2015136, 137Inpatient visits (1+ inpatient hospitalization)MedicaidNRNRNR+++
Raven, 200860Inpatient visits (1+ readmission)Medicaid
Yang, 201722, 53Cost (persistent top 10% decile)MedicaidNRNRNR
Yang, 201825Cost (top 10% expenditures in next 12 months)MedicaidNRNRNR
Colligan, 201631ED visits (0, 1–3, 4+)Medicare

≤64 +

≥75 +

Race (Ref is white)

Black +

Asian -

Hispanic -

Native American +

Unknown/other -

+
Kanzaria, 201728ED visits (4+ visits in 12 months)Population based

Black +/NS

Hispanic +

Other -/NS

+/NS-/NS+/NS+/NS+/NSPoverty indicator NS
Reichard, 201523All utilization (≥75th percentile)Population basedNRNRNR
Wherry, 2014137, 138All utilization (top decile)Population basedNS
Chang, 201954Inpatient visits (persistently high risk, intermittent, or low risk in 2 years)VA-Over 45+

Race (ref Hispanic)

White -

Black -

Other - (p<0.05)

++ (tobacco)-

Number of zip code changes +

Urban+

+ denotes significant positive association, - denotes significant negative association, NS denotes no significance, NR denotes not reported, and a blank cell denotes not included in the model.

ED = emergency department; HH = household; HMO = health maintenance organization; NR = not reported; NS = not statistically significant; PI = Pacific Islander; Ref = reference group; VA = Veterans Health Administration.

Key Question 2. What are the mechanisms that lead to reductions in potentially preventable or modifiable healthcare use and result in improved health outcomes and cost savings in interventions serving HNHC patients?

Table B-11Full list of CMO configurations and supporting data for Program Theory 1: Identifying and targeting HNHC patients for inclusion in interventions

Summary: Identifying and targeting HNHC patients for interventions intended to reduce potentially preventable or modifiable healthcare use and costs requires capturing their complexity based on a combination of prior use of healthcare services, chronic disease, nonmedical barriers to accessing care, experience with the healthcare system, clinician judgment, and patient willingness to participate.a

CMOsRelevant Data Extracts/Summary Information From Included Literature
CMO 1.1. Including historic information obtained from claims and other electronic health records, that can identify prior high cost and/or use of healthcare services [C], adds to intervention designers’ confidence in creating an algorithm [M] that will result in targeting patients who are at the greatest risk of being HNHC in the future [O].

Participants were drawn from adult Medicaid patients attributed to CareMore primary care physicians (PCPs). Program eligibility criteria were aimed at identifying patients at risk for poor outcomes and unnecessary spending, as well as those most likely to benefit from complex care management. The criteria drew from analyses suggesting that combining predictive models, historical claims, and clinician judgment is the most effective approach to identifying patients for complex care management. Eligible patients were first required to meet at least 1 of the following criteria: top 5% of total medical expenditures (TME) in the prior 12 months, top 5% of Chronic Illness Intensity Index (CI3) score, or care team member nomination.108

Predicted future health care costs are based on the Predictive Risk Intelligence SysteM (PRISM) medical cost risk score developed and implemented by Washington State Medicaid programs (Court et al. 2011). PRISM combines diagnostic and pharmacy data to predict future expenditures based on grouping algorithms from two risk adjustment models widely used by State Medicaid programs, the Chronic Illness and Disability Payment System (Kronick et al. 2000) and Rx-Medicaid (Gilmer et al. 2001), both of which have predictive accuracy comparable to commercial alternatives. The PRISM risk score is a ratio calculated by dividing the individual’s expected monthly future expenditures by the average monthly future expenditures of all Medicaid SSI recipients. The risk score equals 1.0 if the individual’s expected expenditures equal the average expenditures of the group.112

Rely on more than 1 type of data-because it may be vulnerable to inaccuracies: One program said they use predictive modeling so that they are not “held hostage waiting for claims to come down the road.” Washington State uses predictive modeling to begin to identify the target population. Using its Health Service Encounter algorithm, the state examines 15 months of integrated health care claims to determine future medical costs and inpatient risk scores. The state has found that conditions such as diabetes, cardiovascular disease, MH and substance abuse are common among the superutilizing subset of patients. It uses different approaches to further stratify subgroups for complex care management including identifying individuals with extreme emergency department utilization (e.g., approximately 80 to 130 ED visits in 15 months), high expected future medical costs (predicted by high utilization and costs in the past), high prospective inpatient risk scores, and sign gaps in care and quality indicators.49

The programs represented at the Summit generally use historical claims data as a foundation to understand the size and scope of super-utilization. Claims analysis is an iterative process and includes identifying areas of high cost and high utilization, and/or identifying groups of recipients with a high number of diagnoses. With this initial broad brush information, programs are able to further shape and define the target population. For example, Community Care of North Carolina (CCNC), which includes 14 regional networks that manage the care of Medicaid beneficiaries, will analyze at least 12 months of data in order to understand which chronic illness and mental health indicators are contributing to a high number of ED visits.49

“After an individual’s records were linked across data sets, the process of flagging individuals as ‘‘high utilizers’’ began. Instead of using charges or receipts to define high utilizers, the decision was made to rely on the number of emergency department (ED) and inpatient visits made by an individual over the prior 12-month period.”78

“From the best-fit synthesis we conducted to answer KQ 1, we identified some characteristics that are often available through electronic databases that can be used to help identify patients.” (Discussion section of this report. Also see full results for KQ 1)

During the first site visit, physicians at both sites reported that they were initially very enthusiastic about the Health Buddy® program, because it offered a promising way to effectively support patients with chronic disease….. Once the physicians received the list of patients who were eligible for the Health Buddy® program, they reported that they became frustrated with the project because they felt that many of the patients selected would not benefit from participating. Further, physicians reported disappointment that many of the patients they believed could be helped by the program were not eligible to participate in the program because they had not been identified through the claims based algorithm developed by HHN.80

Risk assessments of the TST participant population were conducted to inform the development of individualized care plans and assign participants to one of three risk categories to determine the level of service to be provided to each participant.…TST reported that the high-risk intervention was provided to approximately 5% to 7% of the TST participant population that had depression and/or potentially critical health problems that required immediate attention.…What might explain the lack of success in TST’s demonstration? Ineffective Targeting. One explanation may be the inability to accurately target beneficiaries at greatest risk of intensive, costly, service use (as distinct from the need for general care management). …When TST learned that one of its participants was admitted to the hospital, it reassigned this individual to its high-risk intervention, and when appropriate, a care manager visited the beneficiary in the hospital to determine the cause of the hospitalization and identify any new health or social issues to be addressed. Not surprisingly, TST adopted a strategy of targeting beneficiaries at greatest risk of a hospitalization and higher costs. Their targeting strategy was unsuccessful—and costly. The program was unable to predict future complications with any precision for those with initially stable, less costly, conditions. Lacking direct access to patients’ medical records, the health coaches often began working with beneficiaries with incomplete information.79 There are two key elements to the success of these new efforts to target and improve care for high-cost Medicaid cases. First, it is essential to be able to identify in advance patients who are likely to have high costs in the future. Many high-cost occurrences (such as injury, acute illness, or cancer) might be episodic, and high spending in one year might not mean high spending in subsequent years.62

We conducted a retrospective analysis of secondary data from the Medicare program and other linked sources. We used 3 databases, including the Chronic Condition Data Warehouse, hierarchic categorical condition scores, and timeline files. The Chronic Condition Data Warehouse includes fee-for-service billing history for services reimbursed under Medicare Parts A, B, and D, as well as data about beneficiary demographic characteristics, linked at the beneficiary level with a unique identification number.31

We used data from three health systems to develop, evaluate, and implement a model for the prediction of high ED utilization in Washtenaw and Livingston counties: Michigan Medicine and St. Joseph Mercy Health System (operators of all EDs in Ann Arbor and Livingston County) and Integrated Health Associates (IHA), a multispecialty medical practice with clinical sites in both counties.77

Administrative data from DH’s data warehouse were used to obtain demographic, medical, psychological/behavioral health and service utilization and claims data. The tight administrative and clinical integration among all care settings facilitates data capture across the continuum of care.83

After an individual’s records were linked across data sets, the process of flagging individuals as ‘‘high utilizers’’ began. Instead of using charges or receipts to define high utilizers, the decision was made to rely on the number of emergency department (ED) and inpatient visits made by an individual over the prior 12-month period. This step eliminated potential variability related to differences in treatments and payers. Rather than assigning an artificial cutoff, the nuances of the local population were allowed to set the threshold for what constituted high utilization. The Coalition defined high utilization as ‘‘any individual with total emergency or inpatient visits greater than 1.5 standard deviations above the mean.’’ This definition resulted in any individual with 3 or more inpatient visits, or 6 or more ED visits, being flagged in the database as a high utilizer.78

The site for this study was the Duke Outpatient Clinic (DOC), a large primary care safety net clinic in Durham, North Carolina. Patients at the DOC have a high prevalence of multimorbidity, mental illness, and socioeconomic challenges. Beginning in 2012, the clinic initiated an extensive redesign process to better meet complex population health needs and reduce avoidable utilization of ED and inpatient care. This study was conducted to direct further quality improvement efforts. Multiple methods were applied, including both retrospective quantitative analysis of clinical data and an in-depth chart review. The study team extracted electronic health record data for all patients enrolled at the DOC between July 1, 2014, and June 30, 2015. The team matched these data to ED encounter data for the same year period from 2 local hospitals within the Duke University Health System, where DOC patients receive a vast majority of their emergency and hospital care. Lastly, the team conducted 30 chart reviews for 10 of the highest ED utilizers for each of the 3 leading chief complaints to uncover additional details surrounding their frequent ED use patterns.17

The literature contains varying definitions for super-utilizer. The definition used for this analysis was adapted from the work of Johnson, et al. and defined super-utilizers as adult patients (≥ 18 years of age) who, along with having an admission (analysis index admission) during the requisite timeframe, had at least two other admissions in the year prior, or at least one other admission along with a serious mental health diagnosis… In order to focus the analysis on cost savings that could be linked to our interventions, patients on chemotherapy, patients with orthopedic complications, patients diagnosed with HIV, and patients who had repeated admissions for emergency dialysis were excluded from analysis.83

CMO 1.2 Capturing a patient’s use of services in “real time,” if possible, while the patient is still hospitalized [C], adds to the intervention service provider’s confidence that a patient is identified during a period of urgent need for the services [M], resulting in intervention services being initiated prior to or during a period of high use and not during a later period, when service use may have already declined (regression to the mean) [O].

More than half of patients approached in the ED refused to participate, possibly due to competing concerns about their illness or participating in research. Those who enrolled may have been more engaged, and thus, more likely to respond to the intervention, than those who declined. Identifying and approaching patients in real time may be time consuming and resource intensive, but this approach has been found to be more effective in addressing the needs of some populations of high utilizers than using historical claims data to identify and ‘‘segment’’ high utilizers with aligned interventions.110

To be most effective, complex care management programs should target patients at risk of persistently high spending and those whose spending and health outcomes are amenable to complex care planning and engagement. Many complex care management programs use claims data and historical utilization patterns to identify eligible patients. Recent research has highlighted the limitations of this approach—historically high-cost patients often return to normal patterns or spending, or they have drivers of high spending not amenable to complex care management.108

In order to identify chronic frequent users, rather than those with an isolated health event requiring multiple visits, we identified patients with the most ED visits during both the 30-day period and the 12-month period preceding the introduction of the program.111

The Summit participants unanimously agreed that access to real-time information—such as notifications of ED visits or inpatient admissions—and a strong analytics team provide a critical foundation for super-utilizer programs. One leader referred to data as “oxygen for our program.” Programs place a high priority on developing a robust data repository that can be mined to identify groups of patients that might respond well to complex care management.49

Eligible patients were identified through real-time automated methods and recruitment occurred while patients were still hospitalized. Patient intake included an in-depth patient assessment to determine nonmedical barriers to improved health.83

This “real time” approach of using a hospital admission as a triggering event was perceived as useful for two reasons. First, patients with a hospital admission are much more likely to have a subsequent admission in the next twelve months than patients without an admission, which improves the potential case-finding capacity of the algorithm. But, equally important, effective discharge planning is likely to be a critical component of any intervention strategy for high-cost, high risk patients. However, because of limited resources and lag time in acquiring data, our experience in other environments has suggested that some providers and payers are interested in non–“real time,” retrospective analyses. Accordingly, we also examined patients with any claims in 2000–2003, to predict subsequent admissions in 2004 (regardless of whether they had a hospital admission in 2003 or any prior year). This “archival” approach to case finding is somewhat less robust (it finds fewer patients) than the “real time” method and only brief findings for this approach are presented for comparative purposes.62

Risk assessments of the TST participant population were conducted to inform the development of individualized care plans and assign participants to one of three risk categories to determine the level of service to be provided to each participant.…TST reported that the high-risk intervention was provided to approximately 5% to 7% of the TST participant population that had depression and/or potentially critical health problems that required immediate attention.…What might explain the lack of success in TST’s demonstration? Ineffective Targeting. One explanation may be the inability to accurately target beneficiaries at greatest risk of intensive, costly, service use (as distinct from the need for general care management). …When TST learned that one of its participants was admitted to the hospital, it reassigned this individual to its high-risk intervention, and when appropriate, a care manager visited the beneficiary in the hospital to determine the cause of the hospitalization and identify any new health or social issues to be addressed. Not surprisingly, TST adopted a strategy of targeting beneficiaries at greatest risk of a hospitalization and higher costs. Their targeting strategy was unsuccessful—and costly. The program was unable to predict future complications with any precision for those with initially stable, less costly, conditions. Lacking direct access to patients’ medical records, the health coaches often began working with beneficiaries with incomplete information.79

What might explain the lack of success in the Phase II KTBH Demonstration? One explanation may be the targeting of beneficiaries at greatest risk of intensive, costly, service use (as distinct from the need for general care management). Responding to KTBH’s request, CMS staff selected a very costly, complex set of Medicare beneficiaries for their intervention and comparison groups. As a result, the comparison group exhibited substantial regression-to-the-mean (RtoM) effects. While the randomized experimental design should cancel out RtoM effects and isolate a pure intervention effect, the large churning of beneficiaries from lower (higher) to higher (lower) cost groups over time adds considerable statistical noise to the test of savings. Even still, we would have considered the Phase II original intervention to be a success if it had saved 5.4% of costs. Large increases in demonstration period costs in less costly beneficiaries in the base period make it very difficult for intervention staff to target those at highest financial risk. It is much easier to target beneficiaries during the intervention period who actually incur major flare-ups and hospitalizations. Unfortunately, these beneficiaries have already incurred major expenditures by the time they receive intensive disease management services.82

CMO 1.3. Considering patients’ chronic conditions, functional limitations, and clinical severity scores [C] adds to intervention designers’ confidence in identifying HNHC patients [M] who will benefit from intervention services [O1] and who are at risk of having future high healthcare use and cost [O2].

Three categories of high-cost users—beneficiaries who had multiple chronic conditions, were hospitalized, or had high total costs—were identified by CBO for study of persistence of Medicare expenditures over time. Beneficiaries that were selected based upon hospitalization or being in the high total cost groups had baseline expenditures that were four times as high as expenditures for a reference group. Beneficiaries selected based upon presence of multiple comorbid conditions had baseline expenditures that were roughly twice as high as expenditures for a reference group. Subsequent years of costs remained higher for all three cohorts than the reference group; however, total expenditures declined the most for those beneficiaries who were identified as high cost due to a hospitalization followed by beneficiaries who had had high total costs in the base year. Subsequent costs were virtually unchanged for beneficiaries with multiple chronic conditions.81

Denver Health reported challenges in using utilization data alone to find patients at chronic high risk of acute care use—but identifying these patients was important for the success of 21st Century Care. That is, Denver Health assumed that 21st Century Care could reduce service use (such as hospitalizations and ED visits) by identifying patients with chronic care needs and then delivering preventive care to preempt higher-cost acute care later on.…Over the course of the award, however, Denver Health learned that many of its highest-cost patients were only temporarily high cost, suggesting that many of them would have returned to moderate- or low-cost status even without intervention. For example, under its risk stratification algorithm, Denver Health identified so-called super utilizers—all of whom were Tier 4—as people with three or more hospital admissions in a 12-month period, or two or more admissions and a mental health diagnosis. These people accounted for about 30 percent of adult facility costs. By analyzing pre-intervention data, however, research staff at Denver Health showed that, even without special intervention, fewer than half of these super utilizers at a single point in time were still in the category seven months later, and only 28 percent were in the category at the end of 12 months (Johnson et al. 2015b). Because of this challenge using utilization data alone to find chronic high-risk patients, Denver Health, as noted previously, added clinical information (in the form of both CRGs and clinical data such as lab results) to its second and third iterations of the risk-stratification algorithm (although lab results were later removed in subsequent algorithm iterations). Denver Health reported that each revision to the algorithm helped to identify patients who would benefit most from 21st Century Care’s intensive services.85

Denver Health recognized that people with exceptionally high service use at one time did not necessarily continue to have exceptional service use in the future. Over the course of the award, Denver Health integrated clinical information into its risk-stratification algorithm to try to better identify patients who would benefit from intervention.85

Key Finding #1: Several vulnerable subpopulations of Medicare FFS beneficiaries were less likely to agree to participate in the CLM demonstration program. Of all CLM intervention beneficiaries, 65% verbally consented to participate in the CMHCB demonstration at some point during the intervention period. We found that Medicaid enrollees and institutionalized beneficiaries were less likely to be participants; both groups are costly and high users of acute care services. In general, participants tended to be healthier than nonparticipants using baseline characteristics including the prospective HCC score. However, beneficiaries with higher concurrent HCC scores based on the first 6 months of the demonstration were more likely to participate than healthier beneficiaries. This suggests that CLM made some inroads into engaging those with acute clinical deterioration. Further, as CLM’s program matured, they appeared to be more successful engaging sicker and more costly beneficiaries based on baseline health status; however, those with Medicare/Medicaid dual enrollment and the institutionalized were still less likely to become participants. These findings suggest alternative recruiting and outreach strategies are needed to reach dual Medicare/Medicaid enrollees and beneficiaries who are institutionalized.84

Key Finding #1: The HBC program was able to engage beneficiaries who were at higher risk of acute clinical deterioration as measured by the concurrent HCC score. Of the HBC original intervention beneficiaries, 45% verbally consented to participate in the CMHCB demonstration at some point during the intervention period; 40% of the refresh population agreed to participate. For the HBC program, we find that beneficiaries with medium and high concurrent HCC scores were more likely to be participants. Beneficiaries with higher prospective HCC scores and baseline Charlson comorbidity scores were less likely to be participants. This suggests that the HBC program was less able to engage the historically sicker Medicare beneficiaries but more able to engage those at higher risk of acute clinical deterioration as measured by the concurrent HCC score.80

While the high-need patient population is diverse, a synthesis of analyses reported in the literature identified three criteria that could form a basis for defining and identifying this population: total accrued health care costs, intensity of care utilized for a given period of time, and functional limitations.12

Medicare FFS beneficiaries with a primary residence in one of five designated counties including Boston, Massachusetts, and surrounding areas, and a high level of disease severity as indicated by Hierarchical Condition Categories (HCC) scores and high health care costs based on Medicare claims filed during calendar year 2005. Beneficiaries with HCC risk scores >=2.0 and annual costs of at least $2,000 or HCC risk scores >=3.0 and a minimum of $1,000 annual medical costs are eligible for the MGH’s CMP.86

Eligible patients were identified by using standard criteria: a risk score in the 90th percentile for 90-day hospitalization from a validated risk-prediction algorithm (13) and a recent hospitalization or emergency department visit.87Although high utilizers differed significantly from other patients in their medical and behavioral health needs, their presenting complaints were not categorically different from those of low utilizers—they simply had more visits for the same types of complaints utilizers were more likely to present to the ED multiple times for the same complaint. However, most high utilizers had 4 unique chief complaints, suggesting that these patients generally have several, rather than a few, reasons for seeking emergency care. No clearly defined pattern of complaints existed for high utilizers. High utilization in such patients is less likely to be caused by clearly defined disease processes and more by a complex mix of multiple chronic medical conditions and psychosocial factors, making it difficult to predict future utilization or identify specific patient needs based on their chief complaint.17

Predictive modeling is a common tool used by super-utilizer programs to identify who might be at risk for super-utilizing in the future. One program said they use predictive modeling so that they are not “held hostage waiting for claims to come down the road.” Washington State uses predictive modeling to begin to identify the target populations. Using its Health Service Encounter algorithm, the state examines 15 months of integrated health care claims to determine future medical costs and inpatient risk scores. The state has found that conditions such as diabetes, cardiovascular disease, mental health and substance abuse are common among the super-utilizing subset of patients. It uses different approaches to further stratify subgroups for complex care management including identifying individuals with extreme ED utilization (e.g., approximately 80 to 130 ED visits in 15 months), high expected future medical costs (predicted by high utilization and costs in the past), high prospective inpatient risk scores, and significant gaps in care and quality indicators.49

CMO 1.4. Considering patients’ behavioral health and social risk factors (e.g., mental health and substance use disorder diagnoses and social needs) [C] adds to intervention designers’ confidence in identifying HNHC patients [M] who will benefit from intervention services [O1] and who are at risk of having future high healthcare use and cost [O2].

The screening process includes administrative data screening to determine eligibility by usage criteria, followed by an in-person screening to determine other eligibility criteria and ability to consent. Randomization occurred after consent. A proprietary platform integrates study data with real-time data feeds from multiple sources. Staff screened potential participants based on their use of county-funded services over the prior 1–2 years. Our research team developed an electronic triage tool that uses administrative data to predict the likelihood of future high use of county-funded services. To meet criteria, potential participants must have used various combinations of the ED and psychiatric ED, medical and psychiatric inpatient stays in the County-funded public hospital, and/or jail over the past 1–2 years, at high enough levels to meet a threshold score. We embedded the triage tool into the study database and generated a list of potentially eligible participants with the highest scores, redoing the calculation throughout the enrollment period. All county agencies or service providers could refer individuals they suspected met eligibility criteria, but study staff always used the list generated by the triage tool to confirm initial eligibility. County staff used this list to outreach to the highest using individuals.109

Individuals with disability, transportation challenges, homelessness, mental health conditions, and with substance abuse or chemical addiction may be hard to find and engage when they are not actively in treatment. Partnering with public health agencies and community-based organizations was identified as an approach that allows health care organizations to more successfully identify and engage these hard-to-reach populations, some of whom harbor mistrust of health care professionals. Although such partnerships would ideally build on shared data for surveillance, programs had developed approaches in the absence of a formal data sharing system. For example, the Gatekeeper program in Ohio developed a community referral model to identify high risk individuals in partnership with a network of trained community volunteers such as bank tellers, police officers, paramedics, and pharmacists. Volunteers initiated referrals directly to the Gatekeeper program for older adults who identified as being potentially at risk or who might benefit from community services.113

The data on diagnostic history and characteristics of subsequent admissions may also provide some help in conceptualizing intervention design. The relatively high rates of chronic disease suggest the importance of a comprehensive, multidisciplinary approach to any intervention, using what we already know about improving chronic disease management (such as the chronic care model). But the extraordinarily high levels of substance abuse among high-risk patients and the history of mental illness even among the population without serious and persistent mental illness make clear that any intervention will have to take these factors into account. Whatever is on the shelf from chronic disease management vendors for commercial plans and Medicare will require a serious overhaul for adaptation to these populations.62

There are also other important questions that remain unanswered. From claims records we can say little about the social and personal characteristics of these patients. This is a population living in extreme poverty, and a broad range of factors (educational, behavioral, and coping capacity) likely complicate their lives. We have documented their mental illness and substance abuse problems, and there are also potentially high levels of homelessness and housing instability. Getting more and better information about these issues will require further work, but it is clearly critical to any intervention design. However, the potential impact of solving these problems may also be large, even for the most apparently daunting problems such as the high number of mental illness admissions. For some high-risk patients, an effective, supportive housing environment might be enough to tip the balance, allowing sufficient life stabilization to address previously intractable health and mental health problems. An emerging body of research indicates that these “social service” interventions can have a major impact on the use of health services.62

Our findings also show the importance of including patients with mental health disorders in an intervention program. John Billings and Maria Raven noted that more than a third of high utilizers have at least one claim with a mental health disorder diagnosis. Other studies have However, more than half noted that people with mental health disorders have higher rates of receiving ED and inpatient care.20 Most of the patients enrolled in our study had either depression or anxiety. While B2C did not target people with severe mental health needs (such as those recently hospitalized at a psychiatric facility), to our knowledge, the program is unique in having a behavioral health provider screen every enrollee for mental health disorders—and then address those conditions as appropriate.88

Participating super-utilizer programs reported a high prevalence of behavioral health diagnoses in high-utilizers through claims data. Indeed per capita Medicaid costs increase significantly with the addition of a mental health diagnosis, substance abuse diagnosis, or mental health plus substance abuse diagnosis49

All stakeholders identified poorly managed serious mental illness among HNHC patients as a significant driver of preventable high health care utilization. Patients often had inadequate access to mental-health and substance-abuse resources. This was because outpatient programmes did not exist, were inconveniently located or were not financially feasible to attend. This left patients without any options other than the ED for care. Additionally, several patients acknowledged that feeling depressed negatively impacted their care routines and contributed to missing provider appointments which, over time, compounded the severity of their diseases. Importantly, patients also pointed out that the stigma surrounding mental illness was detrimental to their desire to seek out treatment even if it were available. Some patients also felt that policies such as the Florida Mental Health Act (known as the Baker Act) and its equivalent in New York State (known as Kendra’s Law),20,21 which allow for involuntary institutionalization and examination of an individual with possible mental illness for up to 72 hours, did not adequately address or help mitigate the root causes of substance abuse and mental-health disorders. This increased preventable ED and/or hospital utilization for psychiatric needs.61

“Most high utilizers had ≥4 unique chief complaints, suggesting that these patients generally have several, rather than a few, reasons for seeking emergency care. No clearly defined pattern of complaints existed for high utilizers. High utilization in such patients is less likely to be caused by clearly defined disease processes and more by a complex mix of multiple chronic medical conditions and psychosocial factors, making it difficult to predict future utilization or identify specific patient needs based on their chief complaint.” Most high utilizer ED visits appeared to occur close together in clusters presenting complaints not categorically diff from low utilizers: more visits for same type of complaints. Most common complaint across utilizer groups: abdominal pain, chest pain, and shortness of breath. ” The chart review in this study highlighted the inherent difficulty in determining whether patterns of high utilization for these 3 complaints are related more to medical conditions or social/behavioral factors. Although nearly all of the patients in the chart review had mental illness and/or substance abuse, far fewer visits than expected were clearly linked to these conditions.17

The Camden Coalition conducts a cluster analysis to ID the various subpopulations. This involves sorting cases (usually by patient utilization history) into groups, or clusters, so that the degree of association is strong between people in the same cluster, and weak between members in diff ones. Some programs stratify the typologies by the different social needs faced by the patients such as homelessness, joblessness, and language preference—further indicating what interventions would be the most effective.49

Both the taxonomy developed by the Harvard T.H. Chan School of Public Health and the one developed by The Commonwealth Fund segment high-need individuals based on medical characteristics because this is a feasible starting point for most health care systems. Recognizing that a taxonomy focused on medical characteristics may neglect other factors that are key drivers of need, the taxonomy working group built on these efforts to offer a conceptual starter taxonomy that incorporates functional, social, and behavioral factors into a medically oriented taxonomy, not as independent segments but as factors that influence the care model or care team composition most likely to benefit particular patient segments (Figures S-2 and Table S-1). This starter taxonomy can provide guidance for health system leaders and payers on how to embed social risk factors, behavioral health factors, and functional limitations in a taxonomy for high-need patients. Patients would first be assigned to a clinical segment, with follow-on assessment of behavioral health issues and social services needs to determine the specific type of services that are required. Key behavioral health factors most likely to affect care delivery decisions include substance abuse, serious mental illness, cognitive decline, and chronic toxic stress and key social risk factors include low socioeconomic status, social isolation, community deprivation, and house insecurity.12

In the early stages of the CMHCB demonstration, CMP leadership learned that many high-cost, complex patients have mental health issues that were not effectively addressed by the current model of health care delivery or its pilot program. As a result, the program allocated greater resources to support mental health, hiring a social worker to assess the mental health needs of CMP participants and support them in accessing psychiatric care as needed or provide treatment if appropriate.86

CMO 1.5 Considering patients’ self-assessments of “subjective” characteristics about themselves [C] adds to intervention designers’ confidence in identifying HNHC patients [M] who will benefit from intervention services [O1] and who are at risk of having future high healthcare use and cost [O2].

Using the Medicare Health Risk Assessment, we explored two data-driven methods to segment a heterogeneous population of older adults with potentially complex care needs into clinically meaningful subgroups using self-reported information.

Input variables for the segmentation analyses were patient-reported variables drawn from the Medicare HRA, a component of the Medicare Annual Wellness Visit designed to identify patient-reported modifiable risk factors and health needs [8]. Required elements include self-assessment of health status, psychosocial risks, depression, behavioral risks, and Activities of Daily Living and Instrumental Activities of Daily Living. Care delivery systems can add additional questions.

The Medicare HRA is designed to help clinicians address patient-reported risks for preventable adverse outcomes. Although the HRA is most commonly applied at the point of care, if data are systematically collected, representative, and stored in extractable formats, they can be used to inform program development, population health, and outcomes research. Although content collected through patient-reported outcomes may duplicate content obtainable through more traditional clinical data such as ICD codes, ICD codes alone are unlikely to capture subjective responses to questions about pain, loneliness, and independent activities of daily living (for example). In this project, HRA data revealed meaningful subgroups that might not have been obvious from other electronic clinical data and could inform specific clinical interventions. Important differentiators included function, falls, perceived health status, emotional well-being, pain, and presence or absence of an advance directive. Two large subgroups comprised relatively healthy individuals who could benefit from watchful waiting and routine preventive care plus (for one group) life care planning. Much smaller subgroups could be targeted for more intensive and tailored care management. The size of these subgroups can inform resource allocation within delivery systems.89

As we enrolled patients into the group we found that, despite a broad range of medical and behavioral health problems, the common feature they shared and what ultimately served to bring them together as a group was their status of being “on the fringe,” as they described themselves. Nearly every patient had experienced a number of barriers and frustrations in accessing medical care that the DIGMA team seems to have successfully addressed.98

Traditional electronic data such as diagnostic codes and laboratory values may not capture essential information on factors that drive care needs, including function, personal preferences, and social resources, that can only be reported by individuals themselves. Identifying and characterizing complex needs subpopulations requires patient-reported information to help match care delivery to personal needs. Although newer data from electronic health records (EHRs) such as symptom assessments and ICD-10 codes that capture functional status can improve our ability to identify complex needs subpopulations, subjective information can add a level of specificity unlikely to be captured with objective coding.89

Moreover, recognition that computer-based designations of being at risk for costly care (also decorously called predictive analytics) vary considerably, furnish no specific guidance, and are inaccurate is increasing. Most patients in the small, at-risk subgroup will not use such care, whereas care becomes relatively rationed for most patients not designated as such—including those who may require it.… Nevertheless, health care executives embrace the paradigm of high-risk intensive management despite its flaws.…Meanwhile, many of its shortcomings can be remedied by a few standardized, patient-reported measures that forecast a patient’s risk for costly care in a similar manner to predictive analytics, specifically the risk for direct services. For example, patients may simply indicate that they are only somewhat or not very confident that they can manage and control most of their health problems; have had moderate or severe pain during the past 4 weeks; have been bothered extremely or quite a bit during the past 4 weeks by emotional problems, such as anxiety, irritability, depression, or sadness; believe that the medications they are receiving may be causing illness; and have been prescribed more than 5 medications. Although some payers and providers may disagree with the specific metrics, none should assert that a patient’s standardized self-report is an improper tool for guiding care. They may consider this method too old-fashioned or novel for implementation, but its modesty and low cost are remarkable.87

Dr. Wasson notes the limitations of relying on risk scores derived from electronic data. We agree that patient-reported indices have many advantages. Electronic indices, such as the Veterans Health Administration’s Care Assessment Needs Score (1), also have potential advantages as a screening method in health care systems in which such data can be calculated on a population level. Some high-risk patients identified by the Care Assessment Needs Score did not need or were unlikely to benefit from intensive management, and intensive management teams in our study spent substantial time triaging the heterogeneous populations by reviewing health records, contacting primary care providers, and having telephone or in-person visits. Our results suggest that selecting patients for intervention would ideally combine the use of algorithm-based risk scores with measures focused on such issues as those raised by Dr. Wasson, including whether patients believe that they are activated in managing their health or have difficulty managing their prescriptions.87

Data were collected through computerized administrative databases and HRA questionnaires to measure outcomes for utilization, health risk scores, and self-efficacy.90 Social determinants of health. All stakeholders emphasized the importance of inadequate health literacy, unstable housing conditions, and lack of adequate social support in driving preventable high health care utilization. Low health literacy made it difficult for many HNHC patients to manage complex medical conditions on their own, adversely impacting their ability to follow through with day-to-day self-care regimens. They also felt that for some HNHC patients with unstable housing conditions, being in the ED or an inpatient care setting was desirable, as it was the only avenue, as one HNHC patient put it, to ‘get a meal…have a television… stay overnight’. Finally, health system leaders as well as most physicians felt that the interplay between lack of social support and poor disease control was often a reason for presenting to the ED.

Physician: ‘Health literacy and overall education level is probably the biggest impact on the ability to self-manage these conditions’.

Unstable housing: Physician: ‘Eventually, [many] of them become homeless or [have] poor living conditions and those patients…arrive because of weather conditions or for other reasons to the ED to seek shelter and respite’.

Limited social support Health system leader: ‘[Workable solutions] probably have to do with…social support and reduction of isolation and helping them negotiate the complexities’.

Insurance challenges Patient: ‘I have Medicaid and some of the doctors don’t take [it], and so you say to yourself, well, even if I get an appointment, are they going to take me? So just go to the emergency room and let them handle it from there’.

Financial burden Patient: ‘A lot of times [we’re] on a fixed income and you need to see a specialist…You may have a co-pay with your specialist. It could add up if you go excessively. It’s easier to go to the [ED] and get what you need’.61

Patient activation refers to an individual’s knowledge, skills, and confidence related to self-management. The construct is commonly measured using the Patient Activation Measure, which is a thirteen-item interval-level scale with strong psychometric properties that generates a score between 0 and 100. A Patient Activation Measure level may be assigned based on the score, from level 1 (least activated) to level 4 (most activated). Studies show that Patient Activation Measure results are predictive of most health behaviors, clinical indicators, and hospital and emergency department (ED) use. Research also shows that less activated patients with chronic illness are more likely to experience care coordination problems, compared to more activated patients. Numerous studies also indicate that compared to more activated patients, less activated ones with chronic disease are less likely to effectively manage their conditions (for example, they are less likely to adhere to medication regimens), have healthy diets and get regular exercise, regularly monitor symptoms and clinical measurements, ask questions in the medical encounter, and report satisfactory care experiences. These findings suggest that less activated patients may benefit more from care coordination and care management services than patients who have equal disease burden but are more proactive about managing their health.104

In this study we used the four Patient Activation Measure levels. Level 1 indicates that a person does not yet understand the important role that patients play in determining their health, and level 4 indicates that a person is proactive about his or her health and engages in many positive health-related behaviors. Compared with lower-risk patients, high-risk patients were twice as likely to be sixty years or older and were somewhat more likely to be lower income (Exhibit 1). High-risk patients were also almost twice as likely to be depressed and more than twice as likely to be at the lowest Patient Activation Measure level. Furthermore, in 2011 high-risk patients were three times more likely to have had an ED visit and fourteen times more likely to have had a hospitalization. Fairview Health Services, a Pioneer ACO, uses the Patient Activation Measure to allocate its resources more efficiently to support patients. For example, Fairview’s care coordinators and health coaches, who manage high-risk patients, use the Patient Activation Measure level to decide how intensely to follow and manage specific patients. Nurses supporting patients during care transitions use a similar approach, in which the Patient Activation Measure score determines the frequency and focus of their posthospital support efforts.104

As we demonstrated in a previous article, although they are very sick, these patients remain surprisingly functional despite their illness(s) (Roberts et al., 2012). Additional important general observations include the following: these individuals are very resilient, highly resourceful, and are extraordinarily patient with the healthcare system. At enrollment, many are overwhelmed; disengaged; nontrusting; and lacking in a feeling of self worth or deserving of services (making it critical to have psychology expertise integrated into the team structure). Furthermore, this patient group wants to be perceived as agreeable (e.g., may know they will be unable to keep their next appointment for some reason but would not offer that unless asked).91

“We found in our case series that trust was a key theme in the relationship between patients and their clinicians or the health system. Lack of trust in individual doctors or institutions, as well as unrealistically high expectations of the same, appeared to be an important driver of higher costs Patient trust seemed to be informed by both patient factors (prior experience, socioeconomic status, activation) and clinician factors (quality of care, communication skills). Among the five patients, trust appeared to mediate the interaction between patient activation and cost: higher activation was associated with lower costs when patients or families had trust in their clinicians or when needed care was low acuity or standard; however, higher activation (in particular, the confidence and ability to advocate for care) was associated with higher costs when trust seemed lacking, particularly when the stakes were high (in critical illness) and the course uncertain.”.… Observations highlight challenges with trust in the setting of increasing medical complexity, specialization, and team-based care. Important to teach docs communication skills that earn trust, particularly around goals of care, and to better match patients to appropriate docs and care managers to ensure trusting relationships.”29

CMO 1.6 Considering patients’ self-assessments of health system-related risk factors and experience [C] adds to intervention designers’ confidence in identifying HNHC patients [M] who will benefit from intervention services [O1] and who are at risk of having future high healthcare use and cost [O2].

At enrollment, the concept of self-management is not familiar to most of them. Systems, like the Housing Authority, Medicaid, and health systems, often add to their burden. Examples include applications for benefits are frequently difficult to figure out and time consuming to file, applicants often feel disrespected or treated as if they were helpless, and agency staff are often not adequately sensitive to client issues regarding low/no literacy. In addition, for non-English speaking, translation services can be inadequate, cultural competency is a problem, and mailed annual reapplication notices (such as for Medicaid) are difficult to recognize as something official and may be disregarded.91 Example from CSHP illustrating the program’s theory of action “Patient A in Kansas City has multiple chronic conditions and poly-substance abuse, a history of homelessness, frequent ED visits, and no PCP [primary care provider]. At the initial contact with the care team, the patient stated that he would “never want to conform to the rules.” The care team’s strategy is to first establish firm trust. They accomplished this by identifying opportunities to provide basic help, such as involving family members in explaining the impact on diet of modifying cooking practices, supplying a scale and log to support the modification, organizing and explaining the purpose of medications, arranging for transportation and enabling the patient to do so, scheduling and accompanying patients to medical and social service appointments.85

Long wait times Patient: ‘When I go to the emergency room, they [say], “When you get out of here go see your GI doctor”. But, that’s not the way it works..Last time I called to get in the next day, they told me he had 17 patients, and couldn’t see me. In three months, you don’t know what could happen. So, the next thing is [back to] the ED’.61

Mismatch arises from patients’ social circumstances limiting access to services, behavioral issues interfering with care engagement, and lack of health system flexibility to address these barriers. A staff member reflected on the inability for a patient to receive services due to homelessness: “He was homeless when we made the referral and doesn’t…fit into [the] standard hospice system.… Health care systems are designed for these neat packages of people that are housed, have family support, have access to other resources, are not actively using substances.…The services aren’t really designed for complex folks, so that can be really frustrating and exhausting.” (SUMMIT LCSW)61

A few [stakeholders] even suggested that sometimes it felt easier to take an ambulance to the ED and access different services at the same place and time rather than arrange transport for multiple visits including PCP, specialists, bloodwork, etc.61

Economic determinants of health care. Patients identified insurance-related factors and financial burden of upfront costs (such as co-payments) as reasons for frequent ED visits and hospitalizations. For example, several patients on Medicaid reported knowing that many physicians in their community did not accept their insurance. To avoid losing time by contacting multiple primary care offices, they would go to the ED directly. Also, for underinsured and uninsured patients, the ED was the only health care setting where they could receive health care without having to deal with implications of their insurance status or co-pays right away. Many patients also reported the negative impact of financial burden on their medication adherence as a driver of frequently presenting to the ED. They felt that some of these visits can be prevented if medications and essential medical devices were not so expensive.61

Survey of frequent ED patients while in ED

Barriers to care: % agree, could have multiple barriers

It is easy for me to make time to get to necessary med appts: 78%

I always remember to schedule my annual check-ups, tests, and/or screenings: 65%

I feel like I receive better quality health care in the ED than I do in my usual place of care (PCP, clinic): 48%

whether certain services would be helpful to the patient, in the event the ED or health system decided to offer the service:

After-hours options for minor health issues besides the ED: 63%

A nurse to work with you one-on-one to help manage health care needs: 53%

Transportation to get to medical appointments on-time: 46%

While 42% did not think that a PCP would be helpful, many said that they had one.27

CMO 1.7 An enrollment process that includes an extensive period of outreach and trust building [C] adds to an intervention provider’s confidence [M] in determining a participant’s willingness and ability to participate in the intervention [O].

The purpose of community navigators in the Familiar Faces program is to bridge the gaps between their clients and the healthcare and social systems that are often fragmented and difficult to navigate. Furthermore, integration of community navigators into the healthcare system, specifically the information flow offered by EHRs, ensures that community navigators are able to engage with patients during acute episodes of care, when their needs are greatest. In addition to the expertise that community navigators provide to clients in navigating healthcare and social systems, they may build trust between clients and the healthcare system, as they live in the same communities as their Familiar Faces clients. Mistrust of the healthcare system is often high in minority and low socioeconomic populations and may result in delayed medical treatment and use of fewer preventive services.… Because the community navigator was simultaneously a member of the community and the healthcare system, it is possible that they were able to reach community members particularly mistrustful of the healthcare system and start to build a foundation of trust.95

The outreach to ESRD beneficiaries, however, was entirely new in Phase II since ESRD was not part of the clinical focus in Phase I. KTBH program leadership found that when a nurse had an in-person contact with an ESRD beneficiary, the beneficiary was more than twice as likely to enroll in the program. As a result, KTBH program leadership decided to send a nurse to every dialysis facility with more than two eligible beneficiaries. Prior to the visit, the care manager sent a packet to the facility’s administrator, placed a call to the administrator, and tried to make an appointment to conduct an informational breakfast or lunch session with the entire staff to introduce the KTBH program and assuage any concerns about the program. The goal of the informational sessions was to explain to staff that the care managers hoped to accomplish things with the beneficiary that would enable the intervention participants to better manage their condition. They tried to convey to staff that the care managers were not there to make their lives more difficult or to take the place of the existing staff that provided services to beneficiaries. KTBH staff reported receiving the biggest pushback from facility social workers. KTBH staff believed that there was a direct correlation between having the support of social workers and beneficiary participation in that when they received the support of the social workers, prospective participants were more likely to join the KTBH program.82

That’s been the difficult piece, finding people who are appropriate and they want our help at the same time. That’s been the trickiest piece. CCM Nurse94

CCM providers in our study actively looked for positive and negative indicators that patients were willing to engage in care. Providers often found these explicit signs to be inadequate for detecting patients’ desire or readiness to engage in care and therefore looked for more subtle signs and tried to make intuitive assessments.94

The enrollment criteria for the CCM programs included a willingness to engage in care.94

CCM providers looked for indicators that patients were willing to engage during initial patient encounters and enrollment, and looked for signs of successful engagement in ongoing assessments of patients’ communication and actions. When CCM providers first met patients, positive indicators that they were willing to engage included verbally agreeing to take part in the program, returning phone calls to the CCM team or answering the phone when CCM team members call, being receptive to a home visit, and showing up to an initial appointment. CCM providers continued to assess engagement over time by considering how frequently patients missed appointments, how well they adhered to medication and treatment regimes, how much progress they made towards behavioral changes such as reducing substance use or increasing exercise, and how candidly and regularly they communicated with the CCM team. A social worker said that she considers patients likely to engage over time when “we reach out to them by phone and we make an appointment and they show up.”94

We learned that for B2C to reduce the use of acute care, outreach to and enrollment of high utilizers had to happen in real time in the ED.88

Judgment about whether the patient is amenable to management is based on an interview with the patient by the care team and a review of medical records. The assessment of whether the patient is entered into Care One is based on a judgment that the patient has a chronic medical condition, is at high risk for future hospitalization, and is willing to attend outpatient visits and comply with therapy. Other specific exclusions from the Care One program include a single high-cost medical event (e.g., a trauma), residence outside of the hospital’s catchment area, and chronic alcohol or drug abuse.92

Most deep-dive practices indicated that risk-stratification improved the organization and delivery of care. Clinicians and staff continued to report that risk-stratification increased their awareness of high-risk patients’ needs and helped them better allocate staffing resources to different patient populations. For example, in a few practices, patients with a single chronic condition (such as patients with diabetes who needed basic monitoring and health education) received care management from a medical assistant. This enabled the care manager to focus on higher-risk patients (such as patients with poorly controlled diabetes and additional chronic conditions). Risk-stratification continued to help practices identify and prioritize high-risk patients and schedule longer appointments for them as needed. In contrast, respondents in one small deep-dive practice questioned the utility of risk-stratification; they perceived that clinicians knew their patients well enough to determine whether they were high-risk and they believed that the time they spent risk-stratifying patients would be better spent delivering direct patient care.101

Text box III.1. Example from CSHP illustrating the program’s theory of action “Patient A in Kansas City has multiple chronic conditions and poly-substance abuse, a history of homelessness, frequent ED visits, and no PCP [primary care provider]. At the initial contact with the care team, the patient stated that he would “never want to conform to the rules.” The care team’s strategy is to first establish firm trust. They accomplished this by identifying opportunities to provide basic help, such as involving family members in explaining the impact on diet of modifying cooking practices, supplying a scale and log to support the modification, organizing and explaining the purpose of medications, arranging for transportation and enabling the patient to do so, scheduling and accompanying patients to medical and social service appointments.85

C = context; M= mechanism; O = outcome

Table B-12Full list of Context-Mechanism-Outcome (CMO) configurations with supporting data for Program Theory 2: Engaging HNHC patients in interventions to improve their management of their chronic conditions, supporting CMO relationships

Program Theory IssueCMOsRelevant Data Extracts From Included Literature
Patients’ challenges to self-care prior to and during the interventionCMO 2.1. Past experiences with the healthcare system including encountering barriers accessing medical care and disrespect from providers [C] cause patients to distrust the system and providers [M] which inhibit patients from accepting and seeking appropriate help and medical care [O].

More than half of participants (n = 12) related stories of encounters that had upset them; several explicitly mentioned withdrawing from outpatient providers by choosing not to attend appointments with those providers as a result. Over a third of participants switched providers because of dissatisfaction with those relationships. Others who had not switched chose not to follow a given provider’s instructions as a result of these negative interactions. Most of the stories of negative health care encounters focused on feeling disrespect from providers, while others specifically described feeling discriminated against by providers because of race or sex.105

Several participants in this study expressed that they felt that their care sometimes was compromised by perceived disrespect from health care providers, citing race-, sex- or SES [socioeconomic status]-based discrimination.105

When answering questions about trusting their health care providers, almost half (n = 9) of participants stated that they distrusted a particular (usually hospital-based) provider. Respondents generally expressed trust in their primary care providers.105

Nearly every patient had experienced a number of barriers and frustrations in accessing medical care that the DIGMA team seems to have successfully addressed.98

“Patient A in Kansas City has multiple chronic conditions and poly-substance abuse, a history of homelessness, frequent ED [emergency department] visits, and no PCP [primary care provider]. At the initial contact with the care team, the patient stated that he would “never want to conform to the rules.”…His sister reflects, “He used to use the ER [emergency room] for everything.…”85

Challenges associated with accessing health care delivery systems Transportation barriers. Some patients reported that primary care offices were inconveniently located and difficult to access due to transportation barriers. A few even suggested that sometimes it felt easier to take an ambulance to the ED and access different services at the same place and time rather than arrange transportation for multiple visits including primary care, specialists, bloodwork, etc. For patients who could utilize private or public transportation to get to a primary care clinic, the distance often made the trip extremely time-consuming as well as costly. Preventability of ED use appeared contingent upon logistic ease of access to services.

Long wait times. All stakeholders identified scheduling challenges at primary care clinics as an important driver. Many patients reported that they were unable to schedule first-time or follow-up appointments quickly (same-day, next day or even in upcoming weeks) and instead had to wait several months. Furthermore, if a disease exacerbation occurred after regular clinic hours or overnight, patients felt that they had no other options but to seek care in the ED.61

CMO 2.2. Previous and current personal life circumstances and characteristics (e.g., mental illnesses, substance abuse, emotional or physical traumas, extreme poverty, and low literacy) [C] results in feelings of stigma in patients [M] that inhibit them from seeking help and medical care [O].

A predominance of the participants (n= 12) told stories of childhood instability…Significant subthemes included early life traumas, such as death of a parent or other loved one, and abusive relationships with primary caregivers throughout childhood. Some described state agencies as their primary caregivers. Transiency was noted, often in the context of escape from abusive relationships (n = 5), and often resulted in living on the streets or gang and drug involvement, even as children. Only 2 individuals specifically described how events in childhood affected their health during adulthood. Nevertheless, many participants related stories of how this instability may have manifested in health issues, especially with regard to mental health. For example, one woman described: ‘‘I had sexual and physical abuse from my parents since I was a baby, since I was 3 or 4. Mother has been very abusive over the years. Every time we went somewhere she was hitting me, punching me, scratched me, and I’d cover it up.’’ This individual described ongoing difficulty obtaining effective treatment for the post-traumatic stress disorder that repeatedly sent her to the ED. She reported being frequently ‘‘suicidal’’ and ‘‘in crisis’’ as a specific result of childhood abuse, for which she was ‘‘in and out of hospitals too much.’’ She reported a shared goal with her therapist of accessing consistent community-based mental health care rather than relying solely on the ED. Nearly half of the respondents (n = 8) named their mother or a mother figure as their most important primary caregiver. The other half referred to a variety of other caregivers, such as grandparents, other relatives, or the state, or they declined to answer; these situations were described by some participants as traumatic, and by others as a normal course of events. Of participants who described instability in childhood, half noted familial estrangement in adulthood (n = 6). When asked if there was someone they could depend on now for help, most participants spoke of having only 1 or 2 individuals that they could rely on, if any, suggesting a lack of social support in adulthood for nearly all of these respondents.105

Patients may have a variety of barriers that prevent them from accessing traditional primary care venues, particularly those settings that do not allow patients to walk in at their convenience or patients who may need intensive services during a personal crisis. Patients with difficult life circumstances also may be more likely to not show for an appointment.98

Patients in this quality improvement program tend to be younger than those targeted by previously described care transition models and many have unstable health insurance, a history of substance use, and significant mental illness. Nearly all are from socially disadvantaged communities plagued by poor health status, and low literacy is common. Many are struggling with difficult life circumstances such as an alarming number have been emotionally or physically traumatized;…and many have inadequate, or no, family and social support systems. At enrollment, the concept of self-management is not familiar to most of them.91

There are two key elements to the success of these new efforts to target and improve care for high-cost Medicaid cases. First, it is essential to be able to identify in advance patients who are likely to have high costs in the future. Many high-cost occurrences (such as injury, acute illness, or cancer) might be episodic, and high spending in one year might not mean high spending in subsequent years. Second, and equally critical, is the ability to actually affect the care pathways and outcomes of these patients. Because of the circumstances that define their Medicaid eligibility (extremely low income and medical disability) and other factors that are likely to be associated with their social and personal environment (such as homelessness, substance use, or low educational achievement), these patients will undoubtedly present major challenges.62

All stakeholders identified poorly managed serious mental illness among HNHC patients as a significant driver of preventable high health care utilization. Patients often had inadequate access to mental-health and substance-abuse resources. This was because outpatient programmes did not exist, were inconveniently located or were not financially feasible to attend. This left patients without any options other than the ED for care. Additionally, several patients acknowledged that feeling depressed negatively impacted their care routines and contributed to missing provider appointments which, over time, compounded the severity of their diseases. Importantly, patients also pointed out that the stigma surrounding mental illness was detrimental to their desire to seek out treatment even if it were available. Some patients also felt that policies such as the Florida Mental Health Act (known as the Baker Act) and its equivalent in New York State (known as Kendra’s Law),20,21 which allow for involuntary institutionalization and examination of an individual with possible mental illness for up to 72 hours, did not adequately address or help mitigate the root causes of substance abuse and mental-health disorders. This increased preventable ED and/or hospital utilization for psychiatric needs.61

Low health literacy made it difficult for many HNHC patients to manage complex medical conditions on their own, adversely impacting their ability to follow through with day-to-day self-care regimens.61

Economic determinants of health care. Patients identified insurance-related factors and financial burden of upfront costs (such as co-payments) as reasons for frequent ED visits and hospitalizations. For example, several patients on Medicaid reported knowing that many physicians in their community did not accept their insurance. To avoid losing time by contacting multiple primary care offices, they would go to the ED directly. Also, for underinsured and uninsured patients, the ED was the only health care setting where they could receive health care without having to deal with implications of their insurance status or co-pays right away. Many patients also reported the negative impact of financial burden on their medication adherence as a driver of frequently presenting to the ED. They felt that some of these visits can be prevented if medications and essential medical devices were not so expensive.61

CMO 2.3. System-level barriers including inadequate systemic support (e.g., Medicaid, translation services, housing) and lack of cultural competency [C] engenders feelings of distrust and marginalization among patients [M] that inhibit their ability to access appropriate healthcare services [O1] and to participate in interventions [O2].

…some have no income while others have income that it is insufficient to meet basic survival needs making it challenging to pay even minimal co-pays for prescriptions; many live in unstable housing or in dangerous neighborhoods.…Systems, like the Housing Authority, Medicaid, and health systems, often add to their burden. Examples include applications for benefits are frequently difficult to figure out and time consuming to file, applicants often feel disrespected or treated as if they were helpless, and agency staff are often not adequately sensitive to client issues regarding low/no literacy. In addition, for non-English speaking, translation services can be inadequate, cultural competency is a problem, and mailed annual reapplication notices (such as for Medicaid) are difficult to recognize as something official and may be disregarded.91

Many participants faced a variety of barriers to appropriate care, including lack of stable income, health insurance, legal residency, English language proficiency, knowledge of the health system and chronic disease management, stable housing, social support, and transportation. Many also had issues with cultural barriers, mental illness and substance abuse (despite informal program eligibility criteria that excluded some patients with these conditions), and traumatic experiences that made stabilizing their chronic conditions more difficult.85

Many patients are unable to afford even a minimal copayment that may be expected at time of a nonemergent outpatient visit and may choose to access the ED where a copayment may not be required.98

We found that patients with low health literacy (measured by the REALM-SF) reduced ED utilization to a greater degree than patients with higher health literacy. We hypothesize that patients with lower health literacy may have encountered more barriers to accessing primary care or had greater social needs than those with higher literacy and thus, differentially benefitted from individualized assistance from a patient navigator. These preliminary results suggest that care coordination programs that aim to reduce avoidable ED use and hospital admissions may have a greater impact among patients with lower health literacy.110

In the current study, KCCP Care Managers identified multiple barriers to active participation in the intervention including basic needs for food, shelter, and transportation that took precedence over program participation; depression; not having a phone or being unable to manage a phone due to mental illness or addiction; language or other cultural barriers; and mistrust of the system (Cristofalo et al. unpublished data).112

Social determinants of health. All stakeholders emphasized the importance of inadequate health literacy, unstable housing conditions, and lack of adequate social support in driving preventable high health care utilization.… They also felt that for some HNHC patients with unstable housing conditions, being in the ED or an inpatient care setting was desirable, as it was the only avenue, as one HNHC patient put it, to ‘get a meal…have a television… stay overnight’. Finally, health system leaders as well as most physicians felt that the interplay between lack of social support and poor disease control was often a reason for presenting to the ED.61

Relationship building with care providersCMO 2.4. Interventions and care team members initially address patients’ basic needs and explain things in lay terms [C] to establish trust with the patient [M] resulting in building a relationship with their patients [O].

Patients generally had positive impressions of their care managers. During semi-structured interviews with a sample of high-risk patients and caregivers from deep-dive practices, patients who reported having regular contact with their care manager or who were open to working with their care manager felt that the care manager was an asset to their team. Patients particularly valued care managers who listened to them and explained things in lay terms, helped to manage medications and chronic conditions, followed up after a hospitalization, and helped to navigate the health care delivery system and community resources.101

“…The patients that have been on SUMMIT [Streamlined Unified Meaningfully Managed Interdisciplinary Team] for a while who have a really solid relationship with us, that makes a huge difference. They are able to call. They are telling us what their needs are. They can make it to appointments and…coordinate all of those needs a little bit better when they know that we’re going to be reliable and [here] is where they can come for help.” (SUMMIT Physician).58

They [the program staff] expected that its program would have the greatest impact by preventing acute health care events among beneficiaries who were initially not having significant health issues; however, case managers found that they spent a lot of time dealing with urgent issues for patients who “spiraled out of control.” Although initially some patients were skeptical about the MGH [Massachusetts General Physicians Organization] CMP [Care Management Program], overall, patients quickly formed relationships with case managers, including several who requested daily contact with their case managers to help them with their numerous issues.

The care team’s strategy is to first establish firm trust. They accomplished this by identifying opportunities to provide basic help, such as involving family members in explaining the impact on diet of modifying cooking practices, supplying a scale and log to support the modification, organizing and explaining the purpose of medications, arranging for transportation and enabling the patient to do so, scheduling and accompanying patients to medical and social service appointments. Within weeks, the patient has started scheduling transportation and keeping his appointments independent of the care team, and now states that he cares about his health. His sister reflects, “He used to use the ER [emergency room] for everything. Now he asks when his appointment is.”85

Convenience And Access Our model emphasizes convenience and access, starting with location. Our centers—in the range of 6,500–10,000 square feet—are located in urban areas with a high density of low-to-moderate-income seniors. For our patients’ convenience, we offer a broad set of additional services on site, including dental care, digital x-ray, ultrasound, and acupuncture, as well as five to fifteen high-volume specialists. Our average health maintenance organization (HMO) patient received 86 percent of his or her ambulatory encounters at our centers in 2011, although the most expensive aspects of care occurred outside of our centers— for instance, hospitalizations, surgeries, and imaging. Patients find the one-stop-shop approach to care highly appealing.114

CMO 2.5. When care managers support patients with medical and non-medical problems [C], patients are reassured [M1] and gain confidence [M2] in their ability to manage their own care [O].

Care management is a vital piece of the puzzle, pulling together community resources without which recovery would be impossible.…Successful case management also includes assisting with teaching some of these patients basic life skills, for example, not to find housing for them, but rather direct them where to go to get housing assistance. These small, positive steps are then shared with the group, which further reinforces a growing sense of confidence.98

Additionally, efforts to tailor-make health education programs to improve health literacy and numeracy may be warranted for patients to effectively self-manage some of their care needs.61

CMO 2.6. Patients are more motivated [M] to improve their health behaviors [O] when they feel cared for by their providers and other support groups [C].

…participants reported that ‘‘caring’’ providers were particularly important in the trajectories of their illnesses and lives, emphasizing the compassion of the Care Management Team. Providers from the intervention were described as dependable, sensitive, and thoughtful, suggesting that these traits in providers may resonate for individuals whose childhoods lacked caregivers with these qualities.105

Conversely, participants emphasized the importance of caring, trusting, and longitudinal relationships with providers, both on the Care Management Team and with primary care providers. Comorbid mental illness, especially depression, makes managing chronic illnesses such as diabetes more challenging.34–37 Consistent, positive relationships with primary care providers have been shown to decrease rates of hospitalization and ED use for complex patients who struggle with a combination of multiple chronic illnesses, mental illness, and psychosocial challenges.105

Frequent, longer visits built relationships with the care team and other patients. The emotional support provided by the group seemed to be a key factor in assisting patients to find solutions to their health and social problems.98

Half of participants indicated the importance of ‘‘feeling cared for’’ by providers (n = 10). This theme recurred throughout the interviews, especially during descriptions of the Care Management Team. When asked about the best part of the intervention, rather than describing specific services, most participants described the importance of the emotionally supportive interactions they experienced. These participants reported that the experience of feeling cared for was a motivation to improve their own health behaviors (n = 10) (Table 2). Diabetes, depression, and hypertension were the most commonly reported conditions. Despite the natural history of these complex chronic diseases and their tendency to reflect a pattern of deterioration over time, 7 participants reported improvements in their own perceived health status after the intervention. Five of those participants specifically attributed this improvement to the intervention. ‘‘They make you feel like you’re not alone, and they understand you and the things you’re going through. And they actually help explain why you’re going through these things…you don’t feel like just a patient.’’ - 24-year-old African American woman with depression and Type 1 diabetes.105

CMO 2.7. When intervention care providers build trusting relationships with patients [C], patients have confidence in their providers’ desire to help [M] resulting in patients seeking advice from their intervention care provider before going to the ED/hospital [O]

During the early months of CLM’s [Care Level Management’s] program implementation, nurse care managers focused on building relationships with the patients during telephone contact between PVP visits, so that patients would be comfortable calling the nurses if health problems arose. Patients at highest risk were to receive calls on a weekly basis, whereas those at moderate and low risk were to receive calls on a monthly or bimonthly basis.…Over the course of the first year of operations, CLM reported …that they reorganized their patient care teams to include more nursing support. CLM believed that this arrangement would allow patients to bond with the nurse care manager over time, whereas CLM had observed that the clinical specialists were not able to forge a sufficient bond as evidenced by the fact that some of their participants were going to the hospital rather than calling the clinical specialists when problems arose.84

In addition to connecting clients to health and social resources in their community, the community navigators focus on building trust between the client and navigator and subsequently with other healthcare entities and social systems in the community.95

The care manager is an experienced, calm, trusted professional patients can call when they are frightened or in crisis between groups visits, which is often the difference between going to the ED to seek immediate care or waiting a day or 2 until the next group visit.98

These participants articulated an appreciation for continuity in relationships with providers, including members of the Care Management Team. A majority of respondents (n = 14) described their preference for office-based primary care with their usual providers, reserving the ED for emergent medical necessity or after-hours needs.105

Individualized care for HNHC patientsCMO 2.8. Designing flexible interventions that could be tailored and individualized to specific HNHC patient’s needs and circumstances [C] empowers providers [M] to be responsive to each patient’s needs and circumstances.

Care transitions are normally linear and finite (e.g., from Provider A to Provider B), but in our care coordination programs, the number and nature of care transitions are circular, overlapping, and continual. They involve cross-sectoral care givers including social services, government workers, and church and community members—in addition to medical, social work, and behavioral health providers in one or more health systems—and they take place at multiple locations. Because the interventions need to be tailored to each patient individually, based on their medical and life situations, they are not predictable at the outset, and “model fidelity,” as required by most care transition models, is not feasible.91

Specific interventions were tailored to each patient in collaboration with the patients and their family, reflecting the patient’s unique needs.119

Patient intake at IOC included an in-depth patient assessment to determine non-medical barriers to improved health. Care plans and activity to address needs were individualized.83, 85

The ability to tailor care to patients’ individual needs was another ingredient staff members felt they provided to complex patients. A SUMMIT care coordinator described a strategy to assist patients with attending specialty appointments: “I’ll have appointments with patients just with myself if patients need help with scheduling outside the clinic and scheduling transportation.… If a patient chronically no-shows to a (specialty) appointment…I’ll make an appointment for them to come [see] me and we’ll schedule together and…give them an appointment planner or write up all their appointments for them.”58

Each enrollee gets a tailored 60 day care plan and associated patient services they might need including assistance obtaining housing resources, insurance, disability benefits, refugee services, transportation, coordinating primary and specialty care; and filling prescriptions.88

The housing patterns we found, however, suggest the need for flexibility. Consistent with the experience of many Housing First programs, over two-thirds of the housed intervention participants required rehousing after their first placement did not succeed. The ability to offer a new housing placement is a key component of successful Housing First strategies when working with high complexity populations. With the widespread use of Coordinated Entry that will require that counties place individuals with similar risk profiles into PSH, our findings provide support for the need for flexibility, including the ability to rehouse individuals, in order to serve those at highest risk. Our results offer a measured sense of expected changes in their use of other services.109

Another feature of our program was the flexibility in the range and intensity of services we offered to patients. Some patients required infrequent contact to assist with scheduling and attending primary care appointments. Other patients benefited from more intensive contact, including multiple accompanied clinic visits or home visits.111

CMO 2.9. Having interventions address underlying mental health conditions concurrently or before managing other health conditions [C] helps patients’ ability to cope with their health conditions [M] and allows them to benefit from interventions addressing their chronic conditions [O].

Theme 3: Addressing Both Psychosocial and Clinical Needs Participants noted that it wasn’t possible to separate provision of psychosocial support from traditional medical care. This can run counter to what occurs in usual care. “I spent an hour with a patient last week and we didn’t talk about medical problems.… It was a therapeutic session. I’m not a trained therapist, but [that’s] what it was. We didn’t talk about diabetes. We didn’t talk about her foot ulcers.…A lot of times we end up doing the work of social workers, but when you do primary care, you have to do that. It’s not ‘oh hold on,… I’m not getting into that. I’m only here for the medical stuff.’ It all wraps up into one.” (SUMMIT Physician)58

But the extraordinarily high levels of substance abuse among high-risk patients and the history of mental illness even among the population without serious and persistent mental illness make clear that any intervention will have to take these factors into account.62

In the early stages of the CMHCB [Care Management for High Cost Beneficiaries] demonstration, CMP [Care Management Program] leadership learned that many high-cost, complex patients have mental health issues that were not effectively addressed by the current model of health care delivery or its pilot program. As a result, the program allocated greater resources to support mental health, hiring a social worker to assess the mental health needs of CMP participants and support them in accessing psychiatric care as needed or provide treatment if appropriate.86

Many participants faced a variety of barriers to appropriate care, including lack of stable income, health insurance, legal residency, English language proficiency, knowledge of the health system and chronic disease management, stable housing, social support, and transportation. Many also had issues with cultural barriers, mental illness and substance abuse (despite informal program eligibility criteria that excluded some patients with these conditions), and traumatic experiences that made stabilizing their chronic conditions more difficult.85

…the program is unique in having a behavioral health provider screen every enrollee for mental health disorders—and then address those conditions as appropriate.88

We found a significant reduction in use of psychiatric emergency services and a concomitant increase in scheduled mental health visits. Project Welcome Home included Intensive Case Management with a low client-staff ratio led by licensed staff with behavioral health training. Research has shown that experiencing homelessness is one factor that leads to ED visits among psychiatric patients, suggesting an unmet need for mental health care.5,18 Our findings suggest that these visits are amenable to prevention by providing housing with associated low-barriers mental health services.109

CMO 2.10. Connecting patients and supporting them in navigating services that cross medical sectors (e.g., geriatrics, substance disorder treatment) and non-medical sectors (e.g., employment, housing, transportation) [C] help patients gain the confidence [M] to learn how to navigate multiple systems for themselves [O].

Through the CMP, patients are assigned to a personal care manager who assists with access to social and medical resources, helps patients schedule PCP appointments, and helps bridge barriers between patients and the healthcare system. Enrolled patients are assigned to 1 of 3 outpatient primary care clinics. Components of the CMP include: goal creation/assistance in reaching goals, ranging from applying for benefits and receiving stable housing to losing weight and receiving specialty care appointments; assistance with care navigation (schedule appts, follow-up on referrals, and help refill medications); arranging for social services (make personal connections with staff at various agencies in the community and refer patients to appropriate services, including transportation resources, Legal Aid, homeless shelters, faith-based services, and substance abuse resources); care transitions (meet with patients daily while they are admitted and work with discharge planners to assist patients in receiving recommended follow-up care and understanding discharge instructions); and communication with providers (accompanying them to appointments, creating and prioritizing problems lists, coaching patients about questions to ask, and sitting with patients after their visit to explain follow-up instructions).107

After the visit, the patient navigator and patient created a task list based on the provider’s recommendations. For example, if the PCP ordered additional tests or specialist referrals, the navigators assisted in scheduling these additional appointments, phoned patients to remind them, identified and addressed any barriers such as transportation, and encouraged patients to follow PCP recommendations. When needed, navigators helped patients to access medical transportation assistance through the state Medicaid system. If the patient identified social needs such as precarious housing, food insecurity, or insurance questions, they were provided with information to connect with local resources.110

The team (1) conducted home visits, (2) scheduled and accompanied patients to initial primary and specialty care visits to ensure that such appointments are kept and that the patient understands any instructions given during the appointment, (3) coordinated follow-up care and medication management (medication reconciliations), (4) measured blood pressure and blood sugar levels when appropriate, (5) coached patients in disease-specific self care, (6) helped patients apply for social services (e.g. housing support, Social Security (including SSI), Supplemental Nutrition Assistance Program (SNAP), Temporary Aid for Needy Families (TANF), and General Assistance (GA)) and appropriate behavioral health programs. provides disease specific education, coaches the patient in self-care, and works to empower patients to manage their health issues. During subsequent home visits, the team evaluates the patient and team’s progress. The care team works to connect the patient with stable, continuing, and appropriate primary and specialty care. Coalition staff may help schedule further medical appointments as necessary, continue to help organize transportation, accompany patients to medical appointments, check-in after medical appointments to help the patient implement the instructions given by the provider, and continue to organize medications. Home visits in later stages increasingly focus on self-care management skills, health care navigation skills, enhancement of self-efficacy and independence, care plan adjustment and coaching.96

The most frequently used intensive management services were social work and mental health care, highlighting the importance intensive management teams placed on these services on the basis of their comprehensive assessments of patients’ needs. The intensity of services varied greatly among patients assigned to the intervention group; patients who used more services tended to be older and to have more comorbid conditions, higher rates of baseline primary care utilization, and lower rates of substance use disorders and serious mental illnesses. These findings suggest that other models of intensive management may be more appropriate for patients whose mental health and substance use conditions are severe and are likely to prevent effective engagement with the intensive management team.

By design, the intensive management programs seem to have facilitated referrals to home-based primary, palliative or hospice, geriatrics telephone, specialty mental health, and telehealth care. Because sites performed comprehensive assessment of patients’ social issues, treatment plans, and care goals, our results suggest that the intensive management programs could identify unmet needs and connect patients to important resources. Home visits seemed to play a key role in patient assessments, because patients with more intensive services had an average of 1.5 home visits.87

  • Some patients with experience in residential or other intensive management programs need support when trying to complete programs, and need housing/support once programs end
  • Patients desire support when trying to return to school, find employment, or find housing: “I wish someone would help me navigate the system. I don’t know what resources or programs are available to me.”106
Successful case management also includes assisting with teaching some of these patients basic life skills, for example, not to find housing for them, but rather direct them where to go to get housing assistance.98

CMO 2.11. Because patients’ burden of coexisting chronic diseases and social and behavioral issues are heterogeneous, allowing the length of the intervention to vary across participants [C] helps patients feel supported [M] by providing them with sufficient time to demonstrate intervention goals (e.g., self-management behaviors) [O].

Patients are continually enrolled at different times, resulting in different lengths in the post-enrollment time frame. Patient diagnoses that are driving admissions, and their burden of coexisting chronic diseases, are heterogeneous. The natural history of these common chronic diseases is such that the patients have ever evolving health conditions intermixed with periods of disease decompensation. Length of time in the intensive intervention period is variable and determined by demonstrated need and functionality: socially, medically, and behaviorally. Our intervention is not administered by number of days exposed but instead is administered until the patient demonstrates the behavior criteria we have defined (“graduates”), the patient expires, or transitions. Because the patients are graduated according to demonstration of objective self-management behaviors (Figure 2), the resultant postgraduation time frames are also variable.91

Participants’ issues often took longer to resolve than the intervention’s time line typically allowed.85

Barriers to HNHC patient change through interventionsCMO 2.12. Despite successful engagement with the intervention and relationships between members of the care team and HNHC patients [C], HNHC patients may continue to prefer seeking primary care at the hospital or ED [M]. Therefore, interventions may not be able to achieve goals such as reducing use of potentially preventable or modifiable healthcare services [O].

One challenge for the demonstration was that a sizable minority of beneficiaries and caregivers would prefer to visit the ED [emergency department]—instead of contacting the IAH [Independence at Home] practice—if they were unsure whether symptoms required emergency care (Table III.3). Beneficiaries provided a number of reasons for preferring to go to the ED, including that they or their caregivers thought it was the best place to receive care. Even though three-quarters of beneficiaries reported that the IAH practice visited about as often as the patient wanted them to visit (Appendix C, Table C.8), some beneficiaries’ preference for the ED in uncertain situations might contribute to the demonstration’s lack of an effect on outpatient ED visits.103

Engagement with the program was high (95% of patients had at least three encounters with program staff), and patients received an intensive intervention (averaging 7.6 home visits), but two program goals related to the timing of services — a home visit within 5 days after hospital discharge and a visit to a provider’s office within 7 days after discharge — were achieved less than 30% of the time. Challenges in reaching these goals included patients’ lack of stable housing or a telephone and their behavioral health complexities and providers’ few available appointments. The difficulties that this pioneering, data-driven organization had in achieving rapid assistance for patients may portend difficulties in achieving it at scale.96

Patients randomized to PIM were more likely than patients in PACT to strongly agree that they have a VA healthcare provider whom they trust…Survey findings suggest that the program may have influenced some patients’ experiences with patient-centered care and chronic illness care, and increased the number of patients who reported having a trusted provider, but did not influence satisfaction, perceived access, or most measures of care coordination.97

Though the SUMMIT intervention was developed as a way to address high ED and hospital utilization, staff members did not mention reduced utilization as a marker of success. “We are dealing with a pretty sick population.…These are patients that maybe do need to be in the hospital.… A hospitalization is not necessarily a bad outcome for a lot of these patients.” (SUMMIT Physician)58

CMO 2.13. Improvements in patients’ experiences with their care providers through participation in HNHC patient interventions [C] gradually rebuilds patients’ trust in the health care system [M] that may lead to long-term benefits in health behaviors and clinical outcomes [O]Furthermore, relationships are at the core of primary care, so this finding suggests that augmenting a medical home with an intensive management program may help fulfill the promise or primary care. In fact, analyses of satisfaction suggest that the program improved patients’ experiences with primary care, but not with other services. Improving primary care processes could potentially have positive long term consequences, including changes in health behaviors and clinical outcomes.97

C = context; M= mechanism; O = outcome

Table B-13Full list of Context-Mechanism-Outcome (CMO) configurations with supporting data for Program Theory 3: Care provider engagement in interventions for HNHC patients

Program Theory IssueCMOsRelevant Data Extracts From Included Literature
Gaining and maintaining support from physicians and other care providersCMO 3.1. Strong leadership support that facilitates systemic coordination of the intervention and its components smooth the entry of care managers into practices [C] provides credibility of their services to existing practice staff [M], so care managers are more easily incorporated into primary care teams [O].

Once MGH [Massachusetts General Physicians Organization] had generated lists of CMP [Care Management Program]-eligible beneficiaries receiving care from each physician, the CMP medical director met with each practice to introduce the program and discuss which patients were at highest risk for acute events and should receive priority for enrollment. The medical director also met with specialty practices such as the oncology, cardiology, emergency, and orthopedics departments to explain the resources available through the program, because case managers would likely interact with these providers as they facilitated patient access to these services.86

At the time of the program launch, strong integration support from MGH leadership afforded the case managers physical entry into the primary care practice settings whereby the case managers were co-located with the primary care physicians ultimately becoming a part of the beneficiaries’ primary health care teams.86

CMO 3.2. Program leaders’ use of tailored strategies and physician champions to explain intervention services [C] helps endorse the intervention [M] and results in physicians participating in the intervention [O].

A second round of focus groups was conducted with physician groups to specifically discuss how the CMP could add value to their practices.

In addition to providing input about the design of the CMP, the capstone groups provided an opportunity to obtain physician buy-in to the PBCM [practice-based care management] program. Despite the fact that some physician practices already had case managers, CMP management observed that most physician practices were apprehensive about changes such as the introduction of new staff into their practice. CMP leadership used a tailored approach to discuss the project with each practice, offering positive anecdotes from the PBCM pilot project as appropriate. In addition, CMP leadership identified a physician champion for the CMP within each physician practice that had at least 25 or more CMP patients at the start if the project to further ease the transitions involved in the introduction of a case manager into the practice. During program implementation physician champions provided insight about the best way to incorporate case managers into the practice and encourage colleagues to take advantage of services available from the case managers.86

Dr. Fishbane underscored the importance of establishing effective partnerships with the partner nephrologists during [Village Health’s] Phase II and was optimistic about the efforts to secure physician champions, garner enthusiasm and support, and improve physician engagement at the first Medical Advisory Board meeting.82

CMO 3.3. Face-to-face outreach to physicians and their staff by program leaders and/or nurse care managers [C] effectively garners support of the intervention from existing care providers [O] by helping existing care providers understand the value of the intervention [M].

Case managers assigned to each practice met with physicians at the practices to describe the program, the skills that they bring to the physician practice, and their interest in collaborating to support patients in their efforts to manage their medical conditions. Case managers collected information from providers about how they could add value to the medical practice.86

Acquiring buy-in from participating physician practices was viewed as very important. However, it was recognized early on that buy-in was needed on all levels. There was some concern among practice-based nurses, particularly at smaller practices, that there would be a duplication of effort. To obtain buy-in from the nurses, the CMP case managers spent time working with the practice-based nurses to educate them that the goal of the program was to augment and not to replicate their efforts.86

In addition to distributing marketing materials and conducting group presentations, a TST [Texas Senior Trails] nurse with utilization management and provider relations experience visited the offices of the 250 doctors in the Lubbock and Amarillo areas with the highest numbers of CMHCB [Care Management for High Cost Beneficiaries] demonstration-eligible patients. This nurse was largely successful in gaining physician support for the program, often as a result of spending time with physician office staff and administrators who conveyed information about the program to the physicians…Similarly, the TST medical director in Amarillo had so much difficulty obtaining physician support via phone calls to these individuals that he ceased conducting these outreach calls. At the time of our site visit, the TST medical director and managing director were continuing to look for ways to market the program to providers who were not supportive initially. In particular, they were developing messages that conveyed the fact that the program can serve as a resource for physicians, by providing support for patients who are hard to manage because of mental health and/or social issues.79

CMO 3.4. Using a multi-pronged approach to provide physicians with information about intervention services [C] made it more likely to reach doctors to get their support and engagement [M] in the intervention necessary for the program to succeed [O]

The program only works well when physicians are highly engaged.86

MGH enlisted physician support to help ensure the success of its CMP in providing high-quality care to patients. Physicians were asked to conduct the following activities: encourage beneficiaries to participate in the program and enroll them in the program when possible; collaborate with case managers to review initial assessment findings and develop care plans for each patient; inform case managers about patient events and refinements to patient care plans during the demonstration period; and discuss advance directives with enrolled patients…MGH physicians received information about the CMP from a variety of sources, including the program’s medical director, the MGH electronic newsletter, and case managers assigned to each practice.86

CMO 3.5. When an intervention includes an insufficient number of patients the physicians [C], physicians do not fully engage and participate in the program [O] because they feel the intervention is not a good investment in time and resources [M].

The staff also suggested implementing a physician referral model to gain physician buy-in and to identify sufficient numbers of patients to make a financially viable care management program. A physician referral model could increase enrollment by more than 10 times, according to one physician’s estimate, with which others agreed. Interviewed physicians and care managers felt that a physician referral model would increase the appropriateness of patients referred for care management services. It was recommended that patient-specific clinical or educational goals accompany an open physician referral model in order to ensure that participants have clearly identified goals against which to measure their progress.80

Although most physicians were supportive of the outreach efforts, they generally only had one or two patients participating in the program. The program had greatest success with offices that had approximately 30 patients participating in the program…To the extent that patients were concentrated with providers, program staff felt that the physicians were better allies and facilitated the clinical interventions. “A couple things we’ve gotten a little bit smarter about—one is the alignment to the provider… One of the things I would definitely do differently is for ESRD [end-stage renal disease] patients, I would do DaVita only and see what kind of change we could drive there. Then if we had a great solution, we could think about how we could scale it. That was probably 70% of the operational hassle that didn’t actually do anything for patients but took a lot of time and energy. The same is true on the CKD [chronic kidney disease] side with the nephrologists.”82

Although the nephrologists were very engaged initially, the program had less of a renal focus than anticipated given that the beneficiary population did not have the extent of CKD that was originally projected. As a result, the program did not maintain as high visibility among physicians during Phase I as the KTBH [Village Health’s Key to Better Health] program leadership would have liked.82

During the first site visit, physicians at both sites reported that they were initially very enthusiastic about the Health Buddy® program, because it offered a promising way to effectively support patients with chronic disease. The Health Buddy® technology coupled with telephonic care management support was viewed as an effective way to maintain and improve patient health and identify symptoms of complications early, so that timely medical intervention could be used to prevent serious problems requiring hospitalization. Once the physicians received the list of patients who were eligible for the Health Buddy® program, they reported that they became frustrated with the project because they felt that many of the patients selected would not benefit from participating. Further, physicians reported disappointment that many of the patients they believed could be helped by the program were not eligible to participate in the program because they had not been identified through the claims based algorithm developed by HHN [Health Hero Network]…Using information gleaned from its early experience with the program, the HBC [Health Buddy Consortium] made a series of changes and enhancements to its operations and as reported to us at our second site visit.80

CMO 3.6. Developing and implementing a financially supportive system or model for physicians and their practice [C] encourages and motivates physicians and other care providers [M] to spend time with their patients and to continue supporting innovative intervention activities [O].

The Care One program provides incentives to primary care providers by valuing a Care One patient as equal to 5 normal primary care patients when adjusting panel size.92

MGH provided physicians with a $150 financial incentive per patient in Year 1 and $50 in Years 2 and 3 to help cover the cost of physician time for these activities.86

Thus, for such team care to be sustainable, time needs to be carved out for the work involved and systems need to support the follow through.102

The staff also suggested implementing a physician referral model to gain physician buy-in and to identify sufficient numbers of patients to make a financially viable care management program. A physician referral model could increase enrollment by more than 10 times, according to one physician’s estimate, with which others agreed.80

Physician: ‘When your hospital is basically saying… ‘Here is 15 minutes for a repeat visit for another patient’, I mean how are you gonna be able to actually provide the kind of care they need?’61

Recent changes in the Medicare Advantage program (nearly all of ChenMed’s patients are enrolled in Medicare Advantage) have created a favorable environment for delivery system innovation. In particular, the 2004 introduction of the Hierarchal Condition Categories risk adjustment model created a mechanism that reduced the financial risk of taking care of high cost patients with multiple chronic conditions. Patients with multiple chronic conditions have higher risk scores and, accordingly, higher reimbursement. Although not perfect, risk adjustment has alleviated participating payers’ and providers’ concerns about attracting sicker and costly patients without receiving commensurate reimbursement.114

In addition, Medicare Advantage’s capitation model is more favorable to delivery system innovation than traditional fee-for-service Medicare because it eliminates the process of negotiating reimbursement for cost-reducing delivery system innovations. Because providers are paid according to the size of their patient panel in a capitated system, they have an incentive to develop and test innovations to determine which ones lower the cost of care without compromising quality—and, ideally, increase it. For those innovations judged to be cost reducing without compromising quality, providers in a capitated system have the flexibility to deploy the innovations across their network. Providers in the fee-for-service system do not have such flexibility because they must negotiate with payers for the reimbursement of care delivery innovations—a step that can delay or even block such efforts.114

Administrative pressures in health care delivery systems. Physicians and health system leaders felt existing payment structures and administrative pressures (such as the impetus to maximize the number of patients seen while minimizing visit time) negatively impacted the way they could interact with patients. Many agreed that when such a limited time frame is allotted for each patient, it barely gives providers time to think, resulting in the delivery of ‘bad care’. This also affected the way physicians communicate with their patients in key situations including discussions of illness, treatment options and care plans. Finally, stakeholders felt that the current care delivery model significantly dis-incentivized physicians from going into primary care, leading to a primary care physician shortage. The underlying sentiment was that if there are fewer primary care doctors overall, then HNHC patients will be at a greater disadvantage to have continuity of care at a primary care site, their diseases will not be well-controlled, leading to more ED visits and inpatient admissions.61

Staffing arrangements in care management interventionsCMO 3.7. Reducing providers’ workload and responsibilities associated with the implementation of complex intervention activities [C] will reduce provider stress [M] so providers are more satisfied [O1] and more willing and able to engage with their patients and in participate intervention activities [O2] such as attending care team meetings, and carrying out care plans.

At baseline, members were divided in the anticipated effect of team care on their workload and stress levels. At 3 months, one member noted a decrease in workload, and three perceived an increase. Two indicated that the intervention “increased my stress by adding to my many responsibilities.” Getting to Care Team meetings on time was difficult for about half of the team members.102

Some team members felt their work increased by participating in the team.102

Early on, we determined that certain tasks the HC RNs [Health Coach Registered Nurses] and LCSWs [Licensed Clinical Social Workers] were performing could be offloaded as these did not require their level of licensure, training, and skill. By doing so, we could free up the HC RNs and LCSWs to serve more patients and increase their job satisfaction.91

Respondents from both independent and system-owned practices described turnover that occurred because care managers felt overwhelmed with numerous responsibilities.101

Patient And Physician Time We also emphasize physician and patient time. Our primary care physicians, all internists, have a panel of 350–450 patients. By comparison, physicians at many commercial “concierge” practices, where patients pay sizable out-of-pocket retainers for the additional physician time, have larger panels. Small panel sizes allow our physicians to spend more time with their patients. Our physicians average fewer than eighteen visits a day; in contrast, primary care physicians average nearly thirty.114

Physician: ‘Need more primary care physicians who can manage outpatient things… And so you end up not being able to fill the need, and then we see them in the emergency department.’61

CMO 3.8. Case managers, social workers, and high functioning administrative assistants in turn take on many time-consuming tasks (e.g., medication management, identifying community services, outreach, engagement) to help engage and manage HNHC patients and their paperwork [C] so that providers can focus their efforts [M] on providing continuous, comprehensive care to patients [O].

Early on, we determined that certain tasks the HC RNs and LCSWs were performing could be offloaded as these did not require their level of licensure, training, and skill…To address this, we worked with the teams to identify tasks that could be done by high functioning Administrative Assistants (AAs) and we now use AAs for tasks such as maintaining telephone contact with patients to remind them of appointments, check up on them when they have not been heard from, and assist the team members in entering and retrieving data related to the patients they serve. This is effective as long as there are intermittent face to face opportunities for the patients with the HC RNs, LCSWs, and CCLs [Client-Community Liaison].91

“…Case managers take care of things like preauthorization, gathering documentation, medication tracking and other time-consuming issues, allowing PCPs [primary care providers] to focus on the relationship with patients and provide real continuity of care.;…The program does what every PCP needs to be doing but cannot do anymore because of the medicine practice and reimbursement realities and primary care provider shortages.;…Both patients and physicians love the program as case managers take a lot of burden off both sides.;…Key value of the program is in the help they provide PCPs with medication review and management, the most difficult to resolve issue when PCPs do not have any help;…” [Summary from a focus group of multiple physicians]86

The care manager served primarily as an adjunct to the patients’ primary physicians.80

As in 2015, deep-dive practice respondents described approaches to improving support for care managers, to clarify their roles and enhance staffing resources to help them feel less overwhelmed…A few practices were monitoring care managers’ caseloads to determine whether they needed more staff to support high-risk patients, or to reduce (or even eliminate) activities focused on lower-risk patients. These practices brought in social workers to help meet patients’ social needs and medical assistants to assume logistical or administrative tasks.101

Navigators shared information about individual patients utilizing a team-based navigation model that provided flexibility in dividing the workload and providing cross coverage.110

The program was staffed by a multidisciplinary care team consisting of a community health worker (CHW), a social worker (SW), and a PCP [primary care provider]…With guidance and support from the SW and the PCP, the CHW was responsible for patient outreach, engagement, activation, and accompaniment. The SW was responsible for counseling and brief interventions for patients with behavioral health needs and for coordinating referrals to social service agencies and other medical providers. The PCP was responsible for providing comprehensive care for acute and chronic conditions and for coordinating with specialists and inpatient providers.108

The CHW was able to identify unmet social needs contributing to acute care utilization that may not be apparent to busy clinicians and are not readily addressed during a single ED or clinic visit. For example, one patient with chronic restrictive lung disease who was dependent on home oxygen experienced financial insecurity and anxiety related to his inability to make on-time utility payments. The CHW was able to enroll him in a financial assistance program to prevent utility shutoffs, provide a list of local food pantries, and accompany him to primary care appointments where he was connected with the pulmonology clinic social worker who assisted with ongoing needs.111

CMO 3.9. Providing training for staff members [C] gives them the confidence and skills [M] to function effectively as a care team [O1] and to understand and work with HNHC patients [O2].

“…The program has done a remarkable job in training and cultivating case managers who are very good at breaking barriers and making it work for the most difficult patients;…” [Summary from a focus group of multiple physicians]86

…our team members received minimal training in ways to decrease frequent attendance and did not follow a systematic approach in assessing the patient…A more systematic approach, however, would have improved the function of our team.102

Both navigators completed training at the Harold Freeman Institute for Patient Navigation, a 2-day intensive training program that teaches navigators to identify and eliminate barriers to care and serve as a support hub for patients moving through the health care system (22). The PN also completed local training at Gateway Community College, which emphasized needs and resources within the local community (23).110

CMO 3.10. When care managers have regular opportunities to talk across offices and health care systems [C], they are more emotionally and technically prepared [M] to work with HNHC patients [O].

As in 2015, deep-dive practice respondents described approaches to improving support for care managers, to clarify their roles and enhance staffing resources to help them feel less overwhelmed. In some practices affiliated with health systems, respondents described providing opportunities for care managers embedded in practices across the health system to meet regularly, share best practices, and offer one another support.101

CMP leadership also emphasized team support and peer counseling by developing infrastructure that provided opportunities for mutual support among CMP case managers and peer counseling from the members of the mental health team as the emotional toll on staff of working with a highly frail and sick population are substantial.86

CMO 3.11. Having small care teams [C] helps teams members develop awareness of each HNHC patient’s entire complex care [M] which can improve the coordination of patient care [O].In addition, the ability for a team to be small and nimble was seen as a strength as it allowed for increased cohesion. “One of the issues with complex care is [it’s] spread out amongst a bunch of different people.… There’s a learning curve each time the patient meets with a different provider.… With SUMMIT [Streamlined Unified Meaningfully Managed Interdisciplinary Team], it’s a small team.… Everybody knows what’s going on with the patients in terms of their conditions and it really cuts through the confusion.” (Usual Care LCSW)58
Communication across the care teamCMO 3.12. Leadership-supported, regular communication across all staff [C] builds collaborative feelings among teams [M] that results in job satisfaction for care team members [O], and facilitates implementation success [O].

Team communication was important for program implementation, although sites had different levels of success in this area over time. Care teams with a solid supervisory structure and frequent collaboration across all levels of staff experienced greater implementation success and staff satisfaction.85

Due to the complexity of the CMP demonstration population, CMP leadership felt that constant and good communication between all staff within the program was essential.86

CMO 3.13. Having transparent and supportive communication among care team members [C] fosters shared values and commitment [M] that results in stronger, more cohesive care team [O].

Team members caring for HNHC patients noted the importance of shared values and commitment, citing mutual respect for other disciplines and appreciation of the need for teamwork. “We respect one another’s clinical view.… We come at this from different backgrounds and feel like we get more out of our patient care experience if we hear what everyone else has to say.… We have a very supportive and inclusive team environment” (SUMMIT Physician) The importance of the team comes through particularly when patients aren’t faring as well as hoped: “They [other team members] really listen and they really care and we all really feel it when someone does fail…or something bad happens. It’s a very empathetic group of people.…” (SUMMIT Nurse)58

Our finding that staff members value a sense of unity and esprit de corps speaks to the value of cohesive multidisciplinary teams doing this work. As prior studies have shown, individual members of multidisciplinary teams may have different conceptualizations of which disciplines are part of a care team—often these are only a team in name.58

CMO 3.14. Regular, multidisciplinary care team meetings that include physicians and staff [C] gave care team members the openness [M] to discuss patient cases [O1] and the practices’ performance on quality metrics, outcomes, and other performance goals [O2].

Our CHAs [Community Health Advocates] provide perspectives in huddles that often enlighten licensed staff and offer a better understanding for the team regarding the unique needs of the patients we serve.91

VPA [Visiting Physicians Association] corporate medical directors conducted weekly company-wide, web-based meetings with all clinicians, and regional managers conducted individual meetings with IAH practices, to review clinicians’ performance on IAH [Independence at Home] quality metrics and outcomes and consider broader implications for all of their patients.103

Common themes and issues from the Virtual Rounds were also presented at bimonthly management meetings. The bi-monthly management meetings were used to review protocols, present resources, provide training, and identify issues and brainstorm solutions.86

In addition to tracking metrics, most practices reported conducting care team meetings. Care team meetings provided a forum for clinical teams and staff to review quality metrics and progress toward performance goals, discuss an individual beneficiary’s case, and receive information on clinical topics.103

We have designed processes and structures that promote a physician culture of collaboration, transparency, and accountability for high-quality care. For example, our primary care physicians meet three times a week to review hospitalized patients and discuss complex cases practice approaches. Specialists and hospitalists join these meetings as well. We use these sessions to conduct traditional morbidity and mortality review as well as to review each hospitalization and ask, “Could this hospitalization have been prevented?” Physicians are prepared to discuss each hospitalized case and explain to their peers the circumstances involved and their clinical thinking.114

The team met weekly throughout the course of the intervention. One- or two-page patient summaries were prepared by the navigators, including a detailed, written summary of the patient’s medical history, prior ED use, barriers to accessing primary care services, life stressors that could be impacting their health, and type of help the patient wished to receive from the program. Each new patient was discussed by the team after the initial enrollment and on an as-needed basis (e.g., emergence of a new or challenging need or a repeat ED visit). The team discussed ways to support the patient’s clinical and social needs, brainstormed specific resources that might be helpful for the patient, and provided guidance to the patient navigators (24).110

We held multidisciplinary team meetings weekly to develop care plans to support patients’ clinical and social needs.110

CMO 3.15. Having regular care team meetings to discuss HNHC patients [C] may increase provider workloads [M] causing providers’ to be arrive late for meetings [O1] and to not carry out care plans [O2].The primary barriers to conducting regular Care Team meetings were the lack of time to meet and carry out the Care Plan and the difficulty of involving the patient…The team met for 40 min on a weekly basis to discuss one or two of the cases. The physicians were the most likely to arrive late and as noted by the chart review, were at times unable to follow through on the Care Plans.102
CMO 3.16. When providers are given practical, constructive feedback about patient care approaches [C], providers are provided with the clinical knowledge or resources they need [M] to improve the care they provide to their patients [O]

“[Care team meetings] give us an opportunity to look back upon our encounter with the patient and really be able to gauge, ‘Was there a reason why the hospitalization happened, could it have been prevented, is there something that I missed?’ … It can be a little bit unnerving … but it [has] actually … strengthened my practice quite a bit. Because you learn a lot from that feedback.”103

Clinicians valued receiving performance feedback and appreciated the opportunity to discuss cases with other clinicians and share ideas to improve care.103

The CMP leadership implemented Virtual Rounds, regular e-mail reports that went to all staff, as a mechanism of providing feedback on a weekly basis. Case managers used Virtual Rounds to report on difficult patients and unnecessary admissions, and to describe both positive and negative events. Virtual Rounds were also used for case reviews with forms that staff filled out at the end of the week. These case reviews were then discussed with physicians in weekly face-to-face meetings.86

Physicians in our study acknowledged their frustration in caring for frequent attenders, but also received specific, practical suggestions for changing their approach to care.102

Peer consultation provides much needed perspective, more objective assessment and support for the difficulties of the case.102

CMO 3.17. Having patients who received care from providers in other healthcare systems or locations [C] creates challenges for care teams [M] to be able to effectively and efficiently communicate with the patient’s providers [O].

TST staff reported that most participants had a primary provider that was associated with TTUHSC [Texas Tech University Health Sciences Center]; however, many patients, particularly in Amarillo, received care from additional providers that were not associated with the university. These providers typically operated independent practices, so TST care managers had to establish relationships with a number of different practices.79

The second proposed improvement had to do with excluding beneficiaries from practices outside the care management organizations, if a systematic means of communicating with clinicians from these practices is not established.80

Further, not all intervention beneficiaries had primary care physicians in the two study sites, therefore the care managers had to interact with community-based providers with whom they had little or no prior relationship. During our site visits, the care managers cited several challenges working with these physicians, in particular, because of communication barriers.80

CMO 3.18. Providing opportunities for face-to-face conversations among care team members (e.g., being co-located, creating spaces that allow for provider conversations) [C] helps build strong working relationships [M] that improve team communication in support of coordination of patient care [O].

Our findings speak to the importance of co-located, embedded teams that “hear what everyone else has to say.”58

…the Health Buddy® nurse care managers often were not in direct proximity to their beneficiaries’ primary care physicians, thereby potentially affecting their interactions with the beneficiaries’ primary providers, changing medical care plans, or mitigating deterioration in health status…Interviewed physicians felt that care management would be more effective and efficient if care managers were colocated with primary care physicians.80

Later, they returned patient care coordinators to local practice sites after clinicians and patients expressed dissatisfaction with the centralized system. According to one respondent, locating at the practice enables patient care coordinators to have more inperson contact with clinicians and to build relationships with patients. This change promoted strong working relationships among teams of clinicians, medical assistants, and care coordinators. Those strong working relationships help to address patients’ needs and avoid unnecessary readmissions and hospital and ED [emergency department] visits. Another practice changed where the physicians and other staff on the care team sat in the office. This practice clustered the care team together so they could discuss patients’ concerns and care delivery more easily.103

We have also designed our centers to promote physician collaboration and conversation. They look more like an inpatient setting or intensive care unit than a traditional physician office. There is a large nurses’ station in the middle of the center where specialists do their paperwork, which is a sufficient distance from the patient exam rooms to allow for spontaneous discussions between specialists and primary care physicians after the specialist has seen the patient. In addition, there is a cluster of four to six individual primary care physician workstations away from direct patient view, where private conversations among physicians can readily happen. In the vast majority of cases, a specialist is able to have a brief face-to-face conversation with the patient’s primary care physician after she or he sees the patient. The face-to-face conversation allows for more rapid alignment between primary care physician and specialist than the traditional faxed consult and voice mail.114

CMO 3.19. Embedding care/case managers into primary care practices [C] makes it more efficient [M1] and convenient [M2] for physicians to use case managers services [O].

One improvement proposed was featuring a care management structure that pairs care managers and participants’ primary care physicians in the same physical location.80

CPC [Comprehensive Primary Care] practices greatly increased their use of dedicated care managers who were members of the primary care practice team over time. The number of practice survey respondents from CPC practices who reported that “care managers who were members of the practice team systematically provided care management services to high-risk patients” increased from 20 percent in 2012 to 88 percent in 2014 and 2015, and 89 percent in 2016. In comparison, fewer than half of comparison practices reported in 2016 that care managers who were practice care team members systematically provided these services to high-risk patients101

Most physicians supported the general concept and potential benefits of the program but also expressed frustration with several aspects of the current demonstration design…care managers were not embedded in their physical practice locations.80

At the time of the program launch, strong integration support from MGH leadership afforded the case managers physical entry into the primary care practice settings whereby the case managers were co-located with the primary care physicians ultimately becoming a part of the beneficiaries’ primary health care teams.86

C = context; M= mechanism; O = outcome

Key Question 3. Overall, what is the effectiveness and what are the harms of interventions for HNHC patients in reducing potentially preventable or modifiable healthcare use and costs and in improving health outcomes?

Table B-14Summary of strength of evidence by outcome and study model type (by primary setting)

Outcome GroupOutcome MeasurePopulationSystem Level (N=5)Telephonic/Mail (N=9)Community Based (N=9)ED Based (N=7)Ambulatory Intensive Caring Unit (N=3)Primary Care (N=10)Home Based (N=4)
Utilization outcomesED visits, all causeaHNHC patientsIL-NDIM-FI1II
ED visits at 270 days, all causeHNHC patients--I1----
ED visits at 180 days, all causeHNHC patients--I1----
ED visits, ACSCHNHC patientsI1L-ND---II
ED visits, outpatientHNHC patientsI1------
ED visit resulted in inpatient admissionHNHC patientsI1------
ED, any (%)HNHC patients--I1--I1-
Psychiatric emergency visitsHNHC patients--I1I1---
Inpatient admissions, all causeaHNHC patientsIL-NDIL-FI1II
Inpatient admissions, any (%)HNHC patients-II1--L-FI
Inpatient admissions at 270 days, all causeHNHC patients--I1----
Inpatient admissions at 180 days, all causeHNHC patients--I1----
Inpatient admissions, ACSCHNHC patientsI1L-NDI1--IL-F
Inpatient admissions, any ACSC (%)HNHC patients-I---IL-F
Acute medical/surgery staysHNHC patients----I1--
Other inpatient staysHNHC patients----I1--
Inpatient daysHNHC patients--II1-I-
Medical inpatient admissionsHNHC patients---I1---
Medical inpatient daysHNHC patients---I1---
Psychiatric inpatient admissionsHNHC patients--I1I1---
Psychiatric inpatient daysHNHC patients---I1---
Total hospital encountersHNHC patients--I1----
Hospital encounter resulted in discharge to hospital or observation stayHNHC patients--I1----
Hospital encounter resulted in discharge from EDHNHC patients--I1----
Outpatient visitsHNHC patients--I1I-I-
Outpatient visits at 6 monthsHNHC patients-I1-----
Outpatient visits at 12 monthsHNHC patients-I1-----
Outpatient visits at 30 monthsHNHC patients-I1-----
Outpatient substance use treatment visitsHNHC patients--I1----
Outpatient mental health visitsHNHC patients--I1----
Outpatient mental health visit, anyHNHC patients--I1----
Primary care visitsHNHC patientsI1b--L-FcI1I-
Primary care visits at 360 days, all causeHNHC patients--I1----
Primary care visits at 270 days, all causeHNHC patients--I1----
Primary care visits at 180 days, all causeHNHC patients--I1----
Total utilizationHNHC patients-I1---I1-
180-day readmission, countHNHC patients--I----
180-day readmission, any (%)HNHC patients--I1----
180-day readmission, ≥2 (%)HNHC patients--I1----
Specialist visitsHNHC patientsI1---I1I1-
Prescription drugs, anyHNHC patients--I1----
Long-term care, anyHNHC patients--I1----
FQHC visitsHNHC patientsI1------
Filled ≥3 antidepressant prescriptions in first 6 monthsHNHC patients-----I1-
Specialty mental health visit in first 6 monthsHNHC patients-----I1-
Cancelled visits and/or no showsHNHC patients-----I1-
Intensive care unit visitsHNHC patients-----I1-
Dental visitsHNHC patients-----I1-
Care center visitsHNHC patients-----I1-
Probability of entering institutional long-term care within the demonstration yearHNHC patientsI1------
Long-term institutionalization rateHNHC patients------I1
Probability of hospice useHNHC patientsI1------
Probability of SNF useHNHC patientsI1------
Probability of home health useHNHC patientsI1------
Home health daysHNHC patientsI1------
Home health visitsHNHC patientsI1------
Visits in nonacute settings by primary care cliniciansHNHC patientsI1------
Visits in nonacute settings by specialistsHNHC patientsI1------
Care management visitsHNHC patients----I1--
Mental healthcare visitsHNHC patients----I1--
Homeless care visitsHNHC patients----I1--
ED visits at 180 days, all causeHNHC patients with a mental health diagnosis--I1----
Inpatient admissions at 180 days, all causeHNHC patients with a mental health diagnosis--I1----
Primary care visits at 180 days, all causeHNHC patients with a mental health diagnosis--I1----
Outpatient visits at 6 monthsHNHC patients with arthritis-I1-----
Outpatient visits at 6 monthsHNHC patients with high blood pressure-I1-----
Outpatient visits at 6 monthsHNHC diabetes patients-I1-----
Outpatient visits at 12 monthsHNHC patients with arthritis-I1-----
Outpatient visits at 12 monthsHNHC patients with high blood pressure-I1-----
Outpatient visits at 12 monthsHNHC diabetes patients-I1-----
Outpatient visits at 30 monthsHNHC patients with arthritis-I1-----
Outpatient visits at 30 monthsHNHC patients with high blood pressure-I1-----
Outpatient visits at 30 monthsHNHC diabetes patients-I1-----
Total utilization (%)HNHC patients living in a low income area-I1-----
Total utilization (%)HNHC patients living in a low education area-I1-----
Total utilization (%)HNHC patients with Medicaid-I1-----
Cost outcomesTotal costsHNHC patientsL-NDL-NDII1L-FL-FI
Inpatient costsHNHC patients--IL-NDcI1I1-
ED costsHNHC patients--IL-FI1I1-
Hospital costs of careHNHC patients---I---
Hospital chargesHNHC patients--I1----
Hospital payments receivedHNHC patients--I1----
Indirect ED costsHNHC patients--I1----
Medicaid costHNHC patients---I1---
Psychiatric emergency costsHNHC patients---I1---
Psychiatric hospital costsHNHC patients---I1---
All non-ED case management costsHNHC patients---I1---
Acute costsHNHC patientsI1------
Post-acute costsHNHC patients-----I1-
Outpatient department costHNHC patientsI1------
Outpatient costsHNHC patientsI1-I1IcI1I1-
Prescription or pharmacy costsHNHC patients--I1I1-I1-
Primary care physician costHNHC patientsI1------
Long-term care costsHNHC patients--I1----
Other costsHNHC patients-----I1-
Total costsHigh-cost, high-risk HNHC patients------I
Total costsHigh-cost HNHC patients------I
Total costsHNHC patients with dementiaI1------
Total costsHNHC patients without dementiaI1------
Clinical and functional outcomesMortality rateHNHC patientsI1L-NDL-NDI1IIL-ND
Influenza vaccineHNHC patients-I---L-UL-F
Progression to ESRDHNHC patients-I-----
PHC score (physical health)HNHC patients-I1---I1I1
MHC score (mental health)HNHC patients-I1---I1I1
PQH-2 score (depression)HNHC patients-I1---I1I1
Number of ADLs difficult to doHNHC patients-I1---I1I1
Number ADLs receiving helpHNHC patients-I1---I1I1
Helping to cope with a chronic conditionHNHC patients-I1---I1I1
Number of helpful discussion topicsHNHC patients-I1---I1I1
Discussing treatment choicesHNHC patients-I1---I1I1
Communicating with providersHNHC patients-I1---I1I1
Getting answers to questions quicklyHNHC patients-I1---I1I1
Multimorbidity Hassles scoreHNHC patients-I1---I1I1
Percent receiving help setting goalsHNHC patients-I1---I1I1
Percent receiving help making a care planHNHC patients-I1---I1I1
Self-efficacy: Take all medicationsHNHC patients-I1---I1I1
Self-efficacy: Plan meals and snacksHNHC patients-I1---I1I1
Self-efficacy: Exercise 2 or 3 times weeklyHNHC patients-I1---I1I1
Self-care activities: Prescribed medications taken (mean # of days)HNHC patients-I1---I1I1
Self-care activities: Followed healthy eating plan (mean # of days)HNHC patients-I1---I1I1
Self-care activities: 30 minutes of continuous physical activity (mean # of days)HNHC patients-I1---I1I1
Patient satisfactionHNHC patients---I1I1I1-
Psychiatric symptoms (total BSI)HNHC patients---I1---
Access to careHNHC patients----I1--
SF-36 Summary ScoreHNHC patients-----I1-
SF-36 Mental Health Function ScoreHNHC patients-----I1-
SF-20 subscale: Social FunctioningHNHC patients----I1-
SF-20 subscale: Mental HealthHNHC patients-----I1-
SF-20 subscale: General HealthHNHC patients-----I1-
SF-20 subscale: Physical FunctioningHNHC patients-----I1-
SF-20 subscale: Role FunctioningHNHC patients-----I1-
SF-20 subscale: Pain PerceptionHNHC patients-----I1-
Change in HAM-D scoreHNHC patients-----I1-
In remission (HAM-D < 7)HNHC patients-----I1-
Patient-centered care coordinationHNHC patients----I1--
Relationship with providers: trusted providerHNHC patients----I1--
Relationship with providers: feel respected by providerHNHC patients----I1--
Healthcare hassles summary score (challenges in getting care)HNHC patients----I1--
Patient assessment of chronic illness care (PACIC) Summary Score (receipt of care for chronic illness)HNHC patients----I1--
Progression to ESRDHNHC CKD patients-I-----
Graft or fistula prior to hemodialysisHNHC CKD patients-I-----
Graft or fistula prior to hemodialysisHNHC ESRD patients-I-----
HbA1c testHNHC patients, diabetes subgroupI1I---II
LDL-C testHNHC patients, diabetes subgroupI1I---II
LDL-C testHNHC IVD patients-I---II
Eye examHNHC patients, diabetes subgroupI1I-----
Nephrology/nephropathy testHNHC patients, diabetes subgroupI1I-----
Lipid testHNHC patients, IVD subgroupI1I-----
Oxygen saturation testHNHC COPD patients------I
Social risk outcomesParticipation in Supplemental Nutrition Assistance Program, anyHNHC patients--I1----
Receipt of temporary assistance for needy families, anyHNHC patients--I1----
Receipt of general assistance, anyHNHC patients--I1----
Ever housedHNHC patients--I1I1---
Jail staysHNHC patients--I1----
Criminal convictions, anyHNHC patients--I1----
Criminal convictionsHNHC patients--I1----
Shelter daysHNHC patients--I1----
Drug/alcohol treatment, anyHNHC patients--I1----
Overall wellbeingHNHC patients-----I1-
Problem alcohol use, anyHNHC patients---I1---
Homelessness, anyHNHC patients--I1I1---
Homeless monthsHNHC patients--I1----
No health insurance, anyHNHC patients---I1---
No social security income, anyHNHC patients---I1---
Basic financial needs unmetHNHC patients---I1---

I1: Insufficient, only one sample reporting on the outcome; I: Insufficient, 2+ samples reporting on the outcome within the model type; L-F: Low strength of evidence for favorable findings for the outcome; L-ND: Low strength of evidence for no difference for the outcome; L-U: Low strength of evidence for unfavorable findings for the outcome; -: No eligible evidence; M-F: Moderate strength of evidence for favorable findings for the outcome.

a

Includes visits at 12 months.

b

Defined as evaluation and management primary care visits by Kahn et al.122

c

Shumway et al. specified the outpatient and inpatient costs as medical outpatient costs and medical hospital costs.116

ACSC = ambulatory care sensitive conditions; ADL = activities of daily living; BSI = brief symptom inventory; CKD = chronic kidney disease; COPD = chronic obstructive pulmonary disease; ED = emergency department; ESRD = end-stage renal disease; FQHC = Federally Qualified Health Center; HAM-D = Hamilton Depression Rating Scale.; HbA1c = hemoglobin A1c; HNHC = high-need, high-cost; IVD = ischemic vascular disease; LDL-C = low-density lipoprotein cholesterol; MHC = mental health composite; PACIC = patient assessment of chronic illness care; PHC = physical health composite; PQH-2 = patient health questionnaire-2; SF = short form; SNF = skilled nursing facility; SOE = strength of evidence.

Table B-15Study characteristics for system-level transformation models

First Author, Year, Site(s)Brief Description (Sample Size)Study Design (Risk of Bias Assessment)Patient Selection: High Healthcare Use or Cost; Time PeriodPatient Selection: Chronic ConditionsPatient Selection: OtherAdditional Selected Patient Characteristics

Kahn et al., 2016122

503 FQHCs

Support for FQHCs obtaining PCMH status

(HNHC high ED use patient sample N=NR out of 730,353 beneficiaries total) Intervention (N=NR out of 269,364 beneficiaries total) Comparison (N=NR out of 360,989 beneficiaries total)

Observational study (RoB: some concerns)Utilization of ED visits at baseline: 90th percentile vs. <90th percentileNAMedicare FFS beneficiaries; attribution to the practice or clinic responsible for the greatest number of primary care services over the 12-month period preceded the demonstrationNA

Kimmy et al., 2019103, 132

National: 14 practices

Incentive payment (N=42,132)

Intervention (N=8,216)

Comparison (N=33,916)

Observational study (RoB: some concerns)Hospitalization and use of acute or subacute rehabilitation services

2+ chronic conditions

2+ ADLs that require human assistance

Medicare FFS beneficiaries; all IAH-eligible patients of the IAH practices, including those who received home-based care before the demonstration began; not in hospice or long-term care for the entire time they were eligible for the intervention in a given year

Number of chronic conditions in Year 4 Total

<6: 9.8%

6–9: 43.7%

>9: 46.6%

HCC: 3.90

Depression: 54.3%

Peikes et al., 2018101

AR, CO, NJ, OR, NY, OH, KY, OK: 502 practices and 40 payers

Primary care model Shared savings: APM (N=1,730,958)

Intervention (N=565,674)

Comparison (N=1,165,284)

Subgroups (N=NR)

Observational study (RoB: some concerns)2+ hospitalizations in previous 2 years2+ of 13 eligible chronic conditions including congestive heart failure, COPD, acute myocardial infarction, ischemic heart disease, diabetes, any cancer other than skin cancer, stroke, depression, dementia, atrial fibrillation, osteoporosis, rheumatoid arthritis or osteoarthritis, chronic kidney diseaseReceived care in a CPC practiceNR

Peikes et al., 2019123, 133

AR, CO, HI, KC, KY, MI, MT, NJ, NY (Greater Buffalo); NY (North Hudson-Capital), ND, NE, OH, OK, OR, PA, RI, TN: 1,373 practices

Primary care model Shared savings: APM Track 1 multiple chronic condition subgroup (N=NR of 5,163,969)

Intervention (N=NR of 1,189,438)

Comparison (N=NR of 3,974,531)

Observational study (RoB: some concerns)1+ hospitalizations in past 1 year2+ of 12 eligible chronic conditions including congestive heart failure, COPD, history of acute myocardial infarction, ischemic heart disease, diabetes, severe cancer, history of stroke, depression, dementia, atrial fibrillation, rheumatoid arthritis or osteoarthritis, chronic kidney diseaseEnrolled in Medicare Parts A and B, Medicare FFS as primary payer, do not have ESRD and not enrolled in hospice, are not institution-alized or incarcerated, and are not attributed to a PCP for a nonoverlap CMS serviceNR

Peikes et al., 2019123, 133

AR, CO, HI, KC, KY, MI, MT, NJ, NY (Greater Buffalo); NY (North Hudson-Capital), ND, NE, OH, OK, OR, PA, RI, TN: 1,515 practices

Primary care model Shared savings: APM

Track 2 multiple chronic condition subgroup (N=NR of 4,804,265)

Intervention (N=NR of 1,443,553)

Comparison (N=NR of 3,360,712)

Observational study (RoB: some concerns)1+ hospitalizations in past 1 year2+ of 12 eligible chronic conditions including congestive heart failure, COPD, history of acute myocardial infarction, ischemic heart disease, diabetes, severe cancer, history of stroke, depression, dementia, atrial fibrillation, rheumatoid arthritis or osteoarthritis, and chronic kidney diseaseEnrolled in Medicare Parts A and B, Medicare FFS as primary payer, not ESRD and not in hospice, not institution-alized or incarcerated, and are not attributed to a PCP for a nonoverlap CMS serviceNR

ADL = activities of daily living; APM = advanced alternative payment model; AR = Arkansas; CMS = Centers for Medicare & Medicaid Services; CO = Colorado; COPD = chronic obstructive pulmonary disease; CPC = comprehensive primary care; ED = emergency department; ESRD = end-stage renal disease; FFS = fee-for-service; FQHC = Federally Qualified Health Center; HI = Hawaii; HNHC = high-need, high-cost; IAH = Independence at Home; KC = Kansas City; KY = Kentucky; MI = Michigan; MT = Montana; N = number; NA = not applicable; ND = North Dakota; NE = Nebraska; NJ = New Jersey; NR = not reported; NY = New York; OH = Ohio; OK = Oklahoma; OR = Oregon; PA = Pennsylvania; PCMH = patient-centered medical home; PCP = primary care provider; RI = Rhode Island; RoB = risk of bias; TN = Tennessee; vs. = versus.

Table B-16Intervention characteristics for system-level transformation models

First Author, Year, Site(s)Intervention: Brief DescriptionIntervention DurationAssessment, Education, Skills, MonitoringCoordination and Continuity of CareReferral to/Linkages to Community-Based Support ServicesProvidersMode of Delivery Setting(s)IntensityImpact on Clinician Workload/Clinical PracticeComparison

Kahn et al., 2016122

503 FQHCs

Three intervention components to support FQHC transformation into PCMHs: quarterly care management fee payments, technical assistance (TA), and data and performance feedback reports3 yearsPeriodically received three types of feedback reports: the biannual NCQA RAS report, the quarterly cost and utilization data reports, and the quarterly claims-based beneficiary-level report summarizing cost, utilization, and health. FQHCs were offered TA to prepare documentation for NCQA PCMH recognition through extensive learning systems involving varying partnersIntervention goals based on PCMH principles, which are designed to encourage doctors, hospitals, and other healthcare providers to work together to better coordinate care for patientsNRPCMHs are physician- or nurse practitioner–directed medical practicesNRNRNRUsual care at comparison FQHCs

Kimmy et al., 2019103

Effect of incentive payment National: 14 practices

Practices may earn an additional payment if their chronically ill, functionally limited patients’ Medicare expenditures are below an estimated spending target and if the practice meets required standards for a set of quality measuresUp to 4 yearsClinicians are available at all hours of the day; carry out individualized care plans; and use electronic health information systems, remote monitoring, and mobile diagnostic technologyReport on other measures, including fall risk assessments and depression screenings, to promote the provision of such careSome practices added social workers or other staff to coordinate care for their patients with other organizationsPhysicians or nurse practitioners. Team may have also included physician assistants, clinical staff, and other health and social services staffFace-to-faceClinicians made 3–15 home visits per day, varied by site≤1 home visit and no visit from a participating practice in study yearUsual care of Medicare-eligible beneficiaries living in IAH regions

Peikes et al., 2018101

AR, CO, NJ, OR, NY, OH, KY, OK: 502 practices and 40 payers

Comprehensive Primary Care (CPC) Initiative51 monthsCPC practices received financial support, data feedback, and learning supportPractices required to address access and continuity, planned care for chronic conditions and preventive care, risk-stratified care management, patient and caregiver engagementPractices required to address coordination of care across the medical neighborhood

CPC practices, multiple contractors and organizations provided different intervention elements to CPC practices

Patients received intervention services from regular practice staff and from specialized staff (e.g., care coordinators, care managers, social workers)

CMS sent reports on practice and patient-level data; learning support was peer-to-peer, didactic, and one-on-oneReports and financial support were sent quarterly, frequency of learning support variedPractices perceived that a big benefit of CPC participation was increased capacity to provide care management services to high-risk patients; practices shared with CMS any net savings in healthcare costs beyond amount required to cover their care management fee payments; within practices, care managers were increasingly integrated into clinicians’ workUsual care at non-CPC practices

Peikes et al., 2019123

AR, CO, HI, KC, KY, MI, MT, NJ, NY (Greater Buffalo); NY (North Hudson-Capital), ND, NE, OH, OK, OR, PA, RI, TN: 2,888 practices

Comprehensive Primary Care Plus (CPC+)2 yearsPractice-focused intervention; CPC+ practices received financial support, data feedback, health IT support, and learning support; track 2 CPC+ practices received enhanced payments and replacement of some fee-for-service payments with prospective payments

Required to address access and continuity, care management, comprehensive-ness and coordination, patient and caregiver engagement, and planned care and population health

CMS-specified care delivery requirements within each of these functions; they were considered a starting point and practices could choose which care delivery requirements or other changes to adopt first, which personnel would be involved and which tactics they would pursue

Care management, comprehensive-ness and coordination are 2 of the 5 key functions of a CPC+ practice

CPC+ practices; CMS partnered with 79 public and private payers across 18 CPC+ regions; various contractors and organizations provided intervention elements to CPC+ practices

Practices hired new staff to support CPC+ activities: care managers, behavioral health specialists, clinical pharmacists, social workers, data analysts, dietitians, diabetes educators, and QI staff

Practices received data reports; learning was delivered in groups and in-person practice coachingReports and financial support were sent quarterly, frequency of learning support NRNRUsual care at non-CPC+ practices

AR = Arkansas; CMS = Centers for Medicare & Medicaid Services; CO = Colorado; CPC = Comprehensive Primary Care; CPC+ = Comprehensive Primary Care Plus; FQHC = Federally Qualified Health Center; HI = Hawaii; IT = information technology; KC = Kansas City; KY = Kentucky; MI = Michigan; MT = Montana; NCQA = National Committee for Quality Assurance; ND = North Dakota; NE = Nebraska; NJ = New Jersey; NR = not reported; NY = New York; OH = Ohio; OK = Oklahoma; OR = Oregon; PA = Pennsylvania; PCMH = patient-centered medical home; QI = quality improvement; RAS = Readiness Assessment Survey; RI = Rhode Island; TA = technical assistance; TN = Tennessee.

Table B-17Healthcare utilization outcomes for system-level transformation model studies

Utilization MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
Inpatient admissionsObservationalNRNRFQHC: adjusted year 3 difference in visits: −5.32 (SE: 28.60) (p>0.10)122
ObservationalNRNRIAH incentive payment 5-year average annual effect DiD=−0.08 (SE: 0.05) (p>0.10)103, 132
Inpatient admissions, ACSCObservationalNRNRGreater reduction in G1 than G2: IAH incentive payment 5-year average annual effect DiD=−0.04 (SE: 0.02) (p<0.05)103, 132
ED visitsObservationalNRNRFQHC: adjusted year 3 difference in visits: −80.75 (SE: 87.65) (p>0.10)122
ObservationalNRNRGreater reduction in G1 than G2: IAH incentive payment 5-year average annual effect DiD=−0.14 (SE: 0.06) (p<0.01)103, 132
ED visits, outpatientObservationalNRNRIAH incentive payment 5-year average annual effect DiD=−0.02 (SE: 0.06) (p>0.10)103, 132
ED visits resulted in inpatient admissionObservationalNRNRGreater reduction in G1 than G2: IAH incentive payment 5-year average annual effect DiD=−0.11 (SE: 0.05) (p<0.05)103, 132
ED visits, ACSCObservationalNRNRIAH incentive payment 5-year average annual effect DiD=−0.00 (SE: 0.01) (p>0.10)103, 132
Primary care visitsbObservationalNRNRFQHC: adjusted year 3 difference in visits: −132.58 (SE: 84.18) (p>0.10)122
Specialist visitsObservationalNRNRFQHC: adjusted year 3 difference in visits: −29.67 (SE: 78.82) (p>0.10)122
FQHC visitsObservationalNRNRFQHC: adjusted year 3 difference in visits: −15.90 (SE: 54.65) (p>0.10)122
Probability of entering institutional long-term care within the demonstration yearObservationalNRNRIAH incentive payment 4-year average annual effect DiD=−0.38 (SE: 0.37) (p>0.10)132
Probability of hospice useObservationalNRNRIAH incentive payment 4-year average annual effect DiD=−0.83 (SE: 0.68) (p>0.10)103
Probability of SNF useObservationalNRNRIAH incentive payment 4-year average annual effect DiD=0.19 (SE: 0.82) (p>0.10)103
Probability of home health useObservationalNRNRIAH incentive payment 4-year average annual effect DiD=−0.45 (SE: 0.70) (p>0.10)103
Home health daysObservationalNRNRIAH incentive payment 4-year average annual effect DiD=−0.58 (SE: 5.67) (p>0.10)103
Home health visitsObservationalNRNRIAH incentive payment 4-year average annual effect DiD=0.14 (SE: 2.43) (p>0.10)103
Visits in nonacute settings by primary care cliniciansObservationalNRNRIAH incentive payment 4-year average annual effect DiD=0.59 (SE: 0.57) (p>0.10)103
Visits in nonacute settings by specialistsObservationalNRNRIAH incentive payment 4-year average annual effect DiD=−0.39 (SE: 0.32) (p>0.10)103
a

The reported p-value reflect the authors’ adjustment for multiple comparisons.

b

Defined as Evaluation and Management primary care visits by Kahn et al.122

ACSC = ambulatory care sensitive conditions; DiD = difference-in-difference; ED = emergency department; FQHC = Federally Qualified Health Center; G = group; IAH = Independence at Home; NR = not reported; SE = standard error; SNF = skilled nursing facility.

Table B-18Strength of evidence for system-level transformation model versus usual-care outcomes

PopulationOutcomeResultsStudy Design and Sample SizeStrength of Evidence DomainsOverall Evidence Strength (Direction of Effect)
HNHC patientsED visits

FQHC: adj diff=−80.75 (p>0.10);122

IAH incentive payment: lower use in G1 than G2: DiD=−0.14 (p<0.01)103

2 OBSs, N=NRaModerate study limitations, inconsistent, direct, impreciseInsufficient
HNHC patientsInpatient admissions

FQHC: adj diff=−5.32 (p>0.10);122

IAH incentive payment DiD=−0.08 (SE: 0.05) (p>0.10)103

2 OBSs, N=NRaModerate study limitations, consistent, direct, impreciseInsufficient
HNHC patientsTotal cost

FQHC: adj diff=$387.23 (p>0.10);122

CPC: DiD=−$45 (p=0.23);101

CPC+ sample 1: DiD=6.2 (p>0.10);133

CPC+ sample 2: DiD=43.0 (p=04);133

IAH incentive payment DiD=−200 (SE: 151) (p>0.10)103

Pooled mean difference: −$5.41 (95% CI, −38.28 to 49.10); 5 observational samples; I2=44.6%

5 OBSs, N=NRaModerate study limitations, inconsistent, direct, impreciseLow (No difference)
a

The FQHC,122 CPC,101 and CPC+123 studies did not report sample sizes for their HNHC patient populations; the total sample size for the FQHC was 730,353, 1,730,958 for CPC, 5,163,969 for CPC+ Sample 1, and 4,804,265 for CPC+ Sample 2. The sample size was 42,132 for the Independence at Home study.103

Note: CPC+ sample 1: HNHC patients in CPC+ practices making less advanced care delivery changes; CPC+ sample 2: HNHC patients in CPC+ practices making more advanced care delivery changes.

adj diff = adjusted difference; CI = confidence interval; DiD = difference-in-difference; ED = emergency department; FQHC = Federally Qualified Health Center; HNHC = high need, high cost; IAH = Independence at Home; N = number; NR = not reported; OBS = observational study; vs. = versus.

Table B-19Cost outcomes for system-level transformation model studies

Cost MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
Total cost ($)ObservationalNRNRFQHC: adjusted difference=387.23 (SE: 494.62) (p>0.10)122
ObservationalNRNRIAH incentive payment 5-year average annual effect DiD=−200 (SE: 151) (p>0.10)103, 132
ObservationalNRNRCPC DiD=−45 (p=0.23)101
ObservationalNRNRCPC+ Sample 1 2-year impact DiD=6.2 (SE: 19.2) (p>0.10))123, 133
ObservationalNRNRGreater increase in G1 than G2: CPC+ Sample 2 2-year impact DiD=43.0 (SE: 20.5) (p=0.04)123, 133
Outpatient costObservationalNRNRFQHC: adjusted differences in visits: 290.07 (SE: 178.26) (p>0.10)122
Acute costObservationalNRNRFQHC: adjusted for differences in visits: 292.47 (SE: 315.40) (p>0.10)122
OPD costObservationalNRNRFQHC: adjusted for differences in visits: 218.92 (SE: 150.97) (p>0.10)122
Primary care physician costObservationalNRNRFQHC: adjusted differences in visits: 18.99 (SE: 26.65) (p>0.10)122

Note: CPC+ sample 1: HNHC patients in CPC+ practices making less advanced care delivery changes; CPC+ sample 2: HNHC patients in CPC+ practices making more advanced care delivery changes.

CPC+ = Comprehensive Primary Care Plus; DiD = difference-in-difference;; FQHC = Federally Qualified Health Center; G = group; HNHC = high-need, high-cost; IAH = Independence at Home; NR = not reported; OPD = outpatient department; SE = standard error.

Table B-20Cost outcomes for system-level transformation model studies: Subgroup outcomes

Cost MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
Total cost (PBPM)ObservationalNRNRBeneficiaries with dementia subgroup: IAH incentive payment DiD=−222 (SE: 143)(p=NS)103
ObservationalNRNRBeneficiaries without dementia subgroup: IAH incentive payment DiD=−347 (SE: 279) (p=NS)103

DiD = difference-in-difference; G = group; IAH = Independence at Home; NR = not reported; NS = not statistically significant; PBPM = per beneficiary per month; SE = standard error.

Table B-21Clinical and functional outcomes for system-level transformation model studies

Clinical and Functional OutcomesStudy DesignDifference Between Intervention Group (G1) and Comparison Group (G2)
Mortality rateObservationalIAH incentive payment 5-year average annual effect DiD: −0.32 (SE: 0.55) (p>0.10)103, 132

DiD = difference-in-difference; G = group; IAH = Independence at Home; NR = not reported; SE = standard error.

Table B-22Clinical and functional outcomes for system-level transformation model studies: Subgroup outcomes

Clinical and Functional OutcomesStudy DesignDifference Between Intervention Group (G1) and Comparison Group (G2)
HbA1c testObservationalFQHC diabetes subgroup: adjusted year 3 difference in visits: 0.35 (SE: 1.16) (p>0.10)122
LDL testObservationalFQHC diabetes subgroup: adjusted year 3 difference in visits: −1.17 (SE: 1.37) (p>0.10)122
Eye examObservationalFQHC diabetes subgroup: adjusted year 3 difference in visits: 1.09 (SE: 1.53) (p>0.10)122
Nephropathy testObservationalFQHC diabetes subgroup: adjusted year 3 difference in visits: 2.88 (SE: 1.60) (p>0.10)122
All 4 recommended diabetes testsObservationalFQHC diabetes subgroup: adjusted year 3 difference in visits: −0.51 (SE: 0.45) (p>0.10)122
Lipid testObservationalFQHC IVD subgroup: adjusted year 3 difference in visits: −0.81 (SE: 1.80) (p>0.10)122

FQHC = Federally Qualified Health Center; G = group; HbA1c = hemoglobin A1c; IVD = ischemic vascular disease; LDL = low-density lipoprotein; NR = not reported; SE = standard error.

Table B-23Study characteristics for telephonic/mail models

First Author, Year, Site(s)Sample SizeStudy Design (Risk of Bias Assessment)Patient Selection: High Healthcare Use or Cost; Time PeriodPatient Selection: Chronic ConditionsPatient Selection: OtherAdditional Selected Patient Characteristics

McCall et al., 201079

48 counties in western TX

HNHC patients only (N=2,815)

Intervention (N=1,986)

Comparison (N=1,969)

RCT (RoB: some concerns)Medicare costs of $6,000 or more in past 1 yearNAMedicare FFS beneficiaries, majority of visits to TX Tech U Health Sciences Center or Physician Network Services (a PCP)NR

McCall et al., 201081

Suffolk, Nassau, and Queens, NY: 6 nephrology practices

Original sample (N=6,996)

Intervention (N=4,996)

Comparison (N=2,000)

Diabetes subgroup (N=2,165)

ESRD subgroup (N=331)

IVD subgroup (N=2,434)

RCT (RoB: some concerns)Medicare costs of $5,000 or more in past 1 yearChronic kidney disease diagnosis on at least one claim, HCC > 1.7 (high risk for future healthcare utilization), excluded ESRD patientsMedicare FFS beneficiaries with primary residence in the intervention county/regionNR

McCall et al., 201081

Suffolk, Nassau, and Queens, NY: 6 nephrology practices

Refresh sample (N=3,341)

Intervention (N=2,385)

Comparison (N=956)

Diabetes subgroup (N=1,280)

ESRD subgroup (N=97)

IVD subgroup (N=1,508)

RCT (RoB: some concerns)Medicare costs of $5,000 or more in past 1 yearCKD diagnosis as evidence by at least one claim, HCC > 1.7 (high risk for future healthcare utilization), excluded ESRD patientsMedicare FFS beneficiary with primary residence in the intervention county/region, excluded patients institutionalized from March to May 2006 (part of the baseline period)NR

Urato et al., 201382

Sites in NY counties: Nassau, Suffolk, Queens, Kings, Westchester, Richmond, Rockland, and Bronx

Original sample (N=5,889)

Intervention (N=2,945)

Comparison (N=2,944)

Diabetes subgroup (N=2,284)

CKD subgroup (N=3,159)

Intervention

IVD subgroup (N=2,030)

RCT (RoB: some concerns)Medicare costs of $5,000 or more for CKD beneficiaries and $12,000 or more for ESRD beneficiaries in past 1 yearStage 3 CKD diagnosis, as evidence by at least one claimMedicare FFS beneficiaries with primary residence in the intervention county/regionMean HCC score: 2.65

Urato et al., 201382

Sites in NY counties: Nassau, Suffolk, Queens, Kings, Westchester, Richmond, Rockland, and Bronx

Refresh sample (N=4,467)

Intervention (N=2,234)

Comparison (N=2,233)

Diabetes subgroup (N=2,202)

CKD subgroup (N=1,663)

IVD subgroup (N=2,061)

RCT (RoB: some concerns)Medicare costs of $5,000 or more for CKD beneficiaries and $12,000 or more for ESRD beneficiaries in past 1 yearStage 3 CKD diagnosis, as evidence by at least one claimMedicare FFS beneficiaries with primary residence in the intervention county/regionNR

Dally et al., 200290

OH

(N=593)

Intervention (N=297)

Comparison (N=296)

RCT (RoB: some concerns)11+ outpatient visits in 2 years1+ visit with a diagnosis of at least 1 of 3 targeted conditions (arthritis, hypertension, diabetes)Age: 18–64; Kaiser Permanente Ohio patient

McCall et al., 201180

Central OR and central WA: 2 large multispecialty group practices

Original HNHC patient sample (N=125)

Intervention (N=66)

Comparison (N=59)

RCT (RoB: some concerns)Medicare costs of $6,000 or more in 1 yearHF, diabetes, or COPD diagnosis on at least 1 claimMedicare FFS beneficiaries; 2+ visits to the HBC medical practicesNR

McCall et al., 201180

Central OR and central WA: 2 large multispecialty group practices

Refresh HNHC patient sample (N=227)

Intervention (N=120)

Comparison (N=107)

RCT (RoB: some concerns)Medicare costs of $6,000 or more in 1 yearHF, diabetes, or COPD diagnosis on inpatient, outpatient hospital, or physician claims onlyMedicare FFS beneficiaries; 2+ visits to the HBC medical practicesNR

Schickedanz et al., 2019121

Southern CA: 1 health system including 13 medical centers

(N=34,225)

Intervention (N=7,107)

Comparison (N=27,118)

Observational

(RoB: some concerns)

Predicted to be in top 1% of healthcare utilization in health system in the next 1 yearNAAge: 18+; Kaiser Permanente Southern California patient

Nonwhite: 50%

Cancer: 46%

Diabetes: 13%

CAD/CHF: 33%

Asthma: 6%

Charlson Comorbidity score: 7 (3)

Depression: 6%

Lives in low-income census tract: 14%;

Medicare: 60%;

Commercial insurance: 25%

CA = California; CAD = coronary artery disease; CHF = congestive heart failure; CKD = chronic kidney disease; COPD = chronic obstructive pulmonary disease; ESRD = end-stage renal disease; FFS = fee-for-service; HF = heart failure; HBC = Health Buddy Consortium; HCC = hierarchical condition category; HNHC = high-need, high-cost; IVD = ischemic vascular disease; N = number; NA = not applicable; NR = not reported; NY = New York; OH = Ohio; OR = Oregon; PCP = primary care practice; RCT = randomized controlled trial; RoB = risk of bias; TX = Texas; U = University; WA = Washington.

Table B-24Intervention characteristics for telephonic/mail models

First Author, Year, Site(s)Intervention: Brief DescriptionIntervention DurationAssessment, Education, Skills, MonitoringCoordination and Continuity of CareReferral to/Linkages to Community-Based Support ServicesProvidersMode of Delivery Setting(s)IntensityComparison

McCall et al., 201079

48 counties in western TX

TX Senior Trails Program: Care Management for High-Cost Beneficiaries Demonstration: TX Tech University Health Sciences Center Shared savings and care managementNR; up to 16 monthsPatient education and coaching of chronic conditions and self-management skillsFacilitate communication with providers, care plan compliance, hospital discharge planning, medication adherence, access to clinics; sharing information with physiciansCare team drew on community resources to assist with social issuesNurse care managers, nurses for telephone support, social workersTelephone, face-to-face, written: in patient homes, physician offices, or in the hospital, as needed

Mean contacts per beneficiary: 6 (median: 4)

97% of participants had 1+ contacts with a care manager or physician, 50% had 2–4 contacts, and 25% had 5–9 contacts. Written contact was most frequent, face-to-face was least frequent (20% had face-to-face contact with the care manager)

Usual care: comparison group was not contacted

McCall et al., 201081

Suffolk, Nassau, and Queens, NY: 6 nephrology practices

Care Management for High-Cost Beneficiaries Demonstration: VillageHealth I

Shared savings and one-on-one nurse care manager support

Up to 36 months for original sample, up to 24 months for refresh sampleInitial and continuous risk evaluation, telemonitor health failure patients, 24-hour hotline for assistance requests, develop care plan, renal disease educationCoordinated care, referral to nephrologist when reach stage IV CKD, support from pharmacist, medication therapy managementNRCare managers (phone and field RNs), pharmacists, dietitians: telephone support and education materials; social workers: telephone psychosocial support (e.g., insurance, transportation); health service assistants provided admin support for patients and providersTelephone, face-to-face: NROn average, participants were contacted about every 1.4 months or had 13 contacts over 18 months. Nearly all had a telephone or in-person contact during the last 18 months, mostly by telephoneUsual care: comparison group was not contacted

Urato et al., 201382

Sites in NY counties: Nassau, Suffolk, Queens, Kings, Westchester, Richmond, Rockland, and Bronx

Extended Medicare Care Management for High Cost Beneficiaries (CMHCB) Demonstration: VillageHealth II

Shared savings and disease management/case management through a care management organization

Up to 21 months for original sample, up to 11 months for refresh sampleProvided individualized assessment, including risk stratification, and tailored care plans; education related to self-management activities to decrease risk for acute exacerbations of chronic diseasesFacilitated patient relationships with physicians, helped patients comply with physician care plans, hospital discharge planning support, medication managementReferrals or provision for ancillary services (drugs, community services)Nurse care managers, nurses for telephone support, registered dietitian, pharmacist, social workerFace-to-face or telephone contact with nurse care manager, in-person educational classes: in-patient home or over telephone
  • CKD patients provided telephone support only
  • ESRD patients provided phone or in- person support

At least monthly contact with care manager; >50% of beneficiaries did not get a call or in-person meeting with a care manager in the last 15 months

Telephonic contact was the dominant form of contact (about 70%)

Usual care: comparison group was not contacted

Dally et al., 200290

OH

Mailed health promotion program90% remained enrolled for 30 monthsHealth risk appraisal (HRA) questionnaire, personalized letter and report with feedback after each questionnaire with recommendations to reduce the health risks identified by the questionnaire, and condition-specific health education pamphlets and booksNRNRA vendor provided all intervention-related materialsAll materials were mailed to participants’ homesAfter initial HRA questionnaire, 3 additional HRA questionnaires and individualized feedback letters/reports were delivered approximately every 3 monthsControls also received and completed the baseline HRAs, education materials, and an incentive to complete the final questionnaire

McCall et al., 201180

Central OR and central WA: 2 large multispecialty group practices

Care Management for High-Cost Beneficiaries Demonstration: Health Buddy Consortium

Shared savings and Health Buddy® disease management (Health Buddy® device allowed for daily, routine communication with program staff)

Up to 38 months for original population, up to 26 months for refresh populationDaily Health Buddy questionnaire to assess health condition, followup from nurse CM as appropriate13 disease-specific care management programs; triaged and coordinated medical, psychological, or social servicesCoordinated social services, as neededNurse care managers, physiciansTelephone, over Health Buddy deviceNearly all intervention group members who used the device received at least one call from a care manager during the demo and nearly 60% received >20 contacts during this same period. Over 60% of participants never had a device and scheduled regular telephone calls with a nurse care managerUsual care: comparison group was not contacted

Schickedanz et al., 2019121

Southern CA: 1 health system including 13 medical centers

Health Leads Program: social needs screening and navigation interventionNR; up to 14 months (followup time)Social needs screener, intake assessmentNRTailored referral to community-based services (i.e., food banks, housing programs, or other resources to address the identified social need) immediately or during followup callsProgram associates provided screening and navigationTelephoneFollowup calls at minimum every 2 weeks until call were no longer needed or loss to followupUsual care

CA = California; CKD = chronic kidney disease; CM = case manager; CMHCB = Care Management for High Cost Beneficiaries; ESRD = end-stage renal disease; HRA = health risk appraisal; NR = not reported; NY = New York; OH = Ohio; OR = Oregon; RN = registered nurse; TX = Texas; WA = Washington.

Table B-25Healthcare utilization outcomes for telephonic/mail model studies

Utilization MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
Inpatient admissions, all causeRCTOriginal sample DiD: IRR=0.94 (95% CI, 0.82 to 1.07)81
RCTRefresh sample DiD: IRR=0.96 (95% CI, 0.80 to 1.15)81
RCTOriginal sample DiD: IRR=1.06 (95% CI, 0.95 to 1.19)82
RCTRefresh sample DiD: IRR=0.98 (95% CI, 0.86 to 1.11)82
Inpatient admissions, ACSCRCTOriginal sample DiD: IRR=1.05 (95% CI, 0.87 to 1.28)82
RCTRefresh sample DiD: IRR=0.98 (95% CI, 0.80 to 1.20)82
RCTOriginal sample DiD: IRR=0.83 (95% CI, 0.67 to 1.04)81
RCTRefresh sample DiD: IRR=1.02 (95% CI, 0.77 to 1.36)81
Inpatient admissions, any (%)RCTOriginal sample DiD: OR=0.98 (95% CI, 0.82 to 1.18)81
RCT--Refresh sample DiD: OR=0.94 (95% CI, 0.74 to 1.18)81
Inpatient Admissions, ACSC (%)RCTOriginal sample DiD: OR=0.86 (95% CI, 0.69 to 1.08)81
RCTRefresh sample DiD: OR=0.93 (95% CI, 0.70 to 1.25)81
ED visits, all causeRCTOriginal sample DiD: IRR=1.04 (95% CI, 0.91 to 1.19)81
RCTRefresh sample DiD: IRR=1.01 (95% CI, 0.82 to 1.24)81
RCTOriginal sample DiD: IRR=1.03 (95% CI, 0.90 to 1.17)82
RCTRefresh sample DiD: IRR=0.97 (95% CI, 0.85 to 1.10)82
ED visits, ACSCRCTOriginal sample DiD: IRR=1.09 (95% CI, 0.90 to 1.33)82
RCTRefresh sample DiD: IRR=1.00 (95% CI, 0.81 to 1.23)82
RCTOriginal sample DiD: IRR=0.87 (95% CI, 0.71 to 1.08)81
RCTRefresh sample DiD: IRR=1.07 (95% CI, 0.79 to 1.45)81
Outpatient visits at 6 monthsRCTNRNRTotal sample: Poisson coefficient =−0.0328 (p=0.27)90
Outpatient visits at 12 monthsRCTTotal sample: Poisson coefficient =−0.0341 (p=0.11)90
Outpatient visits at 30 monthsRCTNRNRTotal sample: Lower use in G1 than G2: Poisson coefficient =−0.0260 (p=0.04)90
Total Utilization (%)ObservationalNRNRTotal sample DiD:a −2.2 (95% CI, −4.5 to 0.1)121
a

Total utilization includes a count of all visits in emergency department, outpatient, and/or inpatient settings. The subgroups were based on the census tract estimates of patients’ home address. The low-income subgroup included patients who lived in census tracts where a plurality of residents had income below $34,575. The low-education subgroup included patients who lived in census tracts where a plurality of residents had less than a high school education.

⇧ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

⇩ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

ACSC = ambulatory care sensitive conditions; CI = confidence interval; DiD = difference-in-difference; ED = emergency department; G = group; IRR = incidence rate ratio; NR = not reported; OR = odds ratio; RCT = randomized controlled trial.

Table B-26Healthcare utilization outcomes for telephonic/mail model studies: Subgroup outcomes

Utilization MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
Outpatient visits at 6 monthsRCTNRNRArthritis subgroup: Poisson coefficient= −0.0710 (p=0.16)90
RCTNRNRBlood pressure subgroup: Poisson coefficient=0.0838 (p=0.15)90
RCTNRNRDiabetes subgroup: Poisson coefficient= −0.0879 (p=0.07)90
Outpatient visits at 12 monthsRCTArthritis subgroup: Lower use in G1 than G2: Poisson coefficient =−0.1130 (p<0.01)90
RCTBlood pressure subgroup: Higher use in G1 than G2: Poisson coefficient=0.0938 (p=0.02)90
RCTDiabetes subgroup: Poisson coefficient= −0.0651 (p=0.06)90
Outpatient visits at 30 monthsRCTNRNRArthritis subgroup: Lower use in G1 than G2: Poisson coefficient =−0.1004 (p<0.01)90
RCTNRNRBlood pressure subgroup: Poisson coefficient=0.0317 (p=0.21)90
RCTNRNRDiabetes subgroup: Poisson coefficient= −0.0146 (p=0.50)90
Total utilization (%)ObservationalNRNRLow-income area subgroup DiD:a Greater reduction in G1: −7.0 (95% CI −11.9 to −1.9)121
ObservationalNRNRLow-education area subgroup DiD:a Greater reduction in G1: −11.5 (95% CI, −17.6 to −5.0)121
ObservationalNRNRMedicaid insurance subgroup DiD:a Greater reduction in G1: −12.1 (95% CI, −18.1 to −5.6)121

⬆ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

⬇ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

⇧ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

⇩ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

CI = confidence interval; DiD = difference-in differences; G = group; NR = not reported; RCT = randomized controlled trial.

Table B-27Strength of evidence for telephonic/mail models versus usual-care outcomesa

PopulationOutcomeResultsStudy Design and Sample SizeStrength of Evidence DomainsOverall Evidence Strength (Direction of Effect)
HNHC patientsED visits, all cause

VH I: Original sample DiD: IRR=1.04 (95% CI, 0.91 to 1.19)81

VH I: Refresh sample DiD: IRR=1.01 (95% CI, 0.82 to 1.24)81

VH II: Original sample DiD: IRR=1.03 (95% CI, 0.90 to 1.17)82

VH II: Refresh sample DiD: IRR=0.97 (95% CI, 0.85 to 1.10)82

Pooled rate ratio: 1.01 (95% CI, 0.94 to 1.08); 4 RCT samples; I2=0%

4 RCTs, N=20,693Moderate study limitations, inconsistent, imprecise, directLow (No difference)
HNHC patientsED visits, ACSC

VH II: Original sample DiD: IRR=1.09 (95% CI, 0.90 to 1.33)82

VH II: Refresh sample DiD: IRR=1.00 (95% CI, 0.81 to 1.23)82

VH I: Original sample DiD: IRR=0.87 (95% CI, 0.71 to 1.08)81

VH II: Refresh sample DiD: IRR=1.07 (95% CI, 0.79 to 1.45)81

Pooled rate ratio: 0.99 (95% CI, 0.88 to 1.10); 4 RCT samples; I2=0%

4 RCTs, N=20,693Moderate study limitations, inconsistent, imprecise, directLow (No difference)
HNHC patientsInpatient admissions, all cause

VH I: Original sample DiD: IRR=0.94 (95% CI, 0.82 to 1.07)81

VH I: Refresh sample DiD: IRR=0.96 (95% CI, 0.80 to 1.15)81

VH II: Original sample DiD: IRR=1.06 (95% CI, 0.95 to 1.19)82

VH II: Refresh sample DiD: IRR=0.98 (95% CI, 0.86 to 1.11)82

Pooled rate ratio: 0.99 (95% CI, 0.92 to 1.06); 4 RCT samples; I2=0%

4 RCTs, N=20,693Moderate study limitations, inconsistent, imprecise, directLow (No difference)
HNHC patientsInpatient admissions, ACSC

VH II: Original sample DiD: IRR=1.05 (95% CI, 0.87 to 1.28)82

VH II: Refresh sample DiD: IRR=0.98 (95% CI, 0.80 to 1.20)82

VH I: Original sample DiD: IRR=0.83 (95% CI, 0.67 to 1.04)81

VH I: Refresh sample DiD: IRR=1.02 (95% CI, 0.77 to 1.36)81

Pooled rate ratio: 0.95 (95% CI, 0.85 to 1.06); 4 RCT samples; I2=0%

4 RCTs, N=20,693Moderate study limitations, inconsistent, imprecise, directLow (No difference)
HNHC patientsInpatient admissions, any (%)

VH I: Original sample DiD: OR=0.98 (95% CI, 0.82 to 1.18)81

VH I: Refresh sample DiD: OR=0.94 (95% CI, 0.74 to 1.18)81

2 RCTs, N=10,337Moderate study limitations, consistent, imprecise, directInsufficient
HNHC patientsInpatient admissions, ACSC (%)

VH I: Original sample DiD: OR=0.86 (95% CI, 0.69 to 1.08)81

VH I: Refresh sample DiD: OR=0.93 (95% CI, 0.70 to 1.25)81

2 RCTs, N=10,337Moderate study limitations, consistent, imprecise, directInsufficient
HNHC patientsTotal cost

TST: DiD=120 (p>0.05)79

HB: Original sample DiD=−308 (p>0.05)80

HB: Refresh sample DiD=178 (p>0.05)80

VH I: Original sample DiD=−111 (p>0.05)81

VH I: Refresh sample DiD=−142 (p>0.05)81

VH II: Original sample DiD=206 (p>0.05)82

VH II: Refresh sample DiD=−99 (p>0.05)82

Pooled mean difference: −$8.52 (95% CI, −130.02 to 112.98); 7 RCT samples; I2=22.4%

7 RCTs, N=25,000Moderate study limitations, inconsistent, imprecise, directLow (No difference)
HNHC patientsMortality rate

VH I: Original sample diff=0.8 (p=0.51)81

VH I: Refresh sample diff=−1.1 (p=0.49)81

VH II: Original sample diff=0.6 (p=0.61)82

VH II: Refresh sample diff=0.3 (p=0.76)82

Pooled mean difference: 0.34 (95% CI, −1.06 to 1.74); 4 RCT samples; I2=0%

4 RCTs, N=20,693Moderate study limitations, inconsistent, imprecise, directLow (No difference)
HNHC patientsInfluenza vaccine

VH I: Original sample DiD: OR=1.12 (95% CI, 0.93 to 1.34);81

VH I: Refresh sample DiD: OR=0.91 (95% CI, 0.72 to 1.15)81

2 RCTs, N=10,337Moderate study limitations, inconsistent, imprecise, directInsufficient
HNHC patientsProgression to ESRD

VH I: Original sample diff: −0.75 (95% CI, −1.90 to 0.41)81

VH I: Refresh sample diff: 0.91 (95% CI, −2.23 to 0.41)81

2 RCTs, N=10,337Moderate study limitations, inconsistent, imprecise, directInsufficient
HNHC patients CKD subgroupProgression to ESRD

VH II: Original sample diff: Greater in G1: 2.92 (95% CI, 0.30 to 5.54)82

VH II: Refresh sample diff: 0.37 (95% CI, −2.53 to 3.28)82

2 RCTs, N=4,822Moderate study limitations, imprecise, inconsistent, directInsufficient
HNHC patients CKD subgroupGraft or fistula prior to hemodialysis

VH II: Original sample diff: Lower in G1: −3.09 (95% CI, −5.93 to −0.24)82

VH II: Refresh sample diff: −2.05 (95% CI, −6.39 to 2.30)82

2 RCTs, N=4,822Moderate study limitations, consistent, imprecise, directInsufficient
HNHC patients ESRD subgroupGraft or fistula prior to hemodialysis

VH I: Original sample diff: −6.08 (95% CI, −15.75 to 3.59)81

VH I: Refresh sample diff: 2.87 (95% CI, −16.72 to 22.46)81

2 RCTs, N=428Moderate study limitations, inconsistent, imprecise, directInsufficient
HNHC patients Diabetes subgroupHbA1c test

VH II: Original sample DiD: OR=1.22 (95% CI, 0.73 to 2.03)82

VH II: Refresh sample DiD: OR=0.76 (95% CI, 0.47 to 1.23)82

VH I: Original sample DiD: OR=1.02 (95% CI, 0.68 to 1.54)81

VH I: Refresh sample DiD: OR=0.95 (95% CI, 0.56 to 1.61)81

4 RCTs, N=7,931Moderate study limitations, inconsistent, imprecise, directInsufficient
PopulationOutcomeResultsStudy Design and Sample SizeStrength of Evidence DomainsOverall Evidence Strength (Direction of Effect)
HNHC patients Diabetes subgroupLDL test

VH II: Original sample DiD: OR=1.05 (95% CI, 0.70 to 1.58)82

VH II: Refresh sample DiD: OR=1.15 (95% CI, 0.80 to 1.67)82

VH I: Original sample DiD: OR=1.19 (95% CI, 0.84 to 1.68)81

VH I: Refresh sample DiD: OR=0.72 (95% CI, 0.42 to 1.24)81

4 RCTs, N=7,931Moderate study limitations, inconsistent, imprecise, directInsufficient
HNHC patients IVD subgroupLDL test

VH I: Original sample DiD: OR=0.98 (95% CI, 0.72 to 1.35)81

VH II: Refresh sample DiD: OR=0.88 (95% CI, 0.54 to 1.43)81

2 RCTs, N=3,942Moderate study limitations, consistent, imprecise, directInsufficient
HNHC patients Diabetes subgroupEye exam

VH II: Original sample DiD: OR=1.05 (95% CI, 0.81 to 1.137)82

VH II: Refresh sample DiD: OR=1.25 (95% CI, 0.94 to 1.66)82

2 RCTs, N=4,486Moderate study limitations, imprecise, consistent, directInsufficient
HNHC patients Diabetes subgroupNephrology

VH II: Original sample DiD: OR=0.92 (95% CI, 0.71 to 1.18)82

VH II: Refresh sample DiD: OR=1.30 (95% CI, 1.01 to 1.67)82

2 RCTs, N=4,486Moderate study limitations, inconsistent, imprecise, directInsufficient
HNHC patients IVD subgroupLipid panel

VH II: Original sample DiD: OR=1.10 (95% CI, 0.85 to 1.44)82

VH II: Refresh sample DiD: OR=1.22 (95% CI, 0.17 to 0.92)82

2 RCTs, N=4,091Moderate study limitations, consistent, imprecise, directInsufficient
a

Comparison group participants for Dally et al. received baseline education materials and incentives.90

ACSC = ambulatory care sensitive conditions; CI = confidence interval; CKD = chronic kidney disease; DiD = difference-in-difference; diff = difference; ED = emergency department; ESRD = end-stage renal disease; G = group; HB = Health Buddy; HbA1c = hemoglobin A1c; HNHC = high-need, high-cost; IRR = incidence rate ratio; IVD = ischemic vascular disease; LDL = low-density lipoprotein; N = number; OR = odds ratio; RCT = randomized controlled trial; TST = Texas Senior Trails; VH = Village Health; vs. = versus.

Table B-28Cost outcomes for telephonic/mail model studies

Cost MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
Total costRCTDiD=120 (SE:99.6) (p>0.05)79
RCTOriginal sample DiD=−308 (SE: 311.2) (p>0.05)80
RCTRefresh sample DiD=178 (SE: 257.5) (p>0.05)80
RCTOriginal sample DiD=−111 (SE: 83.2) (p>0.05)81
RCTRefresh sample DiD=−142.1 (SE: 138.7) (p>0.05)81
RCTOriginal sample DiD=206 (SE: 152.2) (p>0.05)82
RCTRefresh sample DiD=−99 (SE: 206.6) (p>0.05)82

⇧ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

⇩ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

DiD = difference-in-difference;; G = group; RCT = randomized controlled trial; SE = standard error.

Table B-29Clinical and functional outcomes for telephonic/mail model studies

Clinical and Functional OutcomesStudy DesignDifference Between Intervention Group (G1) and Comparison Group (G2)
Mortality rateRCTOriginal sample difference=0.8 (p=0.51)81
RCTRefresh sample difference=−1.1 (p=0.49)81
RCTOriginal sample difference=0.6 (p=0.61)82
RCTRefresh sample difference=0.3 (p=0.76)82
Influenza vaccineRCTOriginal sample DiD: OR=1.12 (95% CI, 0.93 to 1.34)81
RCTRefresh sample DiD: OR=0.91 (95% CI, 0.72 to 1.15)81
Progression to ESRDRCTOriginal sample difference=−0.75 (95% CI, −1.90 to 0.41)81
RCTRefresh sample difference=−0.91 (95% CI, −2.23 to 0.41)81
PHC score (physical health)RCTANCOVA-adjusted IE=−0.1 (p>0.05)81
MHC score (mental health)RCTANCOVA-adjusted IE=0.0 (p>0.05)81
PHQ-2 (depression, 0 to 6)RCTLower score in G1 than G2:a ANCOVA-adjusted IE=−0.45 (p<0.05)81
Number of ADLs difficult to do (0 to 6)RCTANCOVA-adjusted intervention effect=−0.02 (p>0.05)81
Number of ADLs receiving help (0 to 6)RCTANCOVA-adjusted IE=0.21 (p>0.05)81
Helping to cope with a chronic condition (1 to 5)RCTANCOVA-adjusted IE=0.10, (p>0.05)81
Number of helpful discussion topics (0 to 5)RCTANCOVA-adjusted IE=0.08 (p>0.05)81
Discussing treatment choices (1 to 4)RCTLower score in G1 than G2:b ANCOVA-adjusted IE=−0.19 (p<0.05)81
Communicating with providers (0 to 100)RCTANCOVA-adjusted IE=2.7 (p>0.05)81
Getting answers to questions quickly (0 to 100)RCTANCOVA-adjusted IE=−0.8 (p>0.05)81
Multimorbidity Hassles score (0 to 24)RCTANCOVA-adjusted IE=0.15 (p>0.05)81
Percent receiving help setting goalsRCTANCOVA-adjusted IE=9.5 (p>0.05)81
Percent receiving help making a care planRCTANCOVA-adjusted IE=4.0 (p>0.05)81
Self-efficacy: Take all medications (1 to 5)RCTANCOVA-adjusted IE=0.03 (p>0.05)81
Self-efficacy: Plan meals and snacks (1 to 5)RCTANCOVA-adjusted IE=−0.08 (p>0.05)81
Self-efficacy: Exercise 2 or 3 times weekly (1 to 5)RCTANCOVA-adjusted IE=0.14 (p>0.05)81
Self-care activities: Prescribed medications taken (mean # of days)RCTANCOVA-adjusted IE=−0.15 (p>0.05)81
Self-care activities: Followed healthy eating plan (mean # of days)RCTANCOVA-adjusted IE=−0.03 (p>0.05)81
Self-care activities: 30 minutes of continuous physical activity (mean # of days)RCTANCOVA-adjusted IE=−0.30 (p>0.05)81
a

Lower scores in the PHQ-2 indicate fewer depressive symptoms.

b

Lower scores indicate worse experience, satisfaction, or self-management.

ADL = activities of daily living; ANCOVA = analysis of covariance; CI = confidence interval; DiD = difference-in-difference; ESRD = end-stage renal disease; G = group; IE = intervention effect; MHC = mental health composite; OR = odds ratio; PHC = physical health composite; PHQ-2 = patient health questionnaire-2; RCT = randomized controlled trial.

Table B-30Clinical and functional outcomes for telephonic/mail model studies: Subgroup outcomes

Clinical and Functional OutcomesStudy DesignDifference Between Intervention Group (G1) and Comparison Group (G2)
Progression to ESRDRCTChronic kidney disease patients subgroup: Original sample difference: Greater in G1: 2.92 (95% CI, 0.30 to 5.54)82
RCTChronic kidney disease patients subgroup: Refresh sample difference: 0.37 (95% CI, −2.53 to 3.28)82
Graft or fistula prior to hemodialysisRCTChronic kidney disease patients subgroup: Original sample difference: Lower in G1: −3.09 (95% CI, −5.93 to −0.24)82
RCTChronic kidney disease patients subgroup: Refresh sample difference: −2.05 (95% CI, −6.39 to 2.30)82
RCTEnd-stage renal disease patients subgroup: Original sample difference: −6.08 (95% CI, −15.75 to 3.59)81
RCTEnd-stage renal disease patients subgroup: Refresh sample difference: 2.87 (95% CI, −16.72 to 22.46)81
HbA1c testRCTDiabetes patients subgroup: Original sample DiD: OR=1.22 (95% CI, 0.73 to 2.03)82
RCTDiabetes patients subgroup: Refresh sample DiD: OR=0.76 (95% CI, 0.47 to 1.23)82
RCTDiabetes patients subgroup: Original sample DiD: OR=1.02 (95% CI, 0.68 to 1.54)81
RCTDiabetes patients subgroup: Refresh sample DiD: OR=0.95 (95% CI, 0.56 to 1.61)81
LDL-C testRCTDiabetes patients subgroup: Original sample DiD: OR=1.05 (95% CI, 0.70 to 1.58)82
RCTDiabetes patients subgroup: Refresh sample DiD: OR=1.15 (95% CI, 0.80 to 1.67)82
RCTDiabetes patients subgroup: Original sample DiD: OR=1.19 (95% CI, 0.84 to 1.68)81
RCTDiabetes patients subgroup: Refresh sample DiD: OR=0.72 (95% CI, 0.42 to 1.24)81
RCTIVD patient subgroup: Original sample DiD: OR=0.98 (95% CI, 0.72 to 1.35)81
RCTIVD patient subgroup: Refresh sample DiD: OR=0.88 (95% CI, 0.54 to 1.43)81
Eye examRCTDiabetes patients subgroup: Original sample DiD: OR=1.05 (95% CI, 0.81 to 1.137)82
RCTDiabetes patients subgroup: Refresh sample DiD: OR=1.25 (95% CI, 0.94 to 1.66)82
NephrologyRCTDiabetes patients subgroup: Original sample DiD: OR=0.92 (95% CI, 0.71 to 1.18)82
RCTDiabetes patients subgroup: Refresh sample DiD: Greater increase in G1 than G2: OR=1.30 (95% CI, 1.01 to 1.67)82
All 4 measuresaRCTDiabetes patients subgroup: Original sample DiD: OR=1.01 (95% CI, 0.80 to 1.29)82
RCTDiabetes patients subgroup: Refresh sample DiD: OR=1.18 (95% CI, 0.93 to 1.52)82
None of the measuresbRCTDiabetes patients subgroup: Original sample DiD: OR=1.74 (95% CI, 0.35 to 8.70)82
RCTDiabetes patients subgroup: Refresh sample DiD: OR=0.60 (95% CI, 0.16 to 2.33)82
Lipid panelRCTIVD patient subgroup: Original sample DiD: OR=1.10 (95% CI, 0.85 to 1.44)82
RCTIVD patient subgroup: Refresh sample DiD: greater increase in G1 than G2: OR=1.22 (95% CI, 0.17 to 0.92)82
a

The “All 4 measures” is the rate at which beneficiaries receive all of the following four diabetes measures: rate of annual HbA1c testing, low-density lipoprotein cholesterol (LDL-C) screening, receipt of a retinal eye exam, and medical attention for nephropathy.

b

The “None of the measures” is the rate at which beneficiaries did not receive any of the four diabetes measures.

CI = confidence interval; DiD = difference-in-difference; ESRD = end-stage renal disease; G = group; HbA1c = hemoglobin A1c; IVD = ischemic vascular disease; LDL-C = low-density lipoprotein cholesterol; OR = odds ratio; RCT = randomized controlled trial.

Table B-31Study characteristics for community-based models

First Author, Year, Site(s)Sample SizeStudy Design (Risk of Bias Assessment)Patient Selection: High Healthcare Use or Cost; Time PeriodPatient Selection: Chronic ConditionsPatient Selection: OtherAdditional Selected Patient Characteristics

Finkelstein et al., 202096

Camden, NJ; 4 local-area hospital systems

(N=800)

Intervention (N=399)

Comparison (N=401)

RCT (RoB: Low)1 or more IP admissions in past 6 months2 or more chronic conditions

At least 2 of the following traits or conditions constituting medically and socially complex needs: use of 5 or more active outpatient medications, difficulty accessing services, lack of social support, a coexisting MH condition, an active drug habit, and/or homelessness; 18–80 years of age

Exclusion criteria: uninsured, cognitively impaired, oncology patient, admitted for a surgical procedure for an acute health problem, for mental healthcare (with no comorbid physical health conditions), or for complications of a progressive chronic disease that has limited treatments

≥65 years: 28%

Nonwhite: 85%

Medicare: 48%

Medicaid: 45%

Hypertension: 80.6%; CHF: 33.9%; diabetes: 53.1%

Needs help with mobility: 62%

Self-reported health: poor: 54%

Depression: 30%

Substance abuse: 44%

Raven et al., 2020109

Santa Clara, CA: 1 site

(N=423)

Intervention (N=199)

Comparison (N=224)

RCT (ROB: some concerns)Various combinations of the ED and psychiatric ED, medical and psychiatric inpatient stays in the county-funded public hospital, and/or jail in past 1–2 years at high enough levels to meet a threshold score (threshold score NR)NRMet the Federal definition of chronic homelessness (homeless for 1+ years or 4+ episodes in the prior 3 years that last for more than a year total, with a disabling condition), lived in Santa Clara County, not incarcerated, not engaged in another intensive case management program or other permanent supportive housing program, did not require nursing home level care, and did not have metastatic cancer or qualify for hospice care

Mean age: 51.5

Nonwhite: 35%

Medicaid: 35%

Medicare: 27%

Bell et al., 2015112

King County, Washington: 1 site

(N=1,120)

Intervention (N=557)

Comparison (N=563)

RCT (ROB: low)Determined to be at risk for high future healthcare costsDisabled with mental health and/or substance abuse problemsMedicaid beneficiaries

Mean age: 51

Nonwhite: 43%

Medicaid: 100%

Medicare: NR

Serious mental illness: 50%

Sevak et al., 201885, 124

Aurora, CO; San Diego, CA; Allentown, PA; Kansas City, MO: 1 site per location

(N=1,279)

Intervention (N=149)

Comparison (N=1,130)

Observational study (RoB: some concerns)

Initial inclusion criteria: 2+ inpatient admissions in 6 months

2 of 4 sites amended criteria: 1 site expanded criteria to also include 3+ inpatient admissions in past 12 months, 1 site expanded criteria to 3+ hospital events (admissions or ED visits) in past 6 months

NA

Medicare FFS beneficiaries

Exclusion: patients whose conditions could not be managed with existing program resources

Mean age: 59 years

Black: 48.4%

Hispanic: 8.5%

Zip code poverty rate: 25.6%

Dual coverage at enrollment: 69%

Alzheimer’s: 8.5%

Cancer: 5.3%

CHF: 56.0%

CKD: 63.0%

COPD: 56.3%

Diabetes: 71.1%

Capp et al., 201788

Colorado: 1 site

(N=3,802)

Intervention (N=406)

Comparison (N=3,396)

Observational study (RoB: some concerns)2+ ED visits or inpatient admissions in past 180 daysNA

Age: 18+ years

Exclusion criteria: pregnant, primary diagnosis of substance use disorder, active malignancy, or ESRD; have a caregiver or power of attorney who was making primary decisions; psychiatric hospitalization in the previous 180 days; undergone major surgery in the past month

Nonwhite: 54%

≥56 years: 15%

Medicaid or Colorado Indigent Care Program: 69%

Medicare: 15%

Self-pay: 12%

CKD: 3%

Congestive heart failure: 5%

COPD: 7%

Diabetes: 19%

Hypertension: 38%

Mental health comorbidity: 43%

Weerahandi et al., 2015119

NYC: 1 hospital

(N=1,158)

Intervention (N=579)

Comparison (N=579)

Observational study (RoB: some concerns)At time of index hospitalization: 1 admission in past 30 days or 2 in past 6 monthsNA≥18 years of age, excluded determined to be too medically acute to benefit from behavioral intervention

Hypertension: 69%

Diabetes without chronic complication: 25%

Chronic pulmonary disease: 49%

Congestive heart failure: 23%

Diabetes w/chronic complication: 8%

Depression: 10.5%

Drug abuse: 6%

Psychoses: 3%

Alcohol abuse: 3%

Nonwhite: 77%

Shah et al., 2011107

Kern County, CA: 3 sites

(N=258)

Intervention (N=98)

Comparison (N=160)

Observational study (RoB: High)4+ ED visit or IP admissions OR 3+ IP admissions OR 2+ IP admissions and 1 ED visit in past 1 yearNAAge: 18–64 years, below 200% FPL, uninsured, not eligible for any public insurance programs

Charlson comorbidity index: 1.14

nonwhite: 51%

DeHaven et al., 2012126, 128

Dallas, TX: 1 study site

(N=574)

Intervention (N=265)

Comparison (N=309)

Observational study (RoB: high)Exceeded average ED visit rate of 1.5 ED visits in past 12 monthsNABelow 200% FPL, uninsured, not eligible for health insurance through local hospital system, not receiving Medicaid or MedicareNonwhite: 89.5%

Thompson et al., 201895

Memphis, TN: 1 site

(N=439)

Intervention (N=159)

Comparison (N=280)

Observational study (RoB: high)11+ hospital encounters originating in the ED in past 1 yearNAResident of Memphis, TN, in 38109 zip code

Moderate or severe Charlson comorbidity index (>3): 13%

Nonwhite: 97%

None/missing PCP: 21%

CA = California; CHF = congestive heart failure; CKD = chronic kidney disease; CO = Colorado; COPD = chronic obstructive pulmonary disease; ED = emergency department; ESRD = end-stage renal disease; FFS = fee-for-service; FPL = federal poverty line; IP = inpatient; MH = mental health; MO = Missouri; N = number; NA = not applicable; NJ = New Jersey; NR = not reported; NYC = New York City; PA = Pennsylvania; PCP = primary care provider; RCT = randomized controlled trial; RoB = risk of bias; TN = Tennessee; TX = Texas.

Table B-32Intervention characteristics for community-based models

First Author, Year, Site(s)Intervention: Brief DescriptionIntervention DurationAssessment, Education, Skills, MonitoringCoordination and Continuity of CareReferral to/Linkages to Community-Based Support ServicesProvidersMode of Delivery Setting(s)IntensityComparison

Finkelstein et al., 202096

Camden, NJ; 4 local-area hospital systems

Camden Core Model: hotspottingMedian: 92 days; tailored to patient needs and responsivenessDisease-specific self-care coaching, patient educationScheduled and accompanied patients to initial post-IP primary and specialty care visits and followup care, medication reconciliation, medication managementAssistance applying for social services and BH programsMultidisciplinary: RNs, social workers, LPNs, CHWs, and health coaches

Face-to-face

Enrollment in the hospital; subsequent service delivery through home visits, PCP, and specialty care

Received both a home visit within 14 days and provider visit within 60 days: 75% of participants; further contact tailored to individualUsual post-discharge care that could include home visits or other outreach

Raven et al., 2020109

Santa Clara, CA: 1 site

Project Welcome Home (PWH), a permanent supportive housing intervention with intensive case management servicesUp to 4 yearsVoluntary support services included mental health and substance use services; medication support, community living skills, educational and vocational support, and money managementCommunity-based case management servicesConnected participants to temporary housing, permanent supportive housing, and rehousingMaster’s level social behavioral health providers, bachelor’s level case managers, and staff with lived experience (peers)NRNRUsual care

Bell et al., 2015112

King County, Washington: 1 site

Kings County Care Partners (KCCP) Program, a registered nurse-led care management interventionUp to 24 monthsCompleted a comprehensive in-person assessment of their medical and social needs with their RN care manager and frequent in-person and phone monitoringCare manager arranged to join the participant at 1+ clinic appointments, provided participants with chronic disease self-management coaching, and coordinated care across the medical and mental health systemsCare manager connected participants to community resourcesThree full-time RNs, two social workers (MSWs) with drug/alcohol treatment training, and a bachelor’s-level chemical dependency counselorMixture of face-to-face, telephone, and letterNRUsual care

Sevak et al., 201885, 124

Aurora, CO; San Diego, CA; Allentown, PA; Kansas City, MO: 1 site per location

Health Care Innovation Award: Rutgers Center for State Health Policy (CSHP) community-based care management/care coordination for high-risk patients, replication of Camden Coalition modelMean: 4.2 months (site means ranged from 2.4 months to 6.3 months)

Sites received technical assistance to implement the intervention

Pt education about the importance of using primary and specialty care instead of, or as a followup to, emergency and hospital care and about managing medical and social needs

Developed individualized care plans, integrated care management services through mobile care teamsAssisted in enrollment in social service and BH service programsDiffering combination of RNs, NPs, social workers, CHWs, peer health coaches, medical assistants, and BH providersFace-to-face, telephone calls to physicians or other service providers care teams met with participants in their homes or in other community locations (e.g., library)Mean staff contacts=10.3 per participant per month, almost 6 hours per participant per monthUsual care

Capp et al., 201788

Colorado: 1 site

Bridges to Care (B2C), an ED-initiated, multidisciplinary, community-based program that provides intensive medical, behavioral health, and social care coordination services following an ED visit or hospital discharge60 daysDepression screening, behavioral health screening, helping patient learn empowerment skills and using “teach back” opportunities to ensure the patient understands results and next stepsPatient services include coordinating primary and specialty careCare plan and associated patient services can include assistance with obtaining housing resources, insurance or disability benefits, refugee services, and access to transportation, and filling prescriptionsPrimary care provider, care coordinator, health coach, behavioral health evaluator, and community health workerFace-to-faceFirst home visit occurs 24–72 hours post-enrollment date; second visit is conducted by PCP within 1 week of ED visit/discharge; third and fourth visits are conducted within 30 days of enrollment. Fifth and sixth visits depend on patient specific needs. Final two visits help patient transition out of program.Usual care

Weerahandi et al., 2015119

NYC: 1 hospital

Preventable Admissions Care Team (PACT) program: social work transition of care.35 daysComprehensive psychosocial assessment during hospitalizationFacilitated communication with PCP and specialists; collaborated with caregivers; scheduled PCP appointment within 10 days of dischargeNRMSWs with experience and training working with at-risk, vulnerable populationsPhone; face-to-face: home visits and while attending appointmentsNRUsual care: social worker assistance only during IP

Shah et al., 2011107

Kern County, CA: 3 sites

Care Management Program (CMP) for low-income high utilizers of hospital servicesNRAssisted in goal creation/reaching goalsCare navigation (schedule appts, referral follow up, refill meds), care transitions (assistance in IP and discharge), and communication with providers (accompany to appointments and followup).Arranged for social services (connect with agency staff, referrals)Care managers with experience as case workers or medical office assistantsFace-to-face: at appoint-ments, patients’ homes, or resource centersMet at least monthlyUsual care

DeHaven et al., 2012128

Dallas, TX: 1 study site

Project Access Dallas (PAD): community faith-health partnership to improve access to care and preventive services to the uninsured, care coordination

Participants received $750 a year in pharmacy benefits and were eligible for laboratory tests, ancillary procedures, and IP hospital care. CCC pts: more complex and chronic problems, assigned a CHW Self-care pts: less serious illnesses, access to a telephone help line for medical care questions and CHW services on request

Up to 1 yearIntake interview and HRA to assign to CCC or self-care CCC pts: CHWs developed care coordination plan, taught health education and self-sufficiencyCCC pts: identified and addressed social concerns, identified patients with or at risk of developing type 2 diabetes, identified patients with depression, provided ED and referrals for cancer screening, provided care coordination and other support services (e.g., transportation, translation)CCW link patients to other service organizationsHRA: community-clinic or hospital-based coordinator Services: CHWs, volunteer PCPs, and specialists

Face-to-face for CCC pts; phone for self-care pts: in primary care and specialty care offices

Volunteer PCPs and specialists met with CHWs monthly to discuss patients

CCC pts: at least monthly self-care: as neededUsual care

Thompson et al., 201895

Memphis, TN: 1 site

Familiar Faces Program Community navigators (blend of CHWs and patient navigators) to bridge gap between patients and the healthcare and social systemsUp to 1 yearCreated a plan for patients’ health behaviors, tailored health information to client needs, and motivated them to make healthy choicesHelped to identify and eliminate barriers to health, coordinated careConnecting patients to health and social resources in their communityCommunity navigators employed in the hospital system and received the trainingInitial engagement was face-to-face at the ED/hospital. The mode of delivery of other services was not reportedNRUsual care

B2C = Bridges to Care; BH = behavioral health; CA = California; CCC = community care coordination; CCW = community care worker; CHW = community health worker; CMP = Care Management Program; CO = Colorado; CSHP = Rutgers Centers for State Health Policy; ED = emergency department; HRA = health risk appraisal; IP = inpatient; KCCP = King County Care Partners; LPN = licensed practical nurse; MO = Missouri; MSW = master of social worker; NJ = New Jersey; NP = nurse practitioner; NR = not reported; NYC = New York City; PA = Pennsylvania; PACT = Preventable Admissions Care Team; PAD = Project Access Dallas; PCP = primary care provider; Pt = patient; RN = registered nurse; TN = Tennessee; TX = Texas.

Table B-33Healthcare utilization outcomes for community-based model studies

Utilization MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
180-day readmissions, countRCTNRNRaBGD=0.01 (95% CI, −0.25 to 0.27)96
ObservationalNRNRG1 vs. G2: p=0.32119
180-day readmissions, any (%)RCTNRNRaBGD=0.82 (95% CI, −5.97 to 7.61)96
180-day readmissions, ≥2 (%)RCTNRNRaBGD=0.27 (95% CI, −6.22 to 6.77)96
ED visits, all causeaRCTNRNRIRR=0.85 (95% CI, 0.67 to 1.08) (p>0.05)109
RCTNRNRDiD=−0.3 (95% CI, −5.0 to 4.5) (p=0.91)112
ObservationalNRNRDiD=0.057 (90% CI, −0.194 to 0.309) (p=0.704)85, 124
ObservationalNRGreater reduction in G1: RR=0.6748 (p<0.0001)107
ObservationalNRNRGreater reduction in G1: NR (p<0.01)126, 128
ObservationalNRNRGreater reduction in G1 than G2: DiD=−1.623 (p<0.01)88
ED visits at 270 days, all causeObservationalNRNRGreater reduction in G1 than G2: DiD=−1.091 (p<0.01)88
ED visits at 180 days, all causeObservationalGreater reduction in G1 than G2: DiD=−1.005 (p<0.01)88
ED visit, anyRCTNRNRDiD=0.96 (95% CI, 0.67 to 1.38) (p=0.82)112
Emergency psychiatric visitsRCTNRNRGreater reduction in G1 than G2: IRR=0.62 (95% CI, 0.43 to 0.91) (p<0.05)109
Inpatient admissions, all causeaRCTNRNRIRR=0.97 (95% CI, 0.70 to 1.35) (p>0.05)109
RCTNRNRDiD=0.3 (95% CI, −1.2 to 1.9) (p=0.68)112
ObservationalNRNRDiD: −0.116 (90% CI, −0.252 to 0.020)85, 124
ObservationalNRRR: 0.8070 (p=0.3771)107
ObservationalNRNRGreater reduction in G1 than G2: DiD=−0.906 (p<0.01)88
Inpatient admissions at 270 days, all causeObservationalNRNRGreater reduction in G1 than G2: DiD=−0.438 (p<0.01)88
Inpatient admissions at 180 days, all causeObservationalDiD=−0.159 (p<0.1)88
Inpatient admissions, anyRCTNRNRDiD=0.92 (95% CI, 0.66 to 1.27) (p=0.60)112
Inpatient admissions, ACSCObservationalNRNRDiD=−0.027 (90% CI, −0.081 to 0.028)85, 124
Inpatient psychiatric staysRCTNRNRIRR=0.73 (95% CI, 0.36 to 1.45) (p>0.05)109
Inpatient daysRCTNRNRaBGD=−0.32 (95% CI, −2.17 to 1.53)96
RCTNRNRIRR=1.12 (95% CI, 0.79 to 1.59) (p>0.05)109
ObservationalNRRR: NR (p=NS)107
ObservationalNRNRGreater reduction in G1: NR (p<0.05)126, 128
ObservationalGreater reduction in G1 than G2: DiD: −8 (95% CI, −14 to −2)95
Total hospital encountersObservationalGreater reduction in G1 than G2: DiD: −13 (95% CI, −19 to −6)95
Hospital encounter resulted in discharge to hospital or observation stayObservationalGreater reduction in G1 than G2: DiD: −12 (95% CI, −19 to −5)95
Hospital encounter resulted in discharge from EDObservationalGreater reduction in G1 than G2: DiD: −12 (95% CI, −19 to −4)95
Outpatient visitRCTNRNRDiD=−0.11 (95% CI, −0.51 to 0.28) (p=0.58)112
Outpatient substance use treatment visitsRCTNRNRIRR=0.76 (95% CI, 0.46 to 1.24) (p>0.05)109
Outpatient mental health visitsRCTNRNRGreater increase in G1 than G2: IRR=1.84 (95% CI, 1.43 to 2.37) (p<0.01)109
Outpatient mental health visit, anyRCTNRNRGreater increase in G1 than G2: DiD=1.30 (95% CI, 1.07 to 1.58) (p<0.01)112
Primary care visits at 360 days, all causeObservationalNRNRGreater increase in G1 than G2: DiD=1.932 (p<0.01)88
Primary care visits at 270 days, all causeObservationalNRNRGreater increase in G1 than G2: DiD=1.517 (p<0.01)88
Primary care visits at 180 daysObservationalGreater increase in G1 than G2: DiD=1.218 (p<0.01)88
Prescription drugs, anyRCTNRNRDiD=1.99 (95% CI, 0.29 to 13.70) (p=0.49)112
Long-term care, anyRCTNRNRDiD=1.09 (95% CI, 0.88 to 1.35) (p=0.42)112
a

Followup time for the outcomes was 12 months for CHSP,85, 124 CMP,107 and PAD126, 128 and 360 days for B2C.88

⬆ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

⬇ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

⇩ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

aBGD = adjusted between-group difference; ACSC = ambulatory care sensitive conditions; CI = confidence interval; DiD = difference-in-difference; ED = emergency department; G = group; IRR = incidence rate ratio; NR = not reported; NS = not statistically significant; RCT = randomized controlled trial; RR = relative risk.

Table B-34Healthcare utilization outcomes for community-based model studies: Subgroup outcomes

Utilization MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
ED visits at 180 days, all causeObservationalPatients with a mental health diagnosis subgroup: Greater reduction in G1 than G2: DiD=−1.377 (p<0.01)88
Inpatient admissions at 180 days, all causeObservationalPatients with a mental health diagnosis subgroup: Greater reduction in G1 than G2: DiD=−0.417 (p<0.01)88
Primary care visits at 180 daysObservationalPatients with a mental health diagnosis subgroup: Greater increase in G1 than G2: DiD=1.404 (p<0.01)88

⬆ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

⬇ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

DiD = difference-in-difference; ED = emergency department; G = group.

Table B-35Strength of evidence for community-based models versus usual-care outcomes

PopulationOutcomeResultsStudy Design and Sample SizeStrength of Evidence DomainsOverall Evidence Strength (Direction of Effect)
HNHC patients180-day readmissions

Camden RCT: aBGD=0.01 (95% CI, −0.25 to 0.27);96

PACT: RR: NR (p=0.32)119

1 RCT, N=800

1 OBS, N=1,158

Moderate study limitations, consistency unknown, imprecise, directInsufficient
HNHC patientsED visits, all cause

PWH: IRR=0.85 (95% CI, 0.67 to 1.08)109

KCCP: DiD=−0.3 (95% CI, −5.0 to 4.5)112

CSHP DiD: 0.057 (90% CI, −0.194 to 0.309) (p=0.704)85, 124

B2C: Greater reduction in G1: DiD=−1.623 (p<0.01)88

CMP: Greater reduction in G1: RR=0.6748 (p<0.0001)107

PAD: Greater reduction in G1: NR (p<0.01)126, 128

2 RCTs, N=1,543

4 OBSs, N=5,913

High study limitations (two high RoB OBS studies),107, 126, 128 inconsistent, imprecise, directInsufficient (Mixed findings)
HNHC patientsInpatient admissions, all cause

CSHP DiD: −0.116 (90% CI, −0.252 to 0.020)85, 124

CMP: RR: 0.8070 (p=0.38)107

B2C: Greater reduction in G1: DiD=−0.906 (p<0.01)88

3 OBSs, N=5,339Moderate study limitations (one high RoB OBS studies),107 consistent, imprecise, directInsufficient
HNHC patientsInpatient days

Camden RCT: aBGD=−0.32 (95% CI, −2.17 to 1.53)96

PWH: IRR=1.12 (95% CI, 0.79 to 1.59)109

CMP: RR: NR (p=NS)107

PAD: Greater reduction in G1: NR (p<0.05)126, 128

Familiar Faces: Greater reduction in G1 than G2: DiD: −8% (95% CI, −14% to −2%)95

2 RCTs, N=1,223 3 OBSs, N=1,271High study limitations (three high RoB studies),95, 107, 126, 128 inconsistent, imprecise, directInsufficient (Mixed findings)
HNHC patientsTotal costs

KCCP: DiD=51 (95% CI, −242 to 344)112

CSHP DiD: −1405 (90% CI, −3509 to 700) (p=0.268);85, 124

Familiar Faces: DiD: −4903 (95% CI, −$13,579 to $3774)95

1 RCT, N=1,120

2 OBSs, N=1,718

High study limitations (one high RoB OBS studies),95 inconsistent, imprecise, directInsufficient
HNHC patientsInpatient costs

KCCP: DiD=−12 (95% CI, −260 to 236)112

CSHP DiD=−120 (90% CI, −1891 to 1652)85, 124

1 RCT, N=1,120

1 OBS, N=1,279

Moderate study limitations, consistent, imprecise, directInsufficient
HNHC patientsED costs

KCCP: DiD=−8 (95% CI, −26 to 10)112

PAD: Lower costs in G1: NR (p=0.01)126, 128

1 RCT, N=1,120

1 OBS, N=574

High study limitations (one high RoB OBS studies),126, 128, consistent, imprecise, directInsufficient
HNHC patientsMortality

Camden RCT: adjusted difference=−1.17 (95% CI, −5.25 to 2.91)96

PWH: Difference=3.9% (P NR)109

KCCP: aOR=0.65 (95% CI, 0.39 to 1.09)112

3 RCTs, N=2,343Moderate study limitations, consistent, imprecise, directLow (No difference)

aBGD = adjusted between-group difference; aOR = adjusted odds ratio; B2C: Bridges to Care; CI = confidence interval; CMP = Care Management Program; CSHP = Rutgers Center for State Health Policy; DiD = difference-in-difference; ED = emergency department; G = group; HNHC = high-need, high-cost; IRR = incidence rate ratio; KCCP = King County Care Partners; N = number; NR = not reported; NS = not statistically significant; OBS = observational study; PAD = Project Access Dallas; PWH = Project Welcome Home; RCT = randomized controlled trial; RoB = risk of bias; RR = rate ratio; vs. = versus.

Table B-36Cost outcomes for community-based model studies

Cost MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
Hospital chargesRCTNRNRaBGD=3,722 (95% CI, −23,438 to 30,882)96
Hospital payments receivedRCTNRNRaBGD=680 (95% CI, −3,415 to 4,775)96
Total costsRCTNRNRDiD=51 (95% CI, −242 to 344) (p=0.73)112
ObservationalNRNRDiD: −1,405 (90% CI, −3,509 to 700) (p=0.268)85, 124
ObservationalDiD: −4,903 (95% CI, −$13,579 to $3774) 95
Inpatient costsRCTNRNRDiD=−12 (95% CI, −260 to 236) (p=0.92)112
ObservationalNRNRDiD: −120 (90% CI, −1,891 to 1,652) (p=0.911)85, 124
ED costsRCTNRNRDiD=−8 (95% CI, −26 to 10) (p=0.38)112
ObservationalNRNRLower costs in G1:a NR (p=0.01)126, 128
Indirect ED costsObservationalNRNRLower costs in G1:b NR (p=0.03)126, 128
Outpatient costsRCTNRNRDiD=31 (95% CI, −47 to 109) (p=0.43)112
Prescription drug costsRCTNRNRGreater increase in G1 than G2: DiD=74 (95% CI, 3 to 145) (p=0.048)112
Long-term care costsRCTNRNRDiD=36 (95% CI, −35 to 107) (p=0.33)112
a

Direct ED costs include costs associated with the delivery of care during an ED visit or inpatient admission in the 12 months following enrollment.

b

Indirect ED costs are the fixed costs related to building, maintenance, staffing, and utilities in the 12 months following enrollment.

⇩ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

aBGD = adjusted between-group difference; CI = confidence interval; DiD = difference-in-difference; ED = emergency department; G = group; NR = not reported; RCT = randomized controlled trial.

Table B-37Clinical and functional outcomes for community-based model studies

Clinical and Functional OutcomesStudy DesignDifference Between Intervention Group (G1) and Comparison Group (G2)
MortalityRCTDifference=−1.17 (95% CI, −5.25 to 2.91)96
RCTG1: 18.6% vs. G2: 14.7%, p=NR109
RCTaOR=0.65 (95% CI, 0.39 to 1.09) (p=0.10)112

aOR = adjusted odds ratio; CI = confidence interval; G = group; NR = not reported; RCT = randomized controlled trial.

Table B-38Social risk outcomes for community-based model studies

Social Risk OutcomesStudy DesignDifference
Participation in supplemental nutrition assistance program (%)RCTAD=4.59 (95% CI, 0.52 to 8.65)96
Receipt of temporary assistance for needy families (%)RCTAD=0.69 (95% CI, −0.34 to 1.71)96
Receipt of general assistance (%)RCTAD=0.68 (95% CI, −1.82 to 3.18)96
Ever housedRCTGreater increase in G1 than G2: IRR=1.84 (95% CI, 1.43 to 2.37) (p<0.01)109
Any homeless monthsRCTDiD=0.83 (95% CI, 0.60 to 1.17) (p=0.29)112
Homeless months (mean per 1,000 months)RCTDiD=−1.5 (95% CI, −4.3 to 1.3) (p=0.29)112
Jail staysRCTIRR=1.01 (95% CI, 0.73 to 1.40) (p>0.05)109
Any criminal convictionsRCTDiD=1.95 (95% CI, 1.10 to 3.44) (p=0.02)112
Criminal convictions (mean per 1,000 months)RCTDiD=8.9 (95% CI, −1.5 to 19.3) (p=0.09)112
Shelter daysRCTGreater reduction in G1 than G2: IRR=0.30 (95% CI, 0.17 to 0.53) (p<0.01)109
Any drug/alcohol treatmentRCTDiD=0.92 (95% CI, 0.65 to 1.30) (p=0.62)112

AD = adjusted difference; CI = confidence interval; DiD = difference-in-difference; G = group; IRR = incidence rate ratio; RCT = randomized controlled trial.

Table B-39Study characteristics of ED-based care model interventions

First Author, Year, Site(s)Sample SizeStudy Design (Risk of Bias Assessment)Patient Selection: High Healthcare Use or Cost; Time PeriodPatient Selection: Chronic ConditionsPatient Selection: OtherAdditional Selected Patient Characteristics

Kelley et al., 2020110

New Haven, CT: 1 site

(N=100)

Intervention (N=49)

Comparison (N=51)

RCT (ROB: low)4–18 ED visits in past yearNA18–62 years, active Medicaid insurance, resident of one of 12 surrounding towns, and English or Spanish speaking

Mean age: 40

Nonwhite: 82%

Medicaid: 100%

Medicare: NR

Lin et al., 2017111

Boston, MA: 1 site

(N=72)

Intervention (N=36)

Comparison (N=36)

RCT (ROB: some concerns)Most ED visits during the 30-day period and 12-month period preceding the introduction of the programNANA

Mean age: 48

Nonwhite: 57%

Medicaid: 44%

Medicare: 43%

Shumway et al., 2008116

San Francisco, CA: 1 hospital

(N=252)

Intervention (N=167)

Comparison (N=85)

RCT (RoB: some concerns)5+ ED visits in past 1 yearNAAge: 18+; psychosocial problems that could be addressed with case management

Nonwhite: 87%;

Homeless: 81%

Alcohol problems, alcohol use: 57%

Lack health insurance: 67%

Charlson Comorbidity Index (Mean [SD]): 1.4 (2.2)

Most common diagnosis in prior 1 year: mental disorders, 22%

Seaberg et al., 2017118

Chattanooga, TN metro area: 5 sites

(N=304)

Intervention (N=163)

Comparison (N=141)

RCT (RoB: some concerns)5+ ED visits in past 1 yearNANANA

Enard and Ganelin, 2013127

Houston, TX: 1 site

(N=13,642)

Intervention (N=1,905)

Comparison (N=11,737)

Observational study (RoB: high)Frequent use of the ED for primary care, data included visits to any of the 9 EDs in the healthcare system, receiving urgent or primary care in the ED (levels 3, 4, and 5 medical decisions of minimal to moderate complexity but may be considered PCP patients) in past 1 yearNAAge: 18–64; on Medicaid, uninsured/self-pay, or covered by TX public health benefit

Nonwhite: 82%

Age 18–34: 58.6%

Uninsured: 62.7%

McCormack et al., 2013129

New York, NY: 1 site

(N=60)

Intervention (N=20)

Prospective controls (N=20)

Retrospective controls (N=20)

Observational study (RoB: high)5+ ED visits annually for 2 consecutive years and 1 within 6 monthsAlcohol dependenceUndomiciled without shelter use for 9 of 24 months

Mean age: 50.0 +/-

10.0 years

Nonwhite: NR

Medicaid: NR

Medicare: NR

Navratil-Strawn et al., 2014120

National

(N=14,140)

Intervention (N=7,070)

Comparison (N=7,070)

Observational study (RoB: some concerns)3+ ED visits in past 1 yearNAAge: 65+; UnitedHealth-Care’s AARP Medigap insuranceLive in area with medium/high Minority status: 38%

CA = California; CT = Connecticut; ED = emergency department; MA = Massachusetts; N = number; NA = not applicable; NR = not reported; NY = New York; PCP = primary care provider; RCT = randomized controlled trial; RoB = risk of bias; SD = standard deviation; TN = Tennessee; TX = Texas.

Table B-40Intervention characteristics of ED-based care model interventions

First Author, Year, Site(s)Intervention: Brief DescriptionIntervention DurationAssessment, Education, Skills, MonitoringCoordination and Continuity of CareReferral to/Linkages to Community-Based Support ServicesProvidersMode of Delivery Setting(s)IntensityComparison

Kelley et al., 2020110

New Haven, CT: 1 site

Project Access-New Haven: ED-initiated patient navigation program (ED-PN)12 monthsCompleted a detailed questionnaire with the patient (via interview) about demographics, socioeconomic status, health literacy, medical conditions, clinical and social needs, health-related quality of life, utilization of healthcare services, access/barriers to care, and reasons for ED use. Open-ended questions were also asked to elucidate ways in which the patient could most benefit from patient navigator servicesPatient navigators connected patients to primary care by meeting face-to-face with patients, offering accompaniment to PCP visit(s), phoning each patient regularly to remind them of medical appointments, and problem-solving to overcome personal barriers such as transportation. Patient navigator and patient created a task list based on the provider’s recommendations including assisting in scheduling any additional appointments recommended by PCPConnected patients to local resources to address social needs such as precarious housing, food insecurity, or insurance questionsNurse navigator and a trained patient navigatorFace-to-face or over the phonePatient navigators scheduled regular phone calls to each patient every 2 weeks during weeks 0–4 and every 4 weeks during weeks 13–52Usual care

Lin et al., 2017111

Boston, MA: 1 site

Pilot ED-based care coordination and community health worker program7 monthsED physicians performed a detailed chart review for all patients to identify unmet medical and social issues driving frequent ED visits and CHWs reviewed each patient’s chart and called patients to conduct a standardized intake assessment to determine unmet needsAcute care plans developed in conjunction with the patient’s longitudinal providers including PCPs and uploaded to EHR to be visible to all clinicians and assignment of an ED-based CHW who assisted with care coordination and other tailored needs (e.g., connect with primary care, provide transportation)CHW addressed unmet social and behavioral needs that contributed to ED utilization (e.g., food banks)ED physician and physician assistant, ED community health worker, and nurse care coordinatorFace-to-face or phone including home visitsNRUsual care

Shumway et al., 2008116

San Francisco, CA: 1 hospital

Case management24 monthsPsychosocial problems and functioning were assessed at study entry and at 6, 12, 18, and 24 months. Psychosocial assessment included homelessness, problem alcohol use, lack of health insurance, lack of Social Security income, unmet basic financial needs, and psychiatric symptoms.Provided linkage to medical care providers and ongoing assertive community outreach to maintain continuity of careAssistance in obtaining stable housing, income entitlements, and referral to substance abuse services when neededMaster’s-level psychiatric social workers provided most case management services in collaboration with a nurse practitioner, a primary care physician, and a psychiatristFace-to-face individual and group sessionsNRUsual care

Seaberg et al., 2017118

Chattanooga, TN metro area: 5 sites

Patient navigation for ED patients12 monthsReviewed diagnoses and prescriptionsNRArranged followup appointments and identified relevant community resourcesPatient navigator with hospital case management trainingIn person and via telephoneInitial ED visit, subsequent ED visits, followup calls within 2 weeks and 12 months of initial ED visitUsual care

Enard and Ganelin, 2013127

Houston, TX: 1 site

ED-based patient navigation program≤10 daysInitial assessment to determine barriers to appropriate primary care use. Educate patients on importance of making and keeping appointments and receiving preventive health care. Help patients identify barriers to primary care and identify local, state, and federal resources appropriate for their needsPatient navigators maintain relationships with community-based providers to bolster referral relationships ensure that contact information is currentConnect patients with neighborhood providers or clinics and assess their needs for specific types of referrals based on access issues and health conditions.CHWs as patient navigatorsIn person and via telephoneInitial ED visit and a followup call 3 to 10 days laterUsual care

McCormack et al., 2013129

New York, NY: 1 site

Coordinated case management and facilitated access to homeless outreach services6 monthsSocial workers approached patients and, if authorized, faxed intake and medical release forms to the outreach team. On a subsequent visit, the outreach team came to the ED to confirm eligibility and complete enrollmentSocial worker and outreach team met with participants to relocate patients into supportive settings, coordinate multidisciplinary care, and update plans based on participants’ medical, psychosocial, and housing needsCare plans to offer shelter on dischargeSocial worker and outreach teamFace-to-faceFrequency based on number of visits to the EDUsual care

Navratil-Strawn et al., 2014120

National

Emergency Room Decision-Support (ERDS) programNR; up to 12 months (followup time)Assessment of health needs and treatment optionsMake appointments with providers, refer to care coordination programsProvide connections with health resourcesNurseTelephoneNRUsual care

CA = California; CHW = community health worker; ED = emergency department; ED-PN = ED-initiated Patient Navigation; EHR = electronic health record; ERDS = Emergency Room Decision-Support; MA = Massachusetts; NR = not reported; NY = New York; PCP = primary care provider; TN = Tennessee; TX = Texas.

Table B-41Healthcare utilization outcomes for ED-based care model studies

Utilization MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
ED visitsRCTGreater reduction in G1 than G2: DiD=−1.37 (95% CI, −2.40 to −0.34) (p=0.01)110
RCTNRNRBetween group difference (G1–G2): −35% (p=0.10)111
RCTLower use in G1 than G2a (p<0.01)116
RCTGreater reduction in G1 than G2 (p<0.0001)118
ObservationalGreater reduction in G1 than G2: DiD=−178 (p=0.033)120
ObservationalNRNR

Greater reduction in G1 than G2: DiD (prospective controls): −12.1 (95% CI, −22.1 to −2.0) (p=0.02)

DiD (retrospective controls): −12.8 (95% CI, −26.1 to 0.6) (p=0.06)129

ED visits, anyObservational

Lower use in G1 than G2 among patients with PCR-ED visits in prior 24 months

≥1: OR=0.55 (95% CI, 0.47 to 0.63);

≥2: OR=0.46 (95% CI, 0.37 to 0.57);

≥3: OR=0.32 (95% CI, 0.24 to 0.44);

≥4: OR=0.29 (95% CI, 0.19 to 0.44); or

≥5: OR=0.31 (95% CI, 0.17 to 0.54)127

Observational

G1 vs. G2 among patients with PCR-ED visits in prior 12 months

≥1: OR=0.83 (95% CI, 0.71 to 0.98);

≥2: OR=0.72 (95% CI, 0.57 to 0.93);

≥3: OR=0.90 (95% CI, 0.60 to 1.3);

≥4: OR=0.98 (95% CI, 0.52 to 1.8); or

≥5: OR=0.96 (95% CI, 0.39 to 2.3)127

Inpatient admissions, all causeRCTGreater reduction in G1 than G2: DiD=−0.97 (95% CI, −1.56 to −0.38) (p=0.001)110
ObservationalGreater reduction in G1 than G2: DiD=−53 (p=0.002)120
Inpatient daysObservationalNRNR

DiD (prospective controls): −8.5 (95% CI, −22.8 to 5.8) (p=0.24)

DiD (retrospective controls): −19.0 (95% CI, −34.3 to −3.6) (p=0.06)129

Medical inpatient admissionsRCTG1 vs. G2a (p=NS)116
Medical inpatient daysRCTG1 vs. G2a (p=NS)116
Psychiatric emergency visitsRCT--G1 vs. G2a (p=NS)116
Psychiatric inpatient admissionsRCTG1 vs. G2a (p=NS)116
Psychiatric inpatient daysRCTG1 vs. G2a (p=NS)116
Outpatient visitsRCTDiD: 0.60 (95% CI, −0.43 to 1.63) (p=0.25)110
RCTG1 vs. G2a (p=NS)116
Primary care visitsRCTGreater use in G1 than G2 (p=0.001)118
ObservationalSmaller reduction in G1 than G2: DiD=897 (p<0.001)120
a

Interaction between level of prior ED use (5 to 11 or ≥12 visits in prior 12 months) and group: p=NS.

⬆ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

⬇ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

⇧ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

⇩ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

CI = confidence interval; DiD = difference-in differences; ED = emergency department; G = group; NR = not reported; NS = not statistically significant; OR = odds ratio; PCR-ED = primary care-related emergency department; RCT = randomized controlled trial; vs. = versus.

Table B-42Strength of evidence for ED-based care models versus usual-care outcomes

PopulationOutcomeResultsStudy Design and Sample SizeStrength of Evidence DomainsOverall Evidence Strength (Direction of Effect)
HNHC patientsED visits

ED-PN RCT: greater reduction in G1 than G2: DiD=−1.37 (95% CI, −2.40 to −0.34)110

CHW RCT: between-group difference: −35% (p=0.10)111

Case management RCT: lower use in G1 than G2 (p<0.01);116

Navigation RCT: greater reduction in G1 than G2 (p<0.0001);118

ERDS: greater reduction in G1 than G2: DiD=−178 (p=0.033)120

ED and housing: greater reduction in G1 than G2: DiD (prospective controls): −12.1 (95% CI, −22.1 to −2.0); DiD (retrospective controls): −12.8 (95% CI, −26.1 to 0.6)129

4 RCTs, N=728

2 OBSs, N=14,200

Moderate study limitations (2 high RoB OBS studies),129 consistent, precise, directModerate (Favorable)
HNHC patientsInpatient admissions, all cause

ED-PN RCT: greater reduction in G1 than G2: DiD=−0.97 (95% CI, −1.56 to −0.38) (p=0.001)110

ERDS: greater reduction in G1 than G2: DiD=−53 (p=0.002)120

1 RCT, N=100

1 OBS, N=14,140

Moderate study limitations, consistent, precise, directLow (Favorable)
HNHC patientsPrimary care visits

Navigation RCT: greater use in G1 than G2 (p=0.001)118

ERDS: smaller reduction in G1 than G2: DiD=897 (p<0.001)120

1 RCT, N=304

1 OBS, N=14,140

Moderate study limitations, consistent, precise, directLow (Favorable)
HNHC patientsOutpatient visits

ED-PN RCT: DiD: 0.60 (95% CI, −0.43 to 1.63) (p=0.25)110

Case management RCT: G1 vs. G2 (p=NS)116

2 RCTs, N=352Moderate study limitations, inconsistent, imprecise, directInsufficient (Mixed findings)
HNHC patientsED costs

CHW RCT: between-group difference=−15% (p=0.20)111

Case management RCT: lower in G1 than G2a (p<0.01)116

Navigation RCT: greater reduction in G1 than G2 (p<0.0001)118

ERDS: DiD=−21 (p=0.140)120

3 RCTs, N=628

1 OBS, N=14,140

Moderate study limitations; consistent, precise, directLow (Favorable)
HNHC patientsInpatient costs

CHW RCT: between-group difference=−8% (p=0.10)111

Case management RCT: G1 vs. G2 (p=NS)116

ERDS: DiD=−59 (p=0.080)120

2 RCTs, N=324

1 OBS, N=14,140

Moderate study limitations, inconsistent, imprecise, directLow (No difference)
HNHC patientsHospital costs of care

ED-PN RCT: DiD=−10,202 (95% CI, −22,464 to 2,062)110

Case management RCT: G1 vs. G2 (p=NS)116

2 RCTs, N=352Moderate study limitations, inconsistent, imprecise, directInsufficient
HNHC patientsOutpatient costs

Case management RCT: G1 vs. G2 (p=NS)116

ERDS: DiD=10 (p=0.828)120

1 RCT, N=252

1 OBS, N=14,140

Moderate study limitations, inconsistent, imprecise, directInsufficient

CHW = community health worker; CI = confidence interval: DiD = difference-in differences; ED = emergency department; ED-PN = ED-initiated Patient Navigation; ERDS = Emergency Room Decision-Support; G = group; HNHC = high-need, high-cost; N = number; NS = not significant; OBS = observational study;; RCT = randomized controlled trial; RoB = risk of bias; vs. = versus.

Table B-43Cost outcomes for ED-based care model studies

Cost MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
ED costsRCTNRNRBetween-group difference (G1–G2) =−15% (p=0.20)111
RCTLower in G1 than G2a (p<0.01)116
RCTGreater reduction in G1 than G2 (p<0.0001)118
ObservationalDiD=−21 (p=0.140)120
Inpatient costsRCTG1 vs. G2a,b (p=NS)116
RCTNRNRBetween group difference (G1–G2) =−8% (p=0.10)111
ObservationalDiD=−59 (p=0.080)120
Medicaid cost (payment)RCTDiD=−5,765 (95% CI, −15,883 to 4,353) (p=0.26)110
Psychiatric emergency costsRCTG1 vs. G2a (p=NS)116
Psychiatric hospital costsRCTG1 vs. G2a (p=NS)116
Hospital costs of careRCTDiD=−10,202 (95% CI, −22,464 to 2,062) (p=0.10)110
RCTG1 vs. G2a,c (p=NS)116
All non-ED case management costsRCTG1 vs. G2a (p=NS)116
Total costs ($)ObservationalDiD=−40 (p=0.502)120
Outpatient costsRCT--G1 vs. G2a,b (p=NS)116
ObservationalDiD=10 (p=0.828)120
Prescription costs ($)ObservationalDiD=9 (p=0.201)120
a

Interaction between level of prior ED use (5 to 11 or ≥12 visits in prior 12 months) and group: p=NS.

b

Shumway et al. specified the outpatient and inpatient costs as medical outpatient costs and medical hospital costs.116

c

Shumway et al. included the costs for the ED case management intervention in all hospital costs.116

⬇ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

⇧ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

⇩ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

CI = confidence interval; DiD = difference-in differences; ED = emergency department; G = group; NR = not reported; NS = not statistically significant; RCT = randomized controlled trial; vs. = versus.

Table B-44Clinical and functional outcomes for ED-based care model studies

Clinical and Functional OutcomesStudy DesignDifference Between Intervention Group (G1) and Comparison Group (G2)
MortalityRCTG1: 0/20 vs. prospective G2: 2/20 and retrospective G2: 7/20 (p=NR)129
Satisfaction (4-point Likert scale)RCTG1 vs. G2 (p=NS)118
Psychiatric symptoms (total BSI)RCTG1 vs. G2a (p=NS)116
a

Interaction between level of prior ED use (5 to 11 or ≥12 visits in prior 12 months) and group: p=NS.

BSI = brief symptom inventory; ED = emergency department; G = group; NR = not reported; NS = not statistically significant; RCT = randomized controlled trial; vs. = versus.

Table B-45Social risk outcomes for ED-based care model studies

Social Risk OutcomesStudy DesignDifference Between Intervention Group (G1) and Comparison Group (G2)
HousedRCTG1: 18/20 vs. prospective G2: 0/20 and retrospective G2: 0/20 (p=NR)129
Problem alcohol use (%)RCTLower in G1 than G2a (p=0.04)116
Homelessness (%)RCTLower in G1 than G2a (p<0.01)116
No health insurance (%)RCTLower in G1 than G2a (p=0.02)116
No social security income (%)RCTLower in G1 than G2a (p<0.01)116
Basic financial needs unmetRCTLower in G1 than G2a (p=0.04)116

ED = emergency department; G = group; NR = not reported; RCT = randomized controlled trial; vs. = versus.

Table B-46Study characteristics of aICU (ambulatory intensive caring unit) interventions

First Author, Year, Site(s)Sample SizeStudy Design (Risk of Bias Assessment)Patient Selection: High Healthcare Use or Cost; Time PeriodPatient Selection: Chronic ConditionsPatient Selection: Other

Yoon et al., 201987; Zalman, 201997

GA, OH, WI, NC, CA: 5 sites

(N=2,210)

Intervention (N=1,105)

Comparison (N=1,105)

(N=1,527)

Intervention (N=759)

Comparison (N=768)

RCT (RoB: some concerns)Veterans whose risk of 90-day hospitalization was ≥ 90th percentile based on the VA’s Care Assessment Need (CAN) score and who had experienced a hospitalization or ED visit in past 6 monthsNAIntensive management teams at each site reviewed patient charts to determine whether participants would benefit from intensive services

Durfee et al., 201883

CO: 1 site

(N=3,636)

Intervention (N=1,749)

Comparison (N=1,887)

Observational study (RoB: some concerns)IP admission (analysis index admission) and at least 2 other admissions OR at least one other admission with a serious mental health diagnosis in past 1 yearNAAge: 19+

Horn et al., 201692

Albuquerque, NM: 1 academic medical center

(N=1547)

Intervention (N=753)

Comparison (N=794)

Observational study (RoB: some concerns)High cost (top 1%) in past 1 yearMedically complex patients with a chronic medical condition and at high risk for future hospitalizationParticipant selection guided by the likelihood of recurrent illness and response to care management, and patient willingness to participate in the program and be monitored and contacted

aICU = ambulatory intensive caring unit; CA = California; CAN = care assessment need; CO = Colorado; ED = emergency department; GA = Georgia; IP = inpatient; N = number; NA = not applicable; NC = North Carolina; NM = New Mexico; OH = Ohio; RCT = randomized controlled trial; RoB = risk of bias; VA = Department of Veterans Affairs; WI = Wisconsin.

Table B-47Intervention characteristics of aICU (ambulatory intensive caring unit) interventions

First Author, Year, Site(s)Intervention: Brief DescriptionIntervention DurationAssessment, Education, Skills, MonitoringCoordination and Continuity of CareReferral to/Linkages to Community-Based Support ServicesProvidersMode of Delivery Setting(s)IntensityComparison

Yoon et al., 201987, 97

GA, OH, WI, NC, CA: 5 sites

Augmenting the VA’s PCMH PACT with a PACT-PIM intervention for highest cost patients; locally tailored by siteUp to 15 monthsInitial assessment: record review, followed by comprehensive in-person assessment for medical, MH, and social needs; goals assessment; health coaching for patient and caregivers, pharmacist medication reconciliation and adherence monitoringCare coordination; transitional care management post IP discharge; feedback to PCP, assistance with navigating healthcare servicesNR: VA provides many support services in-houseInterdisciplinary team: physician or nurse practitioner, a nurse, pharmacist, rehabilitation therapists, MH and addiction supportOutpatient, home, and phoneLimited: 1–2 encounters or referral to PCP; Full: goal was 3+ encounters in person or by phone from PIM team; received by 44% of participants. Tailored to individual needs. Full-intervention participants received mean of 14 encounters (range: 3–116)Patient Aligned Care Team (PACT) only

Durfee et al., 201883

CO: 1 sites

IOC in integrated delivery systemNRIn-depth intake assessment included determining medical barriers to improving health, taking into account BH needs, social determinants of health, and patient-identified prioritiesSought to develop comprehensive care plans. More nursing support allowed for medical interventions to be done within the clinic instead of the ED or hospitalNREight existing family practice and internal medicine teams composed of a PCP, medical assistant, and shared nursing and social work resources. Supported by clinical pharmacists, patient navigators, BH cliniciansAdditional staffing to existing PCPs and developed IOCs (new, specialized primary care clinics). Face-to-face with care team; navigators and pharmacists primarily by phoneIOC had higher staff-patient ratio than regular PCP clinic, longer visits, walk-in availability. Mean number of encounters per patient: NRUsual care: historic comparison group who received care prior to implementing the IOC

Horn et al., 201692

Albuquerque, NM: 1 academic medical center

Care One, an intensive chronic care, primary care-oriented program designed to target HNHC patientsNRAssessment of whether amenable to care management based on interview and medical record reviewProvided access to specialty care consultations, assistance from nurse coordinators and social workers, and assistance with unanticipated problems related to access or qualityAssistance with social services, such as transportation to clinical appointments, food stamp applications, etc.A physician, social worker/case manager, patient care coordinator, and MH therapist. A pharmacist assists with medication management for patients with complex comorbiditiesPrimary careNRUsual care

aICU = ambulatory intensive caring unit; BH = behavioral health; CA = California; CO = Colorado; ED = emergency department; GA = Georgia; HNHC = high-need, high-cost; IOC = intensive outpatient clinic; IP = inpatient; MH = mental health; NC = North Carolina; NM = New Mexico; NR = not reported; OH = Ohio; PACT = patient aligned care team; PCMH = Patient-Centered Medical Homes; PCP = primary care provider; PIM = PACT-intensive management; VA = Department of Veterans Affairs; WI = Wisconsin.

Table B-48Healthcare utilization outcomes for aICU (ambulatory intensive caring unit) model studies

Utilization MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
Acute medical/surgery inpatient staysRCTNRNRDiD: 1.13 (95% CI, 0.92 to 1.38)87
Other inpatient staysaRCTNRNRDiD: 1.04 (95% CI, 0.66 to 1.65)87
Inpatient admissionsObservationalDiD: lower reduction in G1 than G2 (negative finding) (p<0.01)83
ED visitsRCTNRNRDiD: 1.02 (95% CI, 0.93 to 1.13)87
Primary care visitsRCTNRNRGreater use in G1: DiD: 1.40 (95% CI, 1.30 to 1.50)87
Care management visitsRCTNRNRGreater use in G1: DiD: 2.70 (95% CI, 1.77 to 4.12)87
Specialty care visitsRCTNRNRDiD: 1.03 (95% CI, 0.96 to 1.10)87
Mental healthcare visitsRCTNRNRGreater use in G1: DiD: 1.33 (95% CI, 1.17 to 1.52)87
Homeless care visitsRCTNRNRDiD: 1.11 (95% CI, 0.86 to 1.44)87
a

Other inpatient stays included psychiatric, substance use disorders, and rehabilitation stays.

⬇ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

Table B-49Cost outcomes for aICU (ambulatory intensive caring unit) model studies

Cost MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
Outpatient costsRCTDiD: greater increase in G1 than G2: 2636 (95% CI, 524 to 4748)87
Inpatient costsRCTDiD: −2,164 (95% CI, −7,916 to 3,587)87
ED costsRCTDiD:−20 (95% CI,−277 to 237)87
Total costs/chargesRCTDiD: 471 (95% CI,−6,347 to 7,290)87
ObservationalDiD: greater reduction in G1 than G2 (p<0.04)83
ObservationalDiD: greater reduction in G1 than G2: −$44,504 (p<0.01)92

⬆ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

⬇ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

⇧ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

⇩ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

CI = confidence interval; DiD = difference-in-difference; ED = emergency department; G = group; RCT = randomized controlled trial.

Table B-50Strength of evidence for aICU models versus usual-care outcomes

PopulationOutcomeResultsStudy Design and Sample SizeStrength of Evidence DomainsOverall Evidence Strength (Direction of Effect)
HNHC patientsTotal costs

PIM: DiD: 471 (95% CI,−6,347 to 7,290)87

IOC: DiD: greater reduction in G1 than G2 (p<0.04);83

Care One DiD: −$44,504 (p<0.01)92

1 RCT, N=2,210

2 OBSs, N=5,183

Moderate study limitations, consistent, precise, directLow (Favorable)
HNHC patientsMortality 1-year post-randomization

PIM: (p=0.93)87

IOC: Lower in G1 than G2: (p<0.01)83

1 RCT, N=2,210

1 OBS, N=3,636

Moderate study limitations, inconsistent, imprecise, directInsufficient

aICU = ambulatory intensive caring unit; CI = confidence interval; DiD = difference-in- difference; G = group; HNHC = high need, high cost; IOC: Intensive Outpatient Clinic; N = number; OBS = observational study; PIM: Patient Aligned Care Team (PACT)-Intensive Management; RCT = randomized controlled trial; vs. = versus.

Appendix Table B-51Clinical and functional outcomes for aICU (ambulatory intensive caring unit) model studies

Clinical and Functional OutcomesStudy DesignDifference Between Intervention Group (G1) and Comparison Group (G2)
Mortality, 1-year post-randomizationRCT(p=0.93)87
ObservationalLower in G1 than G2: (p<0.01)83
Patient-centered care coordinationRCTAOR for all 6 measured dimensions past 6 monthsa (p=NS)97
Access to careRCTAOR for all 3 measured dimensionsb (p=NS)97
Patient satisfaction with careRCTAOR for all 4 measured dimensionsc (p=NS)97
Relationships with Providers: trusted providerRCTGreater in G1 than G2: AOR=1.30 (95% CI, 1.04 to 1.62)97
Relationships with Providers: feel respected by providerRCT(p=NS)97
Healthcare hassles summary score (challenges in getting care)RCT(p=0.61)97
Patient assessment of chronic illness care (PACIC) summary score (receipt of care for chronic illness)RCTGreater in G1 than G2: (p=0.022)97
a

Goals assessed: barriers, medications reviewed, between-visit reminders, primary care informed about specialty care, VA healthcare provider helps coordinate care from different doctors and services, someone talked to them about their health goals, report 10 out of 10 satisfaction with primary care, patient assessment of chronic illness care based on PACIC scale, not significant for medications reviewed in past 6 months, between-visit reminders in past 6 months, primary care informed about specialty care in past 6 months.

b

Dimensions included access to needed services, access to provider when questions about care arise, and received needed services.

c

Dimensions included satisfaction with overall care at VA facility, social services, mental healthcare services, and primary care services.

AOR = adjusted odds ratio; CI = confidence interval; G = group; NS = not statistically significant; PACIC = patient assessment of chronic illness care; RCT = randomized controlled trial; VA = Department of Veterans Affairs.

Table B-52Study characteristics of primary care-based interventions

First Author, Year, Site(s)Type of Intervention; Sample SizeStudy Design (Risk of Bias Assessment)Patient Selection: High Healthcare Use or Cost; Time PeriodPatient Selection: Chronic ConditionsPatient Selection: OtherAdditional Selected Patient Characteristics

McCall et al., 201086

5 MA counties (Norfolk, Suffolk, Middlesex, Essex, and Plymouth)

Primary care model includes home visits pay-for-performance APM (Original N=5,374)

Intervention (N=2,619)

Comparison (N=2,755)

RCT (RoB: some concerns)Beneficiaries with HCC risk scores ≥2.0 and annual costs of ≥$2,000 or HCC risk scores ≥3.0 and ≥$1,000 annual medical costs in past 1 yearHCC risk scores ≥2.0Medicare FFS beneficiaries; at least 2 visits to MA General Hospital physicians for a selected group of outpatient and ED procedures

Nonwhite: 13.4%

Medicaid: 5.3%

McCall et al., 201086

5 MA counties (Norfolk, Suffolk, Middlesex, Essex, and Plymouth)

Primary care model includes home visits pay-for-performance APM (Refresh N=1,569)

Intervention (N=785)

Comparison (N=784)

RCT (RoB: some concerns)Beneficiaries with HCC risk scores ≥2.0 and annual costs of ≥$2,000 or HCC risk scores ≥3.0 and ≥$1,000 annual medical costs in past 1 yearHCC risk scores ≥2.0Medicare FFS beneficiaries; at least 2 visits to MA General Hospital physicians for a selected group of outpatient and ED procedures

Nonwhite: 12.8%

Medicaid: 5.4%

Sledge et al., 2006115

Northeastern U.S.: 1 site

Primary care model (N=96)

Intervention (N=47)

Comparison (N=49)

RCT (RoB: low)2+ medical or surgical hospital admissions per year, excluded highest cost outliers in 12–18 month periodNAAge: 18+

Nonwhite: 62.5%

Medicare/Medicaid: 93%

Major depression: 34%

6th grade or lower reading level: 32% of those who spoke English (n=91)

Coleman et al., 2001117

Denver, CO: 19 physician-nurse teams in 8 PCP practices

Primary care model: (Group visits)

(N=295)

Intervention (N=146)

Comparison (N=149)

RCT (RoB: some concerns)11+ outpatient visits in past 18 months1+ self-reported chronic conditionAge: 60+NA

Katzelnick et al., 200099

WI, WA, MA: 163 physician practices affiliated with 1 of 3 included HMOs

Primary care model (N=407)

Intervention (N=218)

Comparison (N=189)

RCT (RoB: some concerns)Ambulatory visit counts above the 85th percentile (including PCP, medical specialty, and walk-in clinic visits—not MH provider visits) in previous 2 yearsScreened positive for current major depression or major depression in partial remission (HAM-D score of 15 or more)Age: 25–63Female: 77%

Powers et al., 2020108

Memphis, TN: 1 site

Primary care model (N=253)

Intervention (N=93)

Comparison (N=160)

RCT (RoB: some concerns)Top 5% of total medical expenditures in past year and 2+ inpatient admissions or 3+ ED visits in past yearTop 5% chronic Illness Intensity Index (CI3) score and 2+ chronic conditionsNA

Mean age: 45

Nonwhite: NR

Medicaid: 100%

Medicare: NR

Crane et al., 201298

Hendersonville, NC: 1 site

Primary care model: Group Visits (N=72)

Intervention (N=36)

Comparison (N=36)

Observational study (RoB: high)6+ ED visits in past 1 yearNABelow 200% FPL; uninsured

Median age: 32

The following were only reported in intervention group Chronic pain: 75%

Uninsured: 100%

Substance abuse: 47%

Depression: 36%

Adam et al., 2010102

MN: 1 residency clinic

Primary care model (N=21)

Intervention (N=13)

Comparison (N=8)

Observational study (RoB: high)8+ clinic visits in past 1 yearNAAge: 18+

Psychiatric diagnosis: 85%

Nonwhite: 28%

Female: 65%

Median age: 49.5

Vickery et al., 2018100, 125

MN: 1 site

Primary care model (N=NR of 92,891)

Intervention (N=NR of 19,433)

Comparison (N=NR of 73,458)

Observational study (RoB: some concerns)HNHC patient subgroup: More than 4 ED visits or 2 inpatient hospital visits in past 1 yearNAWhole study population: Adults with 1+ months of enrollment under early Medicaid expansionNR

Harrison et al., 2020130

Philadelphia, PA: 12 sites

Primary care model (N=3,048)

CBCM enrollee (N=896)

Comparison (N=2,152)

Observational (ROB: some concerns)Top 10% of total costs for the Medicaid MCO in past yearNAReceived primary care at 1 of 12 participating clinical practices, 18+ years, enrolled in Medicaid

Percentage in age range (years): 18–39: 27%

40–59: 60%

≥60: 12%

Nonwhite: 88%

Medicaid 2+ years: 82%

Medicare: NR

APM = advanced alternative payment model; CBCM = Community-Based Care Management; CO = Colorado; ED = emergency department; FFS = fee-for-service; FPL = federal poverty line; HAM-D = Hamilton Depression Rating Scale; HCC = hierarchical condition category; HNHC = high-need, high-cost; MA = Massachusetts; MCO = managed care organization; MH = mental health; MN = Minnesota; N = number; NA = not applicable; NC = North Carolina; NR = not reported; PA = Pennsylvania; PCP = primary care provider; RCT = randomized controlled trial; RoB = risk of bias; TN = Tennessee; U.S. = United States; WA = Washington; WI = Wisconsin.

Table B-53Intervention characteristics of primary care-based interventions

First Author, Year, Site(s)Intervention: Brief DescriptionIntervention DurationAssessment, Education, Skills, MonitoringCoordination and Continuity of CareReferral to/Linkages to Community-Based Support ServicesProvidersMode of Delivery Setting(s)IntensityComparison

McCall et al., 201086

5 MA counties (Norfolk, Suffolk, Middlesex, Essex, and Plymouth)

Provider-based care management programUp to 36 months for original population, up to 24 months for refresh populationConducted comprehensive assessment to evaluate the unique needs of each patient; educated patients about resources available and lifestyle changes that could help to prevent exacerbations of disease, to prevent or delay hospitalization and about the purpose of their medications and other treatment interventions; reviewed self-management activitiesThe role of the case manager is to provide support across patients’ continuum of careConnect patients to resources to meet medical and psychosocial needsNurse case managers supported by case management program project manager, an administrative assistant, a community resource specialist, and a patient financial counselor as well as social workers to help with mental health program and pharmacistsIn person at primary care offices, hospitals, and home visits as needed and via telephoneAmong the original population, 97% of the intervention group received at least 1 contact. The mean number of contacts per patient was 8. A higher proportion of patients in the HCC risk score group received 10 or more calls during the 36-month period compared with the low HCC risk score group (27% compared to 17%), with 10% of patients in the high HCC risk score group receiving 20+ phone calls compared with 4% in the low HHC risk score group. In the refresh group, 87% received at least 1 contact, and the mean number of contacts was 4Usual care

Sledge et al., 2006115

Northeastern U.S.: 1 site

Primary Intensive Care (PIC)1 yearComprehensive interdisciplinary medical and psychosocial assessment, used a patient-centered approach to improve self-care patterns and coping skills, aimed to track and facilitate completion of recommendations made to the PCP based on the assessmentA report and recommendations for care were presented to PCP and subspecialty providers; the recommendations were intended to optimize chronic illness management; case manager used patient-centered approach to improve coordination of careOffered assistance with referrals and appointmentsPsychiatric nurse and team including social worker, psychiatrist, and general internistIn person and via telephone; home visits when necessaryVaried based on patient needs; at minimum included a monthly telephone call; patients were defined by 3 levels of contact: minimum contact, biweekly contact, and weekly or greater contactUsual care (psychiatric consultation provided only if requested by PCP)

Coleman et al., 2001117

Denver, CO: 19 physician-nurse teams in 8 PCP practices

Patient group visits model of care2 yearsEducation on general and specific health topics, medication management, exercise, and nutrition; assessments included health promotion activities such as blood pressure measurement and need for and delivery of immunizations and medication refills and ongoing chronic disease management and evaluation of acute conditions in 1:1 sessionsProvided active care coordination within the primary care team and between other providers and care settings; promoted continuity of care with the health teamNRPrimary care physician, nurse, pharmacist. Periodic ancillary providers: dietitian, social worker, physical therapistIn personGroup visits were held monthly for 24 months, 120 minutes per session, with an average attendance of 8 to 12 participants per groupUsual care

Katzelnick et al., 200099

WI, WA, MA: 163 physician practices affiliated with 1 of 3 included HMOs

Depression management program (DMP)1 yearAssessed patients to confirm diagnosis and appropriateness for medication; patients and PCPs received education about depression; periodic followup with PCP to monitor patient status and telephone monitoring by coordinatorsPC was supported by coordinators who reviewed patient prescription refills and office visits and monitored treatment adherence, response, and adverse effectsStudy psychiatrists had ongoing contact with PCPs via periodic case reviews and as-needed telephone consultation; psychiatric consultations were encouraged for patients not responding to treatment by 10 weeks and those with more complicated depressionPCPs, psychiatrists, and treatment coordinators with some clinical mental health experienceIn person and via telephoneCoordinators made telephone contact at 2 weeks, 10 weeks, 18 weeks, and 30 weeks and have an average of 2.7 contacts per patient; followup visits with the PCP were prescheduled at 1, 3, 6, and 10 weeks, subsequent visits occurred approximately every 10 weeksUsual care

Powers et al., 2020108

Memphis, TN: 1 site

Complex care management program12 monthsIn-person intake visit to assess patient’s medical, behavioral, and social risk factors. The patient and care team co-developed a tailored care plan that outlined interventions, roles, and responsibilities. CHW called patients at least weekly to assess progress and troubleshoot barriersCHW responsible for patient outreach, engagement, activation, and accompaniment while the social worker was responsible for coordinating referrals to social service agencies and other medical providers and the PCP was responsible for proving comprehensive care for acute and chronic conditions and for coordinating with specialist and inpatient providersCHW accompanied patients to specialist, social service, and other appointments as needed. Social worker responsible for counseling and brief interventions for patients with behavioral health needs and for coordinating referrals to social service agencies.Community health worker, a social worker, and a primary care providerTelephone or face-to-face at the PCP officeWeekly phone call from CHW and monthly in person followup visit to review and revise the care planUsual care

Crane et al., 201298

Hendersonville, NC: 1 site

Care management and drop-in group medical appointments (DIGMAs)1 yearSmall-group sessions emphasized life skills; group and individual sessions addressed health and behavioral issues identified by the patientFor patients with a PCP, care other than emotional or group support provided in the program was reported to or coordinated with the PCP. All care was documented in an electronic medical record, including all phone calls. These records could be accessed by physicians in the ED as needed.Program was based at a county free clinic that provided office space for case manager, room for DIGMA, and variety of wrap-around services to intervention group including free prescriptions at onsite pharmacyThe care team consisted of a family physician, 2 behavioral health providers, and a nurse care managerIn person in clinic or via telephoneDIGMA visits were scheduled twice a week for 1 hour; small group sessions with care manager, scheduled twice a week for 1 hour; direct telephone access to RN care manager available Monday-Friday, 8 AM to 5 PM; median number of visits per month per patient was 2 and median number of patient contacts per month was 3.5Usual care

Adam et al., 2010102

MN: 1 residency clinic

Family medicine care team6 monthsCare team met to review the healthcare status of a case patient and develop a care plan based on discussions with the patient’s primary physician. A member of the team called the patient to schedule a free visit to review the care planThe team was interdisciplinary; providing joint care with consultants was encouraged when indicated, as was engaging family members or other stakeholders in the patient’s careCare plans included referrals as needed to care such as mental health treatment or medicationsFaculty physician, 4 resident physicians, the clinic psychologist, pharmacist, triage nurse, certified medical assistant, and front desk managerIn person at family medicine residency clinicCare team met 1 time per week to discuss the care and status of a patient

Vickery et al., 2018100

MN: 1 site

Hennepin Health Accountable Care Organization (HH ACO)24 monthsDeveloping patient-specific plans based on need, patient education and goal settingConnected patients to primary care and other necessary services, increased access to care for mental illness and substance use (behavioral health) including integration within primary care, care coordination and disease management service intensity based on riskIntegrated county services for social and behavioral needs (housing, vocational, social services) with health care, increased access to services to meet social needs, improved access to dental services, high-risk patient referred to a Coordinated Care Center

Care coordination team: RN care coordinators embedded in primary care clinics, clinical social workers, CHW

Coordinated care center team: physician, NP or PA, care coordinator, social worker, psychologist, pharmacists, licensed chemical dependency counselors, and a part-time addiction psychiatrist support

Patients received care at various care settings, program managed by countyNR; based on patient riskUsual care: enrolled in non-Hennepin Health managed care in Hennepin or Ramsey County

Harrison et al., 2020130

Philadelphia, PA: 12 sites

MCO-led Community-Based Care Management (CBCM)12 monthsNRCoordinated care and managed medicationsIdentified social needs and connected patients to local community resources and partnered with a nonprofit to provide health and substance abuse services for Philadelphia County Medicaid recipientsCommunity health workers hired by practices and nurse case managers hired by the MCONRVaried by practice siteUsual care

CBCM = Community-Based Care Management; CHW = community health worker; CO = Colorado; DIGMA = drop-in group medical appointments; DMP = depression management program; ED = emergency department; HCC = hierarchical condition category; HH ACO = Hennepin Health Accountable Care Organization; MA = Massachusetts; MCO = managed care organization; MN = Minnesota; NC = North Carolina; NP = nurse practitioner; NR = not reported; PA = Pennsylvania; PC = primary care; PCP = primary care provider; PIC = primary intensive care; RN = registered nurse; TN = Tennessee; U.S. = United States; WA = Washington; WI = Wisconsin.

Table B-54Healthcare utilization outcomes for primary care–based model studies

Utilization MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
ED visits, all causeRCTDifference in change over time, G1 vs. G2: (p=0.9)115
RCTOriginal sample: IRR=0.94 (95% CI, 0.81 to 1.09)86
RCTRefresh sample: Lower increase in G1 than G2: IRR=0.75 (95% CI, 0.57 to 0.99)86
RCTNRNRLower use in G1 than G2: aMD=−0.42 (95% CI, −0.13 to −0.72)117
RCTNRNRaMD=−0.02 (95% CI, −0.51 to 0.47) (p=1.00)108
ObservationalNRNRGreater use in G1 than G2: aMD=0.18 (95% CI, 0.01 to 0.37)130
ObservationalNRNRG1 vs. G2: aMD=34.22 (95% CI, −10.9 to 79.3)100, 125
ObservationalGreater reduction in G1 than G2: (p=0.005)98
ObservationalDifference=0.5 (p=NR)102
ED, any (%)RCTNRNRLower use in G1 than G2: aRR=0.64 (95% CI, 0.44 to 0.86)117
ED visits, ACSCRCTOriginal sample: IRR=0.90 (95% CI, 0.70 to 1.16)86
RCTRefresh sample: IRR=0.80 (95% CI, 0.51 to 1.25)86
Inpatient admissions, all causeRCTDifference in change over time, G1 vs. G2: (p=0.55)115
RCTOriginal sample: lower increase in G1 than G2: IRR=0.81 (95% CI, 0.70 to 0.94)86
RCTRefresh sample: lower increase in G1 than G2: IRR: 0.76 (95% CI, 0.58 to 0.99)86
RCTNRNRLower use in G1 than G2 (mean # of admissions): 0.44 vs. 0.81 (p=0.04)117
RCTG1 vs. G2: (p=0.09)99
RCTNRNRGreater reduction in G1 than G2: aMD=−0.32 (95% CI, −0.54 to −0.11) (p=0.014)108
ObservationalNRNRGreater use in G1 than G2: aMD=0.10 (95% CI, 0.03 to 0.16)130
ObservationalNRNRLower use in G1 vs. G2: aMD=−26.11 (95% CI, −35.9 to −16.3)100, 125
Observational----Difference=0 (p=NR)102
Inpatient admissions, any (%)RCTOriginal sample: lower increase in G1 than G2, OR=0.65 (95% CI, 0.55 to 0.78}86
RCTRefresh sample: greater reduction in G1 than G2, OR=0.66 (95% CI, 0.48 to 0.90)86
Inpatient admissions, ACSCRCTOriginal sample: IRR=0.87 (95% CI, 0.66 to 1.14)86
RCTRefresh sample: IRR=0.78 (95% CI, 0.49 to 1.24)86
Inpatient admissions, any ACSC (%)RCTOriginal sample: lower increase in G1 than G2, OR=0.73 (95 %CI, 0.57 to 0.95)86
RCTRefresh sample: OR=0.79 (95% CI, 0.51 to 1.21)86
Inpatient daysRCTNRNRGreater reduction in G1 vs. G2: aMD=−3.46 (95% CI, −4.04 to −2.89) (p<0.001)108
ObservationalNRNRG1 vs. G2: aMD=−219.7 (95% CI, −826 to 386.6)100, 125
Primary care visitsRCTDifference in change over time, G1 vs. G2: (p=0.055)115
RCTNRNRG1 vs. G2: (p=0.20)117
ObservationalNRNRLess use in G1 than G2: aMD=−1.83 (95% CI, −2.10 to −1.55)130
ObservationalNRNRG1 vs. G2: aMD=6.0 (95% CI: −39.3 to 51.4)100, 125
Outpatient visitsRCTGreater increase in G1 than G2: (p=0.02)99
ObservationalDifference=4.5 (p=NR)102
Care center visitsRCTNRNRaMD: 0.47 (95% CI, −0.16 to 1.11) (p=0.576)108
Specialist visitsRCTNRNRGreater reduction in G1 vs. G2: aMD: −1.35 (95% CI, −1.98 to −0.73) (p<0.001)108
Total visitsObservationalNRNRLess use in G1 than G2: aMD: −1.55 (95% CI, −1.93 to −1.21)130
Filled ≥3 antidepressant prescriptions in first 6 monthsRCTNRNRGreater use in G1 than G2: (p<0.001)99
Specialty mental health visit in first 6 monthsRCTNRNRGreater use in G1 than G2: (p=0.03)99
Cancelled visits and/or no showsObservationalDifference=3.5 (p=NR)102
Intensive care unit visitsObservationalNRNRG1 vs. G2: aMD=0.1 (95% CI: −0.7 to 0.9)100, 125
Dental visitsObservationalNRNRGreater reduction in G1 vs. G2: aMD=−30.7 (95% CI, −41.0 to −20.3)100, 125
a

Outpatient visits were defined as primary care visits and group intervention visits for the intervention group and as primary care visits only for the comparison group.

⬆ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

⬇ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

⇧ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

⇩ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

ACSC = ambulatory care sensitive conditions; aMD = adjusted mean difference; aRR = adjusted risk ratio; CI = confidence interval; ED = emergency department; G = group; IRR = incidence rate ratio; NR = not reported; OR = odds ratio; RCT = randomized controlled trial; vs. = versus.

Table B-55Strength of evidence for primary care–based models versus usual-care outcomes

PopulationOutcomeResultsStudy Design and Sample SizeStrength of Evidence DomainsOverall Evidence Strength (Direction of Effect)
HNHC patientsED visits, all cause

PIC RCT: Diff in change over time (p=0.9)115

MGH CMP original sample: IRR=0.94 (95% CI, 0.81 to 1.09)86

MGH CMP refresh sample: IRR=0.75 (95% CI, 0.57 to 0.99)86

Group visit RCT: aMD=−0.42 (95% CI, −0.13 to −0.72)117

CCM RCT: aMD=−0.02 (95% CI, −0.51 to 0.47)108

CBCM: Greater use in G1 than G2: aMD=0.18 (95% CI, 0.01 to 0.37)130

HH ACO: diff=34.22 (95% CI, −10.9 to 79.3)100, 125

Bridges to Health: Greater reduction in G1 than G2: (p=0.005)98

Interdisciplinary pilot: Diff=0.5 (p=NR)102

5 RCTs, N=7,587

4 OBSs, N=NRa

Moderate study limitations (2 high RoB OBS studies),98, 102 inconsistent, imprecise, directInsufficient (Mixed findings)
HNHC patientsED visits, ACSC

MGH CMP original sample: IRR=0.90 (95% CI, 0.70 to 1.16)86

MGH CMP refresh sample: IRR=0.80 (95% CI, 0.51 to 1.25)86

2 RCTs, N=6,943Moderate study limitations, consistent imprecise, directInsufficient
HNHC patientsInpatient admissions, all cause

PIC RCT: diff in change over time: (p=0.55)115

MGH CMP original sample: IRR=0.81 (95% CI, 0.70 to 0.94)86

MGH CMP refresh sample: IRR: 0.76 (95% CI, 0.58 to 0.99)86

Group visit RCT: less use in G1 than G2: 0.44 vs. 0.81, (p=0.04)117

DMP RCT: G1 vs. G2: (p=0.09)99

CCM RCT: Greater reduction in G1 than G2: aMD=−0.32 (95% CI, −0.54 to −0.11) 108

CBCM: Greater use in G1 than G2=aMD: 0.10 (95% CI, 0.03 to 0.16)130

HH ACO: G1 vs. G2: Diff=−26.11 (95% CI, −35.9 to −16.3)100, 125

Interdisciplinary Pilot: diff=0 (p=NR)102

6 RCTs, N=7,994

3 OBS, N=NRa

Moderate study limitations (one high RoB OBS studies),102 consistent, precise, directInsufficient (Mixed findings)
HNHC patientsInpatient admissions, any (%)

MGH CMP: original sample: OR=0.65 (95% CI, 0.55 to 0.78)86

MGH CMP refresh sample: OR=0.66 (95% CI, 0.48 to 0.90)86

2 RCTs, N=6,943Moderate study limitations, consistent, precise, directLow (Favorable)
HNHC patientsInpatient admissions, ACSC

MGH CMP original sample: IRR=0.87 (95% CI, 0.66 to 1.14)86

MGH CMP refresh sample: IRR=0.78 (95% CI, 0.49 to 1.24)86

2 RCTs, N=6,943Moderate study limitations, consistent, imprecise, directInsufficient
HNHC patientsInpatient admissions, any ACSC (%)

MGH CMP original sample: OR=0.73 (95% CI, 0.57 to 0.95)86

MGH CMP refresh sample: OR=0.79 (95% CI, 0.51 to 1.21)86

2 RCTs, N=6,943Moderate study limitations, consistent, imprecise, directInsufficient
HNHC patientsInpatient days

CCM RCT: Greater reduction in G1 vs. G2: aMD=−3.46 (95% CI, −4.04 to −2.89)108

HH ACO: G1 vs. G2: aMD=−219.7 (95% CI, −826 to 386.6)100, 125

1 RCT, N=253

1 OBS, N=NRa

Moderate study limitations, consistent, imprecise, directInsufficient
HNHC patientsPrimary care visits

PIC RCT: diff in change over time: (p=0.055);115

RCT group visits: G1 vs. G2: (p=0.20);117

CBCM: Less use in G1 than G2: aMD=−1.83 (95% CI, −2.10 to −1.55);130

HH ACO: G1 vs. G2: Diff=6.0 (95% CI: −39.3 to 51.4)100, 125

2 RCTs, N=391

2 OBSs, N=NRa

Moderate study limitations, inconsistent imprecise, directInsufficient (Mixed findings)
HNHC patientsOutpatient visits

DMP: greater use in G1 than G2: (p=0.02)99

Interdisciplinary pilot: diff=4.5 (p=NR)102

1 RCT, N=407

1 OBS, N=21

High study limitations (one high RoB OBS studies)102 imprecise, inconsistent, directInsufficient
HNHC patientsTotal cost

PIC RCT: diff in change over time, G1 vs. G2: (p=0.82)115

MGH CMP: original sample DiD: −288 (p<0.01)86

MGH CMP: refresh sample DiD: −355 (p<0.05)86

CCM RCT: Greater reduction in G1 vs. G2: aMD=−7,732 (95% CI, −14,914 to −550)108

CBCM: aMD=829 (95% CI, −1,279 to 3,098)130

Pooled mean difference: −$3,848.43 (95% CI, −5,514.24 to −2,182.61); 3 RCT samples, N=7,196, I2=0.0%

4 RCTs, N=7,292

1 OBS, N=3,048

Moderate study limitations, consistent, imprecise, directLow (Favorable)
HNHC patientsMortality rate

MGH CMP: original sample diff: G1 vs. G2, −1.63 (p=0.19)86

MGH CMP: refresh sample diff: G1 vs. G2: −3.97 (p=0.04)86

2 RCTs, N=6,943Moderate study limitations, imprecise, inconsistent, directInsufficient
HNHC patientsInfluenza vaccine

MGH CMP: original sample DiD OR=0.79 (95% CI, 0.66 to 0.95)86

MGH CMP refresh sample DiD: OR=0.64 (95% CI, 0.46 to 0.87)86

2 RCTs, N=6,943Moderate study limitations, consistent, precise, directLow (Unfavorable)
HNHC patients Diabetes subgroupHbA1c test

MGH CMP, original sample: DiD OR=0.99 (95% CI, 0.56 to 1.76)86

MGH CMP refresh sample: DiD OR=0.70 (95% CI, 0.27 to 1.84)86

2 RCTs, N=1,959Moderate study limitations, consistent, imprecise, directInsufficient
HNHC patients Diabetes subgroupLDL-C test

MGH CMP original sample: DiD OR=0.85 (95% CI, 0.58 to 1.24)86

MGH CMP refresh sample: DiD OR=1.72 (95% CI, 0.86 to 3.42)86

2 RCTs, N=1,959Moderate study limitations, inconsistent, imprecise, directInsufficient
HNHC patients IVD subgroupLDL-C test

MGH CMP original sample: DiD OR=0.92 (95% CI, 0.63 to 1.33)86

MGH refresh sample: DiD OR=1.40 (95% CI, 0.76 to 2.58)86

2 RCTs, N=1,923Moderate study limitations, inconsistent, imprecise, directInsufficient
a

The HH ACO100, 125 study did not report sample sizes for their HNHC populations; the total sample size for the study was 92,891. The sample size for CBCM130 was 3,048, 21 for the interdisciplinary pilot,102 and 72 for Bridges to Health.98

ACSC = ambulatory care sensitive conditions; aMD = adjusted mean difference; CBCM = Community-Based Care Management; CCM = complex care management; CI = confidence interval; DiD = difference-in-difference; DMP = depression management program; ED = emergency department; G = group; HbA1c = hemoglobin A1c; HH ACO = Hennepin Health Accountable Care Organization; HNHC = high-need, high-cost; IRR = incidence rate ratio; IVD = ischemic vascular disease; LDL-C = low-density lipoprotein cholesterol; MGH CMP = Massachusetts General Hospital, Care Management Program, N = number; NR = not reported; OBS = observational study; OR = odds ratio; PIC = Primary Intensive Care; RCT = randomized controlled trial; RoB = risk of bias; vs. = versus.

Table B-56Cost outcomes for primary care–based model studies

Cost MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
Total costRCTDifference in change over time, G1 vs. G2: (p=0.82)115
RCTOriginal sample DiD: Lower increase in G1 than G2: −288 (82.1), (p<0.01)86
RCTRefresh sample DiD: Lower increase in G1 than G2: −355 (157.6), p<0.0586
RCTNRNRGreater reduction in G1 vs. G2: aMD=−7,732 (95% CI, −14,914 to −550) (p=0.036)108
ObservationalNRNRaMD=829 (95% CI, −1,279 to 3,098)130
Inpatient costsObservationalNRNRaMD=297 (95% CI, −1,150 to 1,729)130
Pharmacy costsObservationalNRNRaMD=110 (95% CI, −875 to 1,109)130
Outpatient costsObservationalNRNRaMD=10 (95% CI, −439 to 492)130
Postacute costsObservationalNRNRaMD=362 (95% CI, 150 to 569)130
ED costsObservationalNRNRaMD=5 (95% CI, −155 to 171)130
Other costsObservationalNRNRaMD=45 (95% CI, −159 to 262)130

⬆ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

⇩ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

aMD = adjusted mean difference; CI = confidence interval; DiD = difference-in-difference; Ed = emergency department; G = group; NR = not reported; RCT = randomized controlled trial; vs. = versus.

Table B-57Clinical and functional outcomes for primary care–based model studies

Clinical and Functional OutcomesStudy DesignDifference Between Intervention Group (G1) and Comparison Group (G2)
Mortality rateRCTOriginal sample difference: G1 vs. G2, −1.63 (p=0.19)86
RCTRefresh sample difference: lower in G1 than G2: −3.97 (p=0.04)86
Influenza vaccineRCTOriginal sample DiD: increased less in G1 than G2: OR=0.79 (95% CI, 0.66 to 0.95)86
RCTRefresh sample DiD: increased less in G1 than G2: OR=0.64 (95% CI, 0.46 to 0.87)86
PHC scoreRCTBetter in G1 than G2: ANCOVA-adjusted intervention effect=2.3, p<0.0186
MHC scoreRCTANCOVA-adjusted intervention effect=1.1, p>0.0586
PQH-2 score (depression 0 to 6)RCTANCOVA-adjusted intervention effect=−0.03, p>0.0586
Number of ADLs difficult to do (0 to 6)RCTANCOVA-adjusted intervention effect=−0.28, p>0.0586
Number ADLs receiving help (0 to 6)RCTANCOVA-adjusted intervention effect=−0.21, p>0.0586
Patient satisfactionRCTDifference in change over time, G1 vs. G2: p=0.30115
SF-36 Summary ScoreRCTDifference in change over time, G1 vs. G2: p=0.32115
SF-36 Mental Health Function ScoreRCTDifference in change over time, G1 vs. G2: p=0.6115
Change in HAM-D scoreRCTGreater decrease in G1 than G2: p<0.00199
In remission (HAM-D <7)RCTHigher proportion in G1 than G2: p<0.00199
SF-20 subscale: Social FunctioningRCTBetter in G1 than G2: p<0.05 (data NR)99
SF-20 subscale: Mental HealthRCTBetter in G1 than G2: p<0.05 (data NR)99
SF-20 subscale: General HealthRCTBetter in G1 than G2: p<0.05 (data NR)99
SF-20 subscale: Physical FunctioningRCTG1 vs. G2: p=NS (data NR)99
SF-20 subscale: Role FunctioningRCTG1 vs. G2: p=NS (data NR)99
SF-20 subscale: Pain PerceptionRCTG1 vs. G2: p=NS (data NR)99
Helping to cope with a chronic condition (1 to 5)RCTG1 vs. G2 (N=590): ANCOVA-adjusted intervention effect=0.16, p>0.0586
Number of helpful discussion topics (0 to 5)RCTG1 vs. G2 (N=590): ANCOVA-adjusted intervention effect=−0.02, p>0.0586
Discussing treatment choices (1 to 4)RCTBetter score in G1 than G2: ANCOVA-adjusted intervention effect=0.26, p<0.0186
Communicating with providers (0 to 100)RCTBetter score in G1 than G2: ANCOVA-adjusted intervention effect=4.5, p<0.0586
Getting answers to questions quickly (0 to 100)RCTG1 vs. G2 (N=590): ANCOVA-adjusted intervention effect=5.0, p>0.0586
Multimorbidity Hassles score (0 to 24)RCTG1 vs. G2 (N=590): ANCOVA-adjusted intervention effect=−0.27, p>0.0586
Percentage receiving help setting goalsRCTG1 vs. G2 (N=590): ANCOVA-adjusted intervention effect=−5.6, p>0.0586
Percentage receiving help making a care planRCTG1 vs. G2 (N=590): ANCOVA-adjusted intervention effect=2.3, p>0.0586
Self-efficacy: Take all medications (1 to 5)RCTG1 vs. G2 (N=590): ANCOVA-adjusted intervention effect=0.05, p>0.0586
Self-efficacy: Plan meals and snacks (1 to 5)RCTG1 vs. G2 (N=590): ANCOVA-adjusted intervention effect=0.01, p>0.0586
Self-efficacy: Exercise 2 or 3 times weekly (1 to 5)RCTG1 vs. G2 (N=590): ANCOVA-adjusted intervention effect=0.11, p>0.0586
Self-care activities: Prescribed medications taken (mean # of days)RCTG1 vs. G2 (N=590): ANCOVA-adjusted intervention effect=−0.10, p>0.0586
Self-care activities: Followed healthy eating plan (mean # of days)RCTG1 vs. G2 (N=590), ANCOVA-adjusted intervention effect=−0.16, p>0.05
Self-care activities: 30 minutes of continuous physical activity (mean # of days)RCTG1 vs. G2 (N=590): ANCOVA-adjusted intervention effect=−0.05, p>0.0586

ADL = activities of daily living; ANCOVA = analysis of covariance; CI = confidence interval; DiD = difference-in-difference; G = group; HAM-D = Hamilton Depression Rating Scale; G = group; MHC = mental health composite; NR = not reported; NS = not statistically significant; OR = odds ratio; PHC = physical health composite; PHQ-2 = Patient Health Questionnaire-2; RCT = randomized controlled trial; SF = short form; vs. = versus.

Table B-58Clinical and functional outcomes for primary care–based model studies: Subgroup outcomes

Clinical and Functional OutcomesStudy DesignDifference Between Intervention Group (G1) and Comparison Group (G2)
HbA1c testRCTOriginal sample diabetes subgroup: DiD OR=0.99 (95% CI, 0.56 to 1.76)86
RCTRefresh sample diabetes subgroup: DiD OR=0.70 (95% CI, 0.27 to 1.84)86
LDL-C testRCTOriginal sample diabetes subgroup: DiD OR=0.85 (95% CI, 0.58 to 1.24)86
RCTRefresh sample diabetes subgroup: DiD OR=1.72 (95% CI, 0.86 to 3.42)86
RCTOriginal sample IVD subgroup: DiD OR=0.92 (95% CI, 0.63 to 1.33)86
RCTRefresh sample IVD subgroup: DiD OR=1.40 (95% CI, 0.76 to 2.58)86

CI = confidence interval; DiD = difference-in-difference; HbA1c = hemoglobin A1c; IVD = ischemic vascular disease LDL-C = low density lipoprotein cholesterol; OR = odds ratio; RCT = randomized controlled trial.

Table B-59Social risk outcomes for primary care–based model studies

Social Risk OutcomesStudy DesignDifference Between Intervention Group (G1) and Comparison Group (G2)
Overall well-beingObservationalG1 vs. G2: p=NR102

G = group; NR = not reported; vs. = versus.

Table B-60Study characteristics for home-based care interventions

First Author, Year, Site(s)Sample SizeStudy Design (Risk of Bias Assessment)Patient Selection: High Healthcare Use or Cost; Time PeriodPatient Selection: Chronic ConditionsPatient Selection: OtherAdditional Selected Patient Characteristics

McCall et al., 201084

CA, FL, TX

(Original N=16,077)

Intervention (N=11,516)

Comparison (N=4,561)

(COPD subgroup N=3,344)

Intervention (N=2,384)

Comparison (N=960)

(Diabetes subgroup N=4,502)

Intervention (N=3,223)

Comparison (N=1,279)

(IVD subgroup N=7,356)

Intervention (N=5,223)

Comparison (N=2,133)

(High-cost-only PBPM subgroup N=4,344)

Intervention (N=3,105)

Comparison (N=1,239)

(High-cost and high-risk PBPM subgroup N=6,802)

Intervention (N=4,845)

Comparison (N=1,957)

RCT (RoB: some concerns)High costs with top 5% of costs and 2+ hospitalizations in past 1 year1+ diagnosis from a list, such as heart failure; HCC score was ≥2.75 OR if had an HCC score <2.75, had a diagnosis of selected clinical conditions including peripheral vascular disease, ischemic heart disease, hypertensive heart and/or kidney disease, heart failure, chronic obstructive pulmonary disease (COPD), and asthmaMedicare FFS beneficiaries

Nonwhite: 27%

Mean HCC: 2.8

McCall et al., 201084

CA, FL, TX

(Refresh N=18,344)

Intervention (N=13,104)

Comparison (N=5,240)

(COPD subgroup N=4,735)

Intervention (N=3,393)

Comparison (N=1,342)

(Diabetes subgroup N=5,950)

Intervention (N=4,199)

Comparison (N=1,751)

(IVD subgroup N=7,554)

Intervention (N=5,384)

Comparison (N=2,170)

(High-cost-only PBPM subgroup N=1,414)

Intervention (N=1,027)

Comparison (N=387)

(High-cost and high-risk PBPM subgroup N=8,598)

Intervention (N=6,142)

Comparison (N=2,456)

RCT (RoB: some concerns)2+ hospitalizations in past 1 yearHCC score was >2.749Medicare FFS beneficiaries; excluded beneficiaries with drug/alcohol psychosis or dependence, major depressive, bipolar, and paranoid disorders; institutionalized in last 3 months of previous year; had a hospital claims where discharge date was equal to admission date

Nonwhite: 40%

Mean HCC: 3.8

Kimmy et al., 2019103

National: 14 practices

(Home-based care N=181,001)

Intervention (N=30,324)

Comparison (N=150,677)

Observational study (RoB: some concerns)Hospitalization and use of acute or subacute rehabilitation services; and 1+ home visit from the IAH practice in past 1 year2+ chronic conditions 2+ ADLs that require human assistance, new to home-based primary care (2+ E&M visits from a primary care clinician in the home or an assisted living facility during the 6-month period starting with the first home visit), majority of E&M visits from a primary care clinician during the same period must have taken place in the home or assisted living facilityMedicare FFS beneficiaries, new patients receiving home-based primary care who were IAH eligible and lived in an area served by an IAH practice

Mean HCC : 3.686

Chronically critically ill/medically complex: 29.1%

Depression: 31.9%

Valluru et al., 2019131

Philadelphia, PA; Richmond, VA; Washington, D.C.: 3 sites

(N=1,376)

IAH patients at 3 sites

(N=721)

Comparison with home-based care

(N=82)

Comparison without home-based care

(N=573)

Observational study (ROB: high)Nonelective hospitalization and post-acute care use, either skilled home care or skilled nursing facility, in past year2+ chronic conditionsEnrollment in 1 of 3 sites, FFS Medicare, score of 6+ in JEN Frailty Index

Percentage in age range:

<75: 31%

75–84: 30%

≥85: 39%

African American: 62%

Medicaid: NR

Medicare: 100%

Mean HCC: 3.58

ADL = activities of daily living; CA = California; COPD = chronic obstructive pulmonary disease; E&M = evaluation and management; FFS = fee-for-service; FL = Florida; HCC = hierarchical condition category; IAH = Independence at Home; IVD = ischemic vascular disease; N = number; NR = not reported; OR = odds ratio; PA = Pennsylvania; PBPM = per beneficiary per month; RCT = randomized controlled trial; RoB = risk of bias; TX = Texas, VA = Virginia.

Table B-61Intervention characteristics for home-based models

First Author, Year, Site(s)Intervention: Brief DescriptionIntervention DurationAssessment, Education, Skills, MonitoringCoordination and Continuity of CareReferral to/Linkages to Community-Based Support ServicesProvidersMode of Delivery Setting(S)IntensityComparison

McCall et al., 201084

CA, FL, TX

Evaluation of Medicare Care Management for High Cost Beneficiaries (CMHCB) Demonstration: Care Level Management (CLM) home-based primary care for patients with multiple chronic conditionsUp to 29 months for original population, up to 18 months for refresh populationAssessment tool on patient acuity: number of admissions and emergency room visits within the last 6 months, presence of unmet social and emotional needs, expected number of PVP visits needed in the next 30 days, and presence of compliance, psychiatric, or ongoing home health issuesProvided home-based care and 24/7 access to a PVP to patients; care management addressed adherence to treatment regimens, coordination of care services, end-of-life planning, home safety, socioeconomic issues, psychosocial issues, and medication managementHelped selected beneficiaries receive services from community-based ancillary services as neededPVPs, NPs to support PVPs, nurse care managers as patient advocates and care coordinators (PVPs were adjuncts to patients’ PCP)Face-to-face and by phone12% had no contact, 75% of beneficiaries had one or more physician visits, 22% had 10 or more visits, and 14% had 20 or more visits. 88% of beneficiaries received a telephone call from a nurse or physician, while 24% received 10 or more calls, and 39% of beneficiaries received 20 or more callsUsual care

Kimmy et al., 2019103

Effect of home-based primary care

National: 14 practices

Practices may earn an additional payment if their chronically ill, functionally limited patients’ Medicare expenditures are below an estimated spending target and if the practice meets required standards for a set of quality measuresUp to 4 yearsClinicians are available at all hours of the day; carry out individualized care plans; and use electronic health information systems, remote monitoring, and mobile diagnostic technologyReport on other measures, including fall risk assessments and depression screenings, to promote the provision of such careSome practices added social workers or other staff to coordinate care for their patients with other organizationsPhysicians or nurse practitioners; team may have also included physician assistants, clinical staff, and other health and social services staffFace-to-faceClinicians made 3–15 home visits per day, varied by siteUsual care: Patients who did not receive primary care in the home during the 6 months after their index date

Valluru et al., 2019131

Philadelphia, PA; Richmond, VA; Washington, DC: 3 sites

Home-based primary care integrated with long-term service supports (LTSS)36 monthsNRIntegrated care coordination with community supports including adult day healthcare and home health aide-provided personal care servicesAssistance with meals and transportation and social workers who collaborate with various community LTSS resourcesCare managers, case managers, or social workers depending on the siteFace-to-face in patient homeNRUsual care: Home-based care without long-term services and supports or no home-based care

CA = California; CLM = care level management; CMHCB = Care Management for High Cost Beneficiaries; DC = District of Columbia; FL = Florida; LTSS = long-term services and supports NP = nurse practitioner; NR = not reported; PA = Pennsylvania; PCP = primary care provider; PVP = personal visiting physician; TX = Texas; VA = Virginia.

Table B-62Healthcare utilization outcomes for home-based care model studies

Utilization MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
Inpatient admissions, all causeRCTOriginal sample DiD: IRR=0.94 (95% CI, 0.87 to 1.01)84
RCTRefresh sample DiD: IRR=0.94 (95% CI, 0.88 to 1.00)84
ObservationalNRNRLower reduction in G1 than G2: home-based care DiD=0.05 (90% CI, 0.01 to 0.09)103
Inpatient admissions, any all cause (%)RCTOriginal sample DiD: OR=1.05 (95% CI, 0.93 to 1.18)84
RCTRefresh sample DiD: OR=.93 (95% CI, 0.85 to 1.03)84
Inpatient admissions, ACSCRCTGreater reduction in G1 than G2: original sample DiD: IRR=0.86 (95% CI, 0.76 to 0.97)84
RCTGreater reduction in G1 than G2: refresh sample DiD: IRR=0.89 (95% CI, 0.81 to 0.99)84
ObservationalNRNRHome-based care sample DiD=0.00 (90% CI, −0.02 to 0.02)103
Inpatient admissions, any ACSC (%)RCTGreater reduction in G1 than G2: original sample DiD: OR=0.87 (95% CI, 0.77 to 0.99)84
RCTGreater reduction in G1 than G2: refresh sample DiD: OR=.90 (95% CI, 0.81 to 1.00)84
ED visits, all causeRCTOriginal sample DiD: IRR=0.88 (95% CI, 0.69 to 1.12)84
RCTRefresh sample DiD: IRR=0.95 (95% CI, 0.85 to 1.07)84
ObservationalNRNRHome-based care DiD=0.00 (90% CI, −0.04 to 0.05) (p>0.10)103
ED visits, ACSCRCTOriginal sample DiD: IRR=0.89 (95% CI, 0.66 to 1.18)84
RCTRefresh sample DiD: IRR=1.06 (95% CI, 0.87 to 1.30)84
ObservationalNRNRHome-based care DiD=0.00 (90% CI, −0.02 to 0.01) (p>0.10)103
Long-term institutionalization rateObservationalNRNRG1: 8.1%, G2 with home-based care: 17.7%, G2 without home-based care: 16.4%: p<0.05131

⬆ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

⬇ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was statistically significant.

⇧ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

⇩ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

ACSC = ambulatory care sensitive conditions; CI = confidence interval; DiD = difference-in-difference; ED = emergency department; G = group; IRR = incidence rate ratio; NR = not reported; OR = odds ratio; RCT = randomized controlled trial.

Table B-63Strength of evidence for home-based care models versus usual-care outcomes

PopulationOutcomeResultsStudy Design and Sample SizeStrength of Evidence DomainsOverall Evidence Strength (Direction of Effect)
HNHC patientsED visits, all cause

CLM original sample DiD: IRR=0.88 (95% CI, 0.69 to 1.12)84

CLM refresh sample DiD: IRR=0.95 (95% CI, 0.85 to 1.07)84

IAH home-based care: higher use in G1 than G2: DiD=0.00 (90% CI, −0.04 to 0.05), (p>0.10)103

2 RCTs, N=34,421

1 OBS, N=181,246

Moderate study limitations, imprecise, inconsistent, directInsufficient
HNHC patientsED visits, ACSC

CLM original sample DiD: IRR=0.89 (95% CI, 0.66 to 1.18)84

CLM refresh sample DiD: IRR=1.06 (95% CI, 0.87 to 1.30)84

IAH home-based care sample DiD=0.00 (90% CI, −0.02 to 0.01, p>0.10)103

2 RCTs, N=34,421

1 OBS, N=181,246

Moderate study limitations, inconsistent, imprecise, directInsufficient
HNHC patientsInpatient admissions, all cause

CLM original sample: IRR=0.94 (95% CI, 0.87 to 1.01)84

CLM refresh sample: IRR=0.94 (95% CI, 0.88 to 1.00);84

IAH home-based care: lower reduction in G1 than G2: DiD=0.05 (90% CI, 0.01 to 0.09)103

2 RCTs, N=34,421

1 OBS, N=181,246

Moderate study limitations, imprecise, inconsistent, directInsufficient
HNHC patientsInpatient admissions, ACSC

CLM original sample: DiD: IRR=0.86 (95% CI, 0.76 to 0.97)84

CLM refresh sample: DiD: IRR=0.89 (95% CI, 0.81 to 0.99)84

IAH home-based care sample: DiD=0.00 (90% CI, −0.02 to 0.02)103

2 RCTs, N=34,421

1 OBS, N=181,246

Moderate study limitations, imprecise, consistent, directLow (Favorable)
HNHC patientsInpatient admissions, any all cause (%)

CLM original sample: DiD: OR=1.05 (95% CI, 0.93 to 1.18)84

CLM refresh sample: DiD: OR=0.93 (95% CI, 0.85 to 1.03)84

2 RCTs, N=34,421Moderate study limitations, imprecise, inconsistent, directInsufficient
HNHC patientsInpatient admissions, any ACSC (%)

CLM original sample: DiD: OR=0.87 (95% CI, 0.77 to 0.99)84

CLM refresh sample: DiD: OR=0.90 (95% CI, 0.81 to 1.00)84

2 RCTs, N=34,421Moderate study limitations; precise, consistent, directLow (Favorable)
HNHC patientsTotal cost

CLM original sample DiD=41 (p>0.05);84 CLM refresh sample DiD=−29 (p>0.05)84

IAH home-based care sample DiD=451 (90% CI, 342.4 to 559.6, p<0.10)103

2 RCTs, N=34,421

1 OBS, N=181,246

Moderate study limitations, inconsistent, imprecise, directInsufficient
High-cost, high risk patient subgroupTotal costCLM original sample: DiD=107 (p>0.05)84 CLM refresh sample DiD=−21 (p>0.05)842 RCTs, N=15,400Moderate study limitations, inconsistent, imprecise, directInsufficient
High-cost only patient subgroupTotal costCLM original sample DiD=−170 (p>0.05)84 CLM refresh sample: DiD=−236 (p>0.05)842 RCTs, N=5,758Moderate study limitations, consistent, imprecise, directInsufficient
HNHC patientsMortality rate

CLM original sample: diff in mean rate=0.4, (p=0.63)84

CLM refresh sample: Diff in mean rate=0.1 (p=0.88)84

IAH LTSS: G1 vs. G2 with HBC and G2 without HBC=NR (p>0.05)131

2 RCTs, N=34,421

1 OBS, N=1,376

Moderate study limitations, consistent, imprecise, directLow (No difference)
HNHC patientsInfluenza vaccine

CLM original sample: DiD OR=1.15 (95% CI, 1.02 to 1.30)84

CLM refresh sample: DiD OR=1.15 (95% CI, 1.03 to 1.27)84

2 RCTs, N=34,421Moderate study limitations, consistent, precise, directLow (Favorable)
HNHC patients COPD subgroupOxygen saturation test

CLM original sample: DiD OR=1.02 (95% CI, 0.77 to 1.34)84

CLM refresh sample: DiD OR=0.97 (95% CI, 0.77 to 1.22)84

2 RCTs, N=8,079Moderate study limitations, inconsistent, imprecise, directInsufficient
HNHC patients Diabetes subgroupHbA1c test

CLM original sample: DiD OR=0.91 (95% CI, 0.74 to 1.13)84

CLM refresh: DiD OR=0.98 (95% CI, 0.82 to 1.18)84

2 RCTs, N=10,452Moderate study limitations, consistent, imprecise, directInsufficient
HNHC patients Diabetes subgroupLDL-C test

CLM original sample: DiD OR=0.92 (95% CI, 0.75 to 1.12)84

CLM refresh sample: DiD OR=0.97 (95% CI, 0.77 to 1.22)84

2 RCTs, N=10,452Moderate study limitations, consistent, imprecise, directInsufficient
IVD patient SubgroupLDL-C test

CLM original sample: DiD OR=0.89 (95% CI, 0.76 to 1.05)84

CLM refresh sample: DiD OR=0.95 (95% CI, 0.81 to 1.11)84

2 RCTs, N=14,910Moderate study limitations, consistent, imprecise, directInsufficient

ACSC = ambulatory care sensitive conditions; CI = confidence interval; CLM = care level management; COPD = chronic obstructive pulmonary disease; DiD = difference-in-difference; ED = emergency department; G = group; HbA1c = hemoglobin A1c; HBC = home-based care; HNHC = high-need, high-cost; IAH = Independence at Home; IRR = incidence rate ratio; IVD = ischemic vascular disease; LDL-C = low-density lipoprotein cholesterol; LTSS = long-term services and supports; N = number; NR = not reported; OBS = observational study; OR = odds ratio; RCT = randomized controlled trial; vs. = versus.

Table B-64Cost outcomes for home-based care model studies

Cost MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
Total costRCTOriginal sample DiD=41 (SE: 67.9) (p>0.05)84
RCTRefresh sample DiD=−29 (SE: 73.1) (p>0.05)84
ObservationalNRNRGreater increase in G1 than G2: home-based care DiD=$451 (SE: 66) (90% CI, 342.4 to 559.6)103

⇧ = Increase in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

⇩ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

CI = confidence interval; DiD = difference in difference; G = group; NR = not reported; NS = not statistically significant; RCT = randomized controlled trial; SE = standard error.

Table B-65Cost outcomes for home-based care model studies: Subgroup outcomes

Cost MeasuresStudy DesignDirection of Change in Intervention Group (G1)Direction of Change in Comparison Group (G2)Difference
Total costRCTOriginal sample high-cost, high-risk subgroup DiD=107 (SE: 127.6) (p>0.05)84
RCTRefresh sample high-cost, high-risk subgroup DiD=−21 (SE: 121.3) (p>0.05)84
RCTOriginal sample high-cost-only subgroup DiD=−170 (SE: 104.0) (p>0.05)84
RCTRefresh sample high-cost-only subgroup DiD=−236 (SE: 197.5) (p>0.05)84

⇩ = Reduction in the outcome between the intervention period and the baseline period; the difference between the intervention and comparison groups was not statistically significant.

DiD = difference-in-difference; G = group; RCT = randomized controlled trial; SE = standard error.

Table B-66Clinical and functional outcomes for home-based care model studies

Clinical and Functional OutcomesStudy DesignDifference Between Intervention Group (G1) and Comparison Group (G2)
Mortality rateRCTOriginal sample: difference in mean rates=0.4, (p=0.63)84
RCTRefresh sample: difference in mean rates=0.1 (p=0.88)84
OBSG1: 35.8%, G2 with home-based care: 24.9%, G2 without home-based care: 26.9%: p>0.05131
Influenza vaccineRCTDiD greater in G1 than G2: original sample: DiD OR=1.15 (95% CI, 1.02 to 1.30)84
RCTDiD greater in G1 than G2: refresh sample: DiD OR=1.15 (95% CI, 1.03 to 1.27)84
PHC score (physical health)RCTGreater in G1 than G2: ANCOVA-adjusted IE=2.1 (p<0.0584)
MHC score (mental health)RCTANCOVA-adjusted IE=1.7 (p>0.0584)
PHQ-2 score (depression)RCTANCOVA-adjusted IE=0.26 (p>0.0584)
Number of activities of daily living (ADLs) difficult to doRCTANCOVA-adjusted IE=0.03 (p>0.0584)
Number ADLs receiving helpRCTANCOVA-adjusted IE=0.00 (p>0.0584)
Helping to cope with a chronic conditionRCTANCOVA-adjusted IE=0.19 (p>0.0584)
Number of helpful discussion topicsRCTANCOVA-adjusted IE=0.20 (p>0.0584)
Discussing treatment choicesRCTGreater in G1 than G2: ANCOVA-adjusted IE=0.23 (p<0.0584)
Communicating with providersRCTGreater in G1 than G2: ANCOVA-adjusted IE=6.55 (p<0.0184)
Getting answers to questions quicklyRCTANCOVA-adjusted IE=4.90 (p>0.0584)
Multimorbidity Hassles scoreRCTANCOVA-adjusted IE=−0.44 (p>0.0584)
Percentage receiving help setting goalsRCTANCOVA-adjusted IE=6.1 (p>0.0584)
Percentage receiving help making a care planRCTANCOVA-adjusted IE=3.9 (p>0.0584)
Self-efficacy: Take all medicationsRCTANCOVA-adjusted IE=0.20 (p>0.0584)
Self-efficacy: Plan meals and snacksRCTANCOVA-adjusted IE=0.15 (p>0.0584)
Self-efficacy: Exercise 2 or 3 times weeklyRCTANCOVA-adjusted IE=0.20 (p>0.0584)
Self-care activities: Prescribed medications takenRCTANCOVA-adjusted IE=0.06 (p>0.0584)
Self-care activities: Followed healthy eating planRCTANCOVA-adjusted IE=−0.03 (p>0.0584)
Self-care activities: 30 minutes of continuous physical activityRCTGreater in G1 than G2: ANCOVA-adjusted IE=0.63 (p<0.0584)

ADL = activities of daily living; ANCOVA = analysis of covariance; CI = confidence interval; DiD = difference-in-difference; G = group; IE = intervention effect; MHC = mental health composite; OR = odds ratio; PHC = physical health composite; PHQ-2 = patient health questionnaire-2; RCT = randomized controlled trial.

Table B-67Clinical and functional outcomes for home-based care model studies: Subgroup outcomes

Clinical and Functional OutcomesStudy DesignDifference Between Intervention Group (G1) and Comparison Group (G2)
Oxygen saturation testRCTOriginal sample COPD subgroup: DiD OR=1.02 (95% CI, 0.77 to 1.34)84
RCTRefresh sample COPD subgroup: DiD OR=0.97 (95% CI, 0.77 to 1.22)84
HbA1c testRCTOriginal sample diabetes subgroup: DiD OR=0.91 (95% CI, 0.74 to 1.13)84
RCTRefresh sample diabetes subgroup: DiD OR=0.98 (95% CI, 0.82 to 1.18)84
LDL-C testRCTOriginal sample diabetes subgroup: DiD OR=0.92 (95% CI, 0.75 to 1.12)84
RCTRefresh sample diabetes subgroup: DiD OR=0.97 (95% CI, 0.77 to 1.22)84
RCTOriginal sample IVD subgroup: DiD OR=0.89 (95% CI, 0.76 to 1.05)84
RCTRefresh sample IVD subgroup: DiD OR=0.95 (95% CI, 0.81 to 1.11)84

CI = confidence interval; COPD = chronic obstructive pulmonary disease; DiD = difference-in-difference; HbA1c = hemoglobin A1c; IVD = ischemic vascular disease; LDL-C = low-density lipoprotein-cholesterol; OR = odds ratio; RCT = randomized controlled trial.

Appendix B. Figures

Appendix Figure B-2 is titled “System-level transformation models versus usual care, total cost.” The figure demonstrates a pooled effect size in total annual cost between treatment and comparison groups of high need, high cost patients. The figure includes five samples of high need, high cost patients for four studies. The figure demonstrates no difference in annual total cost between the groups with a mean difference of −$5.41 with a 95% confidence interval of −38.28 to 49.10; and I2=44.6%.

Figure B-2System-level transformation models versus usual care, total cost

Appendix Figure B-3 is titled “Telephonic/mail models versus usual care, all-cause ED visits.” The figure demonstrates a pooled effect size in all-cause ED visits between treatment and comparison groups of high need, high cost patients. The figure includes four samples of high need, high cost patients for two studies. The figure demonstrates a pooled relative risk of 1.01 with a 95% confidence interval of 0.94 to 1.08; and I2=0%.

Figure B-3Telephonic/mail models versus usual care, all-cause ED visits

Appendix Figure B-4 is titled “Telephonic/mail models versus usual care, ACSC ED visits.” The figure demonstrates a pooled effect size in ACSC ED visits between treatment and comparison groups of high need, high cost patients. The figure includes four samples of high need, high cost patients for two studies. The figure demonstrates changes in ACSC ED visits to not be significantly different in these RCT samples with a pooled relative risk of 0.99 with a 95% confidence interval of 0.88 to 1.10; and I2=0%.

Figure B-4Telephonic/mail models versus usual care, ACSC ED visits

Appendix Figure B-5 is titled Telephonic/mail models versus usual care, all-cause inpatient admissions.” The figure demonstrates a pooled effect size in inpatient admissions between treatment and comparison groups of high need, high cost patients. The figure includes four samples of high need, high cost patients. The figure demonstrates changes in inpatient admissions to not be significantly different in these RCT samples with a pooled relative risk of 0.99 with a 95% confidence interval of 0.92 to 1.06; and I2=0%.

Figure B-5Telephonic/mail models versus usual care, all-cause inpatient admissions

Appendix Figure B-6 is titled Telephonic/mail models versus usual care, ACSC inpatient admissions” The figure demonstrates a pooled effect size in ACSC inpatient admissions between treatment and comparison groups of high need, high cost patients. The figure includes four samples of high need, high cost patients. The figure demonstrates changes in inpatient admissions to not be significantly different in these RCT samples with a pooled relative risk of 0.95 with a 95% confidence interval of 0.85 to 1.06; and I2=0%.

Figure B-6Telephonic/mail models versus usual care, ACSC inpatient admissions

Appendix Figure B-7 is titled Telephonic/mail models versus usual care, total cost.” The figure demonstrates a pooled effect size in total costs between treatment and comparison groups of high need, high cost patients. The figure includes seven samples of high need, high cost patients. The figure demonstrates no difference in total cost between the groups with a mean difference of −$8.52 with a 95% confidence interval of −130.02 to 112.98; and I2=22.4%.

Figure B-7Telephonic/mail models versus usual care, total cost

Appendix Figure B-8 is titled Telephonic/mail models versus usual care, mortality rate.” The figure demonstrates a pooled effect size in mortality rate between treatment and comparison groups of high need, high cost patients. The figure includes four samples of high need, high cost patients. The figure demonstrates no difference between groups in mortality rates with a mean difference of 0.34 with a 95% confidence interval of −1.06 to 1.74; and I2=0%.

Figure B-8Telephonic/mail models versus usual care, mortality rate

Appendix Figure B-9 is titled “Primary care models versus usual care, total cost.” The figure demonstrates a pooled effect size in total costs between treatment and comparison groups of high need, high cost patients. The figure includes three samples of high need, high cost patients. The figure demonstrates no difference in total annual cost between the groups with a mean difference of −$3,848.43 with a 95% confidence interval of −5,514.24 to −2,182.61; and I2=0%.

Figure B-9Primary care models versus usual care, total cost

Footnotes

a

Theory and guidance from National Academy of Medicine report: “Determining an ideal definition for a high-need patient requires a delicate balance. A highly constrained definition will risk missing people, potentially depriving them of needed resources. On the other hand, casting an overly broad definition might include people who are not high-need and do not need additional resources. Abrams noted that basing identification of high-need patients exclusively on cost will miss many people, and if the focus is exclusively on chronic conditions, a large number of people may be identified whose chronic conditions are under control.”12

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