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Table 1.  
Acute Care Intervention Descriptive Characteristics of Study Samplea
Acute Care Intervention Descriptive Characteristics of Study Samplea
Table 2.  
Community Intervention Descriptive Characteristics of Study Sample
Community Intervention Descriptive Characteristics of Study Sample
Table 3.  
Medicaid and Medicare Outcomes Associated With the Acute Care Interventiona
Medicaid and Medicare Outcomes Associated With the Acute Care Interventiona
Table 4.  
Medicaid and Medicare Outcomes Associated With the Community Interventiona
Medicaid and Medicare Outcomes Associated With the Community Interventiona
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Baltimore City Health Department. Supplementary maps: neighborhood health profiles 2008. http://health.baltimorecity.gov/sites/default/files/Supplementary%20maps3.pdf. Accessed October 1, 2018.
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Renders  CM, Valk  GD, Griffin  S, Wagner  EH, Eijk  JT, Assendelft  WJ.  Interventions to improve the management of diabetes mellitus in primary care, outpatient and community settings.  Cochrane Database Syst Rev. 2001;(1):CD001481.PubMedGoogle Scholar
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McAlister  FA, Lawson  FM, Teo  KK, Armstrong  PW.  A systematic review of randomized trials of disease management programs in heart failure.  Am J Med. 2001;110(5):378-384. doi:10.1016/S0002-9343(00)00743-9PubMedGoogle ScholarCrossref
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Bruce  ML, Raue  PJ, Reilly  CF,  et al.  Clinical effectiveness of integrating depression care management into Medicare home health: the Depression CAREPATH randomized trial.  JAMA Intern Med. 2015;175(1):55-64. doi:10.1001/jamainternmed.2014.5835PubMedGoogle ScholarCrossref
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Liaw  W, Moore  M, Iko  C, Bazemore  A.  Lessons for primary care from the first ten years of Medicare coordinated care demonstration projects.  J Am Board Fam Med. 2015;28(5):556-564. doi:10.3122/jabfm.2015.05.140322PubMedGoogle ScholarCrossref
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Brown  RS, Peikes  D, Peterson  G, Schore  J, Razafindrakoto  CM.  Six features of Medicare coordinated care demonstration programs that cut hospital admissions of high-risk patients.  Health Aff (Millwood). 2012;31(6):1156-1166. doi:10.1377/hlthaff.2012.0393PubMedGoogle ScholarCrossref
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Berkowitz  SA, Brown  P, Brotman  DJ,  et al; J-CHiP Program.  Case study: Johns Hopkins Community Health Partnership: a model for transformation.  Healthc (Amst). 2016;4(4):264-270. doi:10.1016/j.hjdsi.2016.09.001PubMedGoogle ScholarCrossref
8.
Hsiao  YL, Bass  EB, Wu  AW,  et al; on behalf of the J-CHiP program.  Implementation of a comprehensive program to improve coordination of care in an urban academic health care system.  J Health Organ Manag. 2018;32(5):638-657. doi:10.1108/JHOM-09-2017-0228PubMedGoogle ScholarCrossref
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NORC. Third Annual Report of the Health Care Innovation Award for the J-CHiP Program. https://downloads.cms.gov/files/cmmi/hcia-chspt-thirdannualrpt.pdf. Accessed October 1, 2018.
10.
NORC. Third Annual Report Addendum of the Health Care Innovation Award for the J-CHiP Program. https://downloads.cms.gov/files/cmmi/hcia-chspt-thirdannualrptaddendum.pdf. Accessed October 1, 2018.
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Hansen  LO, Young  RS, Hinami  K, Leung  A, Williams  MV.  Interventions to reduce 30-day rehospitalization: a systematic review.  Ann Intern Med. 2011;155(8):520-528. doi:10.7326/0003-4819-155-8-201110180-00008PubMedGoogle ScholarCrossref
12.
Everett  AS, Reese  J, Coughlin  J,  et al.  Behavioural health interventions in the Johns Hopkins Community Health Partnership: integrated care as a component of health systems transformation.  Int Rev Psychiatry. 2014;26(6):648-656. doi:10.3109/09540261.2014.979777PubMedGoogle ScholarCrossref
13.
Sisters Together and Reaching Inc website. http://www.sisterstogetherandreaching.org/. Accessed October 1, 2018.
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Men and Families Center website. http://www.menandfamiliescenter.org/. Accessed October 1, 2018.
15.
Maryland Health Services Cost Review Commission. New all-payer model for Maryland global budget development for FY 2014. http://www.hscrc.state.md.us/documents/md-maphs/wg-meet/pay/2014-03-13/global-budget-update-3-11-14.pdf. Published March 12, 2014. Accessed October 1, 2018.
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Murphy  SME, Hough  DE, Sylvia  ML,  et al.  Going beyond clinical care to reduce health care spending: findings from the J-CHiP community-based population health management program evaluation.  Med Care. 2018;56(7):603-609. doi:10.1097/MLR.0000000000000934PubMedGoogle ScholarCrossref
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Center for Medicare & Medicaid Services. Health Care Innovation Awards round one project profiles. https://innovation.cms.gov/files/x/hcia-project-profiles.pdf. Updated December 2013. Accessed October 1, 2018.
18.
Rajkumar  R, Patel  A, Murphy  K,  et al.  Maryland’s all-payer approach to delivery-system reform.  N Engl J Med. 2014;370(6):493-495. doi:10.1056/NEJMp1314868PubMedGoogle ScholarCrossref
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Berkowitz  SA, Ishii  L, Schulz  J, Poffenroth  M.  Academic medical centers forming accountable care organizations and partnering with community providers: the experience of the Johns Hopkins Medicine Alliance for Patients.  Acad Med. 2016;91(3):328-332. doi:10.1097/ACM.0000000000000976PubMedGoogle ScholarCrossref
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    Views 2,329
    Original Investigation
    Health Policy
    November 2, 2018

    Association of a Care Coordination Model With Health Care Costs and UtilizationThe Johns Hopkins Community Health Partnership (J-CHiP)

    Author Affiliations
    • 1Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
    • 2NORC at the University of Chicago, Bethesda, Maryland
    • 3Johns Hopkins HealthCare, Glen Burnie, Maryland
    • 4Johns Hopkins Health System, Baltimore, Maryland
    • 5Substance Abuse Mental Health Services Administration, Department of Health and Human Services, Washington, DC
    • 6Centers for Medicare & Medicaid Services, Baltimore, Maryland
    • 7Sisters Together and Reaching, Baltimore, Maryland
    • 8Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
    • 9Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
    • 10Men and Families Center, Baltimore, Maryland
    • 11Johns Hopkins Community Physicians, Baltimore, Maryland
    JAMA Netw Open. 2018;1(7):e184273. doi:10.1001/jamanetworkopen.2018.4273
    Key Points

    Question  Is the Johns Hopkins Community Health Partnership, a broad care coordination program inclusive of acute care and community interventions, associated with improved health outcomes?

    Findings  This quality improvement study found that the community intervention was associated with a statistically significant reduction in admissions, readmissions, and emergency department visits for Medicaid, but the utilization results were mixed for the acute care intervention. In terms of cost of care, there were statistically significant cost savings totaling $113.3 million.

    Meaning  A care coordination model in an urban academic center environment can be associated with improved outcomes, including substantial cost reduction.

    Abstract

    Importance  The Johns Hopkins Community Health Partnership was created to improve care coordination across the continuum in East Baltimore, Maryland.

    Objective  To determine whether the Johns Hopkins Community Health Partnership (J-CHiP) was associated with improved outcomes and lower spending.

    Design, Setting, and Participants  Nonrandomized acute care intervention (ACI) and community intervention (CI) Medicare and Medicaid participants were analyzed in a quality improvement study using difference-in-differences designs with propensity score–weighted and matched comparison groups. The study spanned 2012 to 2016 and took place in acute care hospitals, primary care clinics, skilled nursing facilities, and community-based organizations. The ACI analysis compared outcomes of participants in Medicare and Medicaid during their 90-day postacute episode with those of a propensity score–weighted preintervention group at Johns Hopkins Community Health Partnership hospitals and a concurrent comparison group drawn from similar Maryland hospitals. The CI analysis compared changes in outcomes of Medicare and Medicaid participants with those of a propensity score–matched comparison group of local residents.

    Interventions  The ACI bundle aimed to improve transition planning following discharge. The CI included enhanced care coordination and integrated behavioral support from local primary care sites in collaboration with community-based organizations.

    Main Outcomes and Measures  Utilization measures of hospital admissions, 30-day readmissions, and emergency department visits; quality of care measures of potentially avoidable hospitalizations, practitioner follow-up visits; and total cost of care (TCOC) for Medicare and Medicaid participants.

    Results  The CI group had 2154 Medicare beneficiaries (1320 [61.3%] female; mean age, 69.3 years) and 2532 Medicaid beneficiaries (1483 [67.3%] female; mean age, 55.1 years). For the CI group’s Medicaid participants, aggregate TCOC reduction was $24.4 million, and reductions of hospitalizations, emergency department visits, 30-day readmissions, and avoidable hospitalizations were 33, 51, 36, and 7 per 1000 beneficiaries, respectively. The ACI group had 26 144 beneficiary-episodes for Medicare (13 726 [52.5%] female patients; mean patient age, 68.4 years) and 13 921 beneficiary-episodes for Medicaid (7392 [53.1%] female patients; mean patient age, 52.2 years). For the ACI group’s Medicare participants, there was a significant reduction in aggregate TCOC of $29.2 million with increases in 90-day hospitalizations and 30-day readmissions of 11 and 14 per 1000 beneficiary-episodes, respectively, and reduction in practitioner follow-up visits of 41 and 29 per 1000 beneficiary-episodes for 7-day and 30-day visits, respectively. For the ACI group’s Medicaid participants, there was a significant reduction in aggregate TCOC of $59.8 million and the 90-day emergency department visit rate decreased by 133 per 1000 episodes, but hospitalizations increased by 49 per 1000 episodes and practitioner follow-up visits decreased by 70 and 182 per 1000 episodes for 7-day and 30-day visits, respectively. In total, the CI and ACI were associated with $113.3 million in cost savings.

    Conclusions and Relevance  A care coordination model consisting of complementary bundled interventions in an urban academic environment was associated with lower spending and improved health outcomes.

    Introduction

    The nearly 200 000 residents of East Baltimore, where life expectancy can be 20 years shorter than in more affluent communities nearby, face multiple challenges to their health and well-being.1 Many of these residents receive care at Johns Hopkins facilities, and there are challenges in coordinating care for patients, especially during transitions, related to both medical complexity and underlying social factors. We also know that effective care coordination can lead to improved health outcomes, especially for those of greatest need with chronic conditions.2-6 The Johns Hopkins Community Health Partnership (J-CHiP) is a care coordination initiative spanning the care continuum. It has 2 principal program components: (1) a bundle of interventions deployed in 2 acute care East Baltimore hospitals (Johns Hopkins Hospital [JHH] and Johns Hopkins Bayview Medical Center [JHBMC]), with additional focus on those discharged to local skilled nursing facilities (SNFs) and (2) a care management model embedded in ambulatory primary care sites located in the community. The Johns Hopkins Community Health Partnership was catalyzed by a Health Care Innovation Award (HCIA) from the Center for Medicare & Medicaid Innovation, a component of the Centers for Medicare & Medicaid Services, an agency of the US Department of Health & Human Services. Launched in July 2012, external funding for the acute care component (acute care intervention [ACI]) ended in June 2015, while funding for the community component (community intervention [CI]) ended in June 2016.7 It is estimated that more than 80 000 participants received services through the award. Previous articles, including a case study, have described the interventions.7,8 This article is a follow-up to a report on outcomes as assessed by NORC (formerly the National Opinion Research Center) at the University of Chicago, the independent evaluator for the J-CHiP HCIA award.9,10

    The ACI, an evidence-based bundle of services, focused on improving the coordination of care for all patients and included (1) early screen for discharge planning to predict service needs following discharge; (2) daily multidisciplinary unit-based rounds to review goals and priorities of care; (3) innovative patient education using tablet-based modules; (4) enhanced medication management, including the option of medications in hand at the time of discharge; (5) telephone follow-up after discharge by nurses staffing a patient access line; and (6) skilled home care, remote patient monitoring, and/or a skilled nurse transition guide for high-risk patients.11 Most of these services were deployed across 35 adult inpatient units at either JHH or JHBMC. An additional intervention comprising discharge and disease-based protocols was offered to a subset of patients discharged to 5 local SNFs.

    The CI, a care coordination model with integrated behavioral health care, was embedded at 8 ambulatory sites. The intervention used risk prediction models to identify and target Medicare and Medicaid (M/M) patients at greatest risk for future hospitalization. In the original rollout of the intervention, called J-CHiP Classic, multidisciplinary teams providing care coordination and community-based health workers (CHWs) were deployed to the neighboring community, working in close partnership with patients’ primary care practitioners. These CHWs focused on addressing patients’ barriers to care, often meeting patients at appointments and in their homes. Health behavioral specialists embedded in the care teams intervened around substance use or other psychiatric diagnoses.12 About 1 year into the program, the Tumaini (Swahili for hope) for health component was added to the intervention. This component was implemented by 2 community-based organizations, Sisters Together and Reaching13 and the Men and Families Center.14 Both organizations became key partners in delivering a complementary component of the CI. Sisters Together and Reaching hired and trained an additional nurse case manager and a team of CHWs to supplement those already trained by Johns Hopkins HealthCare, the managed care arm of Johns Hopkins Medicine. The Men and Families Center trained and deployed neighborhood-based support navigators, akin to block captains, in a single census tract to provide outreach and support services to all residents regardless of their insurance status.

    For recipients of the ACI or CI, we hypothesized that improved care coordination with team-based deployment would reduce avoidable health care utilization following discharge and in other circumstances, including hospital admissions, readmissions, and emergency department (ED) visits, and would subsequently lead to a reduction in total cost of care from the perspective of the Centers for Medicare & Medicaid Services.

    Methods
    Source of Data and Study Population

    Difference-in-differences (DID) study designs were used to study the association between the intervention and health care utilization in the ACI and CI groups. For the ACI, the preintervention period was for claims from January 1, 2011, to June 30, 2012, and the ramp-up period when the intervention was beginning to be implemented was from July 1, 2012, through March 31, 2013; the postintervention period began on April 1, 2013, and M/M claims data through June 30, 2015, were used for the analysis. For the CI, the preintervention period was the 2-year span (8 quarters) before the beneficiary enrolled in the intervention. Medicare claims data for the CI group were available through June 30, 2016, and we used a 90-day claims runoff date of March 31, 2016, for the analysis. Medicaid claims data for the CI group were available through March 31, 2016, with a 90-day claims runoff date of December 31, 2015. Evaluation efforts with respect to the J-CHiP effort were approved by the NORC institutional review board as well as by the Johns Hopkins University institutional review board. A waiver of consent was also granted by the Johns Hopkins institutional review board. This study adhered to the Standards for Quality Improvement Reporting Excellence (SQUIRE) 2.0 reporting guideline.

    Acute Care Intervention

    The acute care analysis compared the associated outcomes of M/M program participants during their 90-day postacute episode with those of a propensity score–weighted preintervention group at J-CHiP hospitals and with a comparison group drawn from similar hospitals in Maryland (Table 1). The Johns Hopkins Community Health Partnership provided an enrollment file of the acute care participants at JHH and JHBMC who were hospitalized in particular inpatient units, which were linked to fee-for-service M/M enrollment and claims data to obtain their demographic and health characteristics and analyze their utilization and cost outcomes. A pool of episodes from these same hospitals during the preintervention period served as the pretreatment group. For the comparison group, a pool of M/M fee-for-service episodes discharged from 3 similar hospitals in geographic proximity to JHH and JHBMC during the preimplementation and postimplementation periods were used. Comparison hospitals were chosen based on case mix and patient demographic characteristics and were all located in Maryland to account for the unique all-payer hospital payment model (eAppendix 1 in the Supplement). We expected that the global budgeting would have similar effects on all Maryland hospitals, but note that being an HCIA awardee, there may be unobserved hospital- and practitioner-level selection effects that are also correlated with outcomes.

    Community Intervention

    The analysis of the CI compares the changes in outcomes of M/M program participants before and after their enrollment in the intervention with those of a propensity score–matched comparison group (Table 2). We linked an intervention enrollment file of the CI participants to M/M enrollment and claims data in the preintervention and postintervention periods to obtain their demographic and health characteristics and to analyze their utilization and cost outcomes. As described previously,7 the J-CHiP team identified eligible Medicare and Medicaid patients aged 18 years or older with at least 1 chronic condition who received care at a designated primary care site. From this population, high-risk patients were identified based on their risk of future hospitalization. For the Medicare population, we screened 27 000 Medicare fee-for-service patients and calculated hospitalization risk using the ACG (adjusted clinical groups) score. For the Medicaid population, we screened approximately 53 000 Priority Partners Managed Care Organization Medicaid patients using a multivariable logistic regression model to augment the ACG hospitalization score with supplemented data (claims, health plan enrollment, health risk assessment, and lab data). For the comparison group, we analyzed claims to identify a pool of Medicare fee-for-service beneficiaries who lived in or near geographic proximity (defined by the 7 zip codes in East Baltimore where the participating ambulatory sites were located) and who had at least 1 evaluation and management visit to a practitioner within the time period of the community intervention to determine a pseudo–enrollment start date. This allowed us to control for contextual factors that could affect both the treatment and comparison groups. From this pool of potential comparison beneficiaries, selected beneficiaries had at least 1 evaluation and management visit to a practitioner during the year prior to the J-CHiP intervention start date.

    Outcome Measures

    We used claims-based measures for all outcomes, as electronic medical record data were not available for the comparison groups. Additionally, because both the ACI and CI did not target participants with particular clinical profiles (eg, those with diabetes or heart disease) but rather were designed to improve care coordination and care management for participants with broader medical and social needs, we used claims-based measures capturing utilization, quality, and cost of care that reflect these goals. Outcome measures were constructed for each quarter of the study period (eAppendix 2 and eTable in the Supplement). Utilization outcomes were coded as binary events, indicating whether or not the beneficiary had an event during the quarter. Total cost of care (to the payer) was constructed for each 90-day beneficiary-episode (ACI), or for each beneficiary quarter (CI). Utilization measures included all-cause hospital admissions, ED visits, and 30-day readmissions. In addition, we expected the ACI to affect timely receipt of practitioner follow-up (7 and 30 days) after hospital discharge. We assessed physician, nurse practitioner, and physician assistant follow-up visits following hospital discharge as a quality of care measure, as evidence has suggested that 7- and 30-day follow-up visits with practitioners may lead to higher-quality outcomes. This follow-up visit measure does not take into consideration phone calls made to patients via the patient access line after discharge. We expected the CI to have an impact on how participants received care coordination through medical and social supports, and therefore we assessed ambulatory care–sensitive conditions visits and potentially avoidable hospitalizations as quality measures. We computed the former for Medicare and the latter for Medicaid participants because the ambulatory care–sensitive conditions hospitalization measure requires the Present on Admission indicator, which was present on Medicare claims but absent on Medicaid claims. All cost estimates were based solely on data from M/M claims and did not include the cost of the intervention.

    Analytic Design

    For the ACI, participants were selected based on having an inpatient stay in a preintervention or postintervention period; the unit of analysis was therefore a beneficiary-episode. For the CI group, participants were selected based on being in the community (and enrolled in the program for the treatment group); therefore, the unit of analysis was the beneficiary. Race/ethnicity were determined and cataloged by claims history for both the ACI and CI. A DID approach was used to estimate the associations between the interventions and utilization and cost outcome measures in each quarter and over the entire preintervention and postintervention period (eAppendix 3 and eFigure 1 in the Supplement). As a DID model is valid when there are no differences in the preintervention period trend, we examined unadjusted data to assess parallel trends and included prior-year outcomes in the propensity score models. In this study, we report aggregate cost measures to show the total savings accrued to M/M over the intervention period. The aggregate measures are the net difference, which is calculated by summing the quarterly impacts weighted by the number of episodes or beneficiaries each quarter. We report quarterly utilization and quality outcomes (average number of events per quarter) per 1000 episodes (ACI) or per 1000 beneficiaries (CI). Quarterly utilization and quality estimates are a weighted average for all quarters in the intervention.

    Statistical Analysis
    Propensity Score Weighting and Matching

    For both the ACI and CI, we used propensity score models to minimize differences between the treatment and comparison groups (eAppendix 4, eFigure 2, eFigure 3, eFigure 4, eAppendix 5, eFigure 5, eFigure 6, and eFigure 7 in the Supplement). In the analysis of the ACI, the propensity score was estimated as the probability of a patient being enrolled in the intervention, conditional on the patient’s covariates. Discharges from both groups during the pretreatment and posttreatment comparison groups were assigned a weight based on the likelihood of being in the posttreatment intervention group. Table 1 shows the descriptive characteristics of the ACI populations after propensity score weighting. In the analysis of the CI, propensity score matching was used to find comparison group beneficiaries comparable to treatment beneficiaries. As with the ACI, the model predicted the likelihood of enrolling in the program. The common support (overlap in the distribution of scores) was then examined, and differences in demographic and health measures in the weighted or matched treatment and comparison groups were assessed to ensure sufficient comparability. Table 2 shows the descriptive characteristics of the CI populations after propensity score matching.

    Statistical Tests Used

    The primary statistical tests for the research question on impact were generalized linear regression models with a gamma distribution or 2-part models with the best-fitting distribution for cost measures, and logit link for binary outcomes, with a prespecified confidence interval of 90% (in accordance with Center for Medicare & Medicaid Innovation guidance). P values were noted at P < .10 (P < .10, P < .05, P < .01) and were 2-sided.

    For some cost categories in the acute care Medicare analyses, 2-part models with the best-fitting distributional form were used. Regression adjustment variables included age category, race/ethnicity, sex, prior-year hospitalizations and cost, dual-eligibility indicator, health risk scores, an indicator of end-stage renal disease, and disability. In the ACI, we also included prior-year hospitalizations and cost and discharge disposition.

    Using the regression coefficients, we estimated predicted probabilities; all analyses were conducted in Stata statistical software version 14.0 (StataCorp).

    Results
    Acute Care Intervention

    For the ACI, there were 26 144 beneficiary-episodes for Medicare (13 726 [52.5%] female patients; mean patient age, 68.4 years) and 13 921 beneficiary-episodes for Medicaid (7392 [53.1%] female patients; mean patient age, 52.2 years). For Medicare, the ACI was associated with a statistically significant reduction in aggregate cost of care of $29.2 million ($1115 per beneficiary-episode) with increases in 90-day hospitalization and 30-day readmission of 11 and 14 per 1000 beneficiary-episodes, respectively, and reduction in practitioner follow-up visits of 41 and 29 per 1000 beneficiary-episodes for 7-day and 30-day visits, respectively. For Medicaid, the statistically significant reduction in aggregate cost of care was $59.8 million ($4295 per beneficiary-episode), and 90 day ED visit rates were reduced by 133 per 1000 beneficiary-episodes while the hospitalization rate was increased by 49 per 1000 beneficiary-episodes and the practitioner follow-up visits were reduced by 70 and 182 per 1000 beneficiary-episodes for 7-day and 30-day visits, respectively (Table 3). Savings for the Medicaid population were associated with relative reductions in outpatient care and acute care inpatient costs. For the Medicare population, reductions in total cost of care were largely associated with relative reductions in SNF expenses, although there were other contributors as well.

    Community Intervention

    A total of 2154 Medicare beneficiaries (1320 [61.3%] female; mean age, 69.3 years) and 2532 Medicaid beneficiaries (1483 [67.3%] female; mean age, 55.1 years) received the CI. For Medicaid, statistically significant aggregate total cost-of-care reduction associated with the CI was $24.4 million (average savings of $1643 per beneficiary per quarter), and reductions of hospitalizations, ED visits, and 30-day readmissions were 33, 51, and 36 per 1000 beneficiaries, respectively, and there was a reduction of avoidable hospitalizations by 7 per 1000 beneficiaries. There were no statistically significant findings for Medicare (Table 4). In total, the CI and ACI were associated with $113.3 million in cost savings.

    Discussion

    The J-CHiP program, consisting of multiple complementary and intersecting bundles of strategies deployed within an urban academic environment, was associated with desired outcomes in many utilization, quality, and costs measures for high-risk M/M beneficiaries in East Baltimore by improving the coordination of services across the health care continuum. This was particularly notable with respect to cost savings, and the results showed a statistically significant total cost of care reduction in the ACI for both M/M populations and in the CI for the Medicaid population. Greater aggregate cost savings and lower utilization were found among the Medicaid population than the Medicare population for both the ACI and CI (of note, results of utilization were examined as counts rather than binary events and conclusions were consistent with the binary models).

    With respect to the ACI and the Medicaid population, there was a statistically significant increase in hospitalizations, a reduction in ED visits, and an overall statistically significant reduction in total aggregate costs related to reduced acute inpatient and outpatient costs. This suggests the reduced ED visit rate, as well as possibly less intensive acute care resource use during a hospitalization and/or postacute costs, may have contributed to the overall decreased costs. For example, if a hospitalization was required, it was less expensive, likely because of the increased emphasis on early discharge planning, in particular for those with greatest coordination needs. With respect to the ACI and the Medicare population, although hospital use and readmissions increased, there was an overall statistically significant reduction in total aggregate costs, which was likely driven at least in part by a reduction in SNF costs.

    Other measures not used in this evaluation (eg, measures of procedures, tests, physician and other staff costs, and community care) may be useful to consider for future impact assessment. However, because of the nature of the evaluation, which required assessment of 107 awardees in total, evaluators used a focused and consistent set of measures; more specific tailoring of outcomes was not within the study’s scope. Finally, it is notable that even with the focus on services following discharge, the results show reductions in practitioner follow-up visits. Because transitional and follow-up care provided by transition guides or care coordinators after discharge was an important intervention component and would not be considered a follow-up practitioner visit, those patients who did not then attend their scheduled follow-up appointment may have done so either because they could not arrange travel or thought it was not needed. The follow-up visits measure reflects completed, and not scheduled, visits.

    As mentioned, the cost analyses show that the greatest cost reduction for the Medicare population receiving the ACI was associated with a reduction in SNF utilization. Although related, this was distinct from our additional DID analyses presented in our report that focused on the patients discharged from JHH and JHBMC to 5 partner SNFs near JHH and JHBMC in comparison with those discharged to other SNFs in the same market area.9 While we did not find relative savings or lower utilization in this subgroup analysis,9 we do observe herein significantly lower SNF costs for those receiving the overall ACI for the Medicare population.

    The CI showed a statistically significant reduction in costs, admissions, and ED visits for the Medicaid population, but not for the Medicare population. While there was a trend toward cost reduction for the Medicare population in the final year, this changed in the final quarter, netting a nonsignificant increase in overall costs. The disparate outcomes between M/M patients may reflect the CI focus on patients’ social determinants of health—such as whether the patient can obtain medications, food, or housing—described previously7 as a greater need among the Medicaid population. In this context, it is worth considering previously reported findings of subgroup DID analyses of the CI that showed similar outcomes for both the J-CHiP Classic and Tumaini models as well as for patients who differed in their frequency of receiving care coordination services (receiving program staff contact each enrollment quarter vs otherwise).9

    In addition to our analyses of claims-based outcomes, our evaluation also assessed quality of care using survey data for both clinicians and CHWs.9 The Johns Hopkins Community Health Partnership fielded a modified Consumer Assessment of Healthcare Providers and Systems survey for J-CHiP participants in the CI (but who may also have been in the ACI). While these data on patient experience were not collected from the comparison beneficiaries and cannot be attributed solely to J-CHiP, the data provide insight on the process of care delivery. The high survey scores indicate that beneficiaries experienced high levels of quality of care. Respondents reported that their health care practitioner explained things clearly (99%), listened carefully (95%), showed respect (99%), provided easy-to-understand instructions (98%), knew their medical history (95%), and spent enough time with them (98%). Likewise, respondents reported good communication with CHWs, who explained things in a way that was easy to understand (95%), listened carefully (91%), and showed respect for what patients had to say (94%). About 78% of respondents noted that in the previous 12 months they had a discussion with someone in the practitioner’s office about specific goals for their health. Overall, J-CHiP community respondents were extremely satisfied with their practitioners. On a scale of 0 to 10, with 10 being the best, J-CHiP patients rated their health care practitioner an average of 8.9 and rated their trust of their CHW an average of 9.1. About 92% of respondents said they would recommend the CHW to their family and friends.9

    Limitations

    There were several limitations to this study. Although the ACI was designed to be all-payer, the evaluation focused only on the M/M populations due to data availability. For the CI, enrollment for the comparison group was based on the patient having an evaluation and management visit on the claim; as a result, while both groups have similar baseline utilization and costs, the comparison group, by definition, was as likely, if not more likely, to get care at the time of enrollment as J-CHiP’s participants. It is unclear how selection of the comparison group based on realized ambulatory care may bias the results. In addition, Maryland hospitals where beneficiaries received care were participating in health care delivery reforms such as the Maryland All-Payer Model, which began in January 2014 (midway through the HCIA award period) and involved hospital global budgeting efforts.15 The purposeful selection by NORC of Maryland hospitals for the comparison group should minimize the impact of these state reform activities; however, it is possible that the response of each hospital was different, which could bias the results in ways we cannot measure. We acknowledge that unobserved differences in socioeconomic characteristics between Medicare participants in the CI group and the comparison group may bias results toward the null. Even though comparison beneficiaries typically reside in the same general zip code vicinity as those participating in the CI, they are likely to differ on unobserved socioeconomic characteristics. In contrast, our Medicaid CI findings are unbiased, as both comparison and intervention beneficiaries have similar socioeconomic characteristics by virtue of their eligibility for Medicaid. Also, we do not include the cost of the intervention, as this would require a cost-benefit analysis that is beyond the scope of the evaluation and would have necessitated a better understanding of the full scope of associated benefits as well as of costs associated with in-kind contributions. For example, a cost-benefit analysis would entail a collaborative agreement between the J-CHiP implementation team and Center for Medicare & Medicaid Innovation to monetize the return on investment with respect to both direct benefits (beneficiary health, staff training) and indirect benefits (quality of life, improved employment due to better functioning).

    In addition to results reported here, there are additional J-CHiP evaluation efforts.16 With respect to the ACI, the analysis herein included a focus on intervention units, while additional, currently unpublished analyses look more broadly at the entire target population. The NORC analysis included comparison discharges from selected hospitals in Maryland, while the additional analysis focuses on intrahospital comparisons with yet-to-be-deployed units at the same hospitals. With respect to the CI, this analysis focused on community participants who were touched by a care manager across the entire program duration, while a recently published analysis studied a broader population, including those who may have only received a CHW intervention.

    The J-CHiP award was for $19.9 million and included additional institutional investment in these interventions. Statistically significant cost savings achieved by the Centers for Medicare & Medicaid Services were approximately $113.3 million. The initially projected cost savings estimate for the award from the application in 2011 was $52.6 million.17 Overall, this suggests a very favorable outcome in terms of cost savings. Nearly all aspects of the J-CHiP award have subsequently been sustained through either state-based initiatives related to the Maryland All-Payer Model18 or other initiatives such as the Johns Hopkins HealthCare Medicaid managed care plan or the JHM accountable care organization.19

    Conclusions

    In summary, the evaluation of the J-CHiP program suggests that a care coordination model that includes separate but complementary bundles of intervention strategies in an urban academic environment can be associated with dramatic improvements in many key utilization and cost indices. However, it is worth noting that the size of the effect is likely not wholly generalizable, as other efforts to implement such care delivery transformations will reflect investments made by the organizations, baseline health and utilization of patients served, and the communities in which they reside. State and federal efforts should continue to focus on best practices, such as those used by J-CHiP, to achieve improvements in health outcomes.

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    Article Information

    Accepted for Publication: September 3, 2018.

    Published: November 2, 2018. doi:10.1001/jamanetworkopen.2018.4273

    Correction: This article was corrected on November 21, 2018, to fix an error in the Author Affiliations.

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2018 Berkowitz SA et al. JAMA Network Open.

    Corresponding Author: Scott A. Berkowitz, MD, MBA, Johns Hopkins University School of Medicine, 733 N Broadway, Miller Research Bldg G46, Baltimore, MD 21205 (sberkow3@jhmi.edu).

    Author Contributions: Drs Parashuram and Rowan had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Berkowitz, Parashuram, Andon, Bellantoni, Deutschendorf, Dunbar, Durso, Everett, Giuriceo, Hebert, Howell, Leung, Lyketsos, Novak, Purnell, Sylvester, Zollinger, Rothman, Brown.

    Acquisition, analysis, or interpretation of data: Parashuram, Rowan, Andon, Bass, Bellantoni, Brotman, Deutschendorf, Durso, Hickman, Hough, Howell, Huang, Lepley, Leung, Lu, Lyketsos, Murphy, Wu, Zollinger, Koenig, Ahn.

    Drafting of the manuscript: Berkowitz, Parashuram, Rowan, Giuriceo, Hebert, Huang, Lepley, Wu, Zollinger.

    Critical revision of the manuscript for important intellectual content: Berkowitz, Parashuram, Rowan, Andon, Bass, Bellantoni, Brotman, Deutschendorf, Dunbar, Durso, Everett, Giuriceo, Hickman, Hough, Howell, Leung, Lu, Lyketsos, Murphy, Novak, Purnell, Sylvester, Wu, Zollinger, Koenig, Ahn, Rothman, Brown.

    Statistical analysis: Parashuram, Rowan, Bellantoni, Hough, Huang, Leung, Lu, Murphy, Zollinger.

    Obtained funding: Berkowitz, Parashuram, Lyketsos, Rothman, Brown.

    Administrative, technical, or material support: Berkowitz, Andon, Bellantoni, Brotman, Deutschendorf, Dunbar, Durso, Hebert, Hickman, Howell, Lepley, Leung, Novak, Wu, Zollinger, Koenig, Ahn, Rothman, Brown.

    Supervision: Berkowitz, Parashuram, Durso, Everett, Wu, Zollinger, Ahn, Rothman, Brown.

    Conflict of Interest Disclosures: Dr Giuriceo is an employee of the Centers for Medicare & Medicaid Services. Dr Howell reports grants and personal fees from Society of Hospital Medicine, grants from John A. Hartford foundation, and personal fees from Johns Hopkins University outside the submitted work. Dr Rothman is a member of the Board of Directors of Merck. No other disclosures were reported.

    Funding/Support: The study described was supported by the US Department of Health and Human Services, Centers for Medicare & Medicaid Services, Center for Medicare and Medicaid Innovation (grant 1C1CMS331053). Several of the authors received funding associated with this study including several members from the Johns Hopkins team who received partial salary support during the J-CHiP program duration (Drs Bellantoni, Berkowitz, Brotman, Everett, Hough, Howell, and Zollinger).

    Role of the Funder/Sponsor: The Centers for Medicare & Medicaid Services had a role in reviewing the manuscript. The funders had no other role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation or approval of the manuscript; and decision to submit the manuscript for publication.

    Group Information: The Johns Hopkins Community Health Partnership (J-CHiP) members are Abigail Pulcinella, Johns Hopkins Health System, Lutherville, Maryland (Communications Leader); Albert W. Wu, MD, MPH, Johns Hopkins University School of Public Health, Baltimore, Maryland (Research Facilitating Team); Amy Deutschendorf, MS, RN, ACNS-BC, Johns Hopkins Health System, Baltimore (Executive Board, Acute Care Leadership Team); Anita Everett, MD, Substance Abuse Mental Health Services Administration, Department of Health and Human Services, Washington, DC (Executive Board, Behavioral Health Leadership Team); Anne Langley, JD, MPH, Johns Hopkins Health System, Baltimore (Community Care Leadership Team); Arielle Apfel, MS, Johns Hopkins University School of Medicine, Baltimore (Evaluation Leadership Team); Brian Knight, Men and Families Center, Baltimore (Tumaini Team); Carol Sylvester, RN, MS, Johns Hopkins Health System, Baltimore (Executive Board, Acute Care Leadership Team, Skilled Nursing Facility Leadership Team); Chidinma Ibe, PhD, Johns Hopkins University School of Medicine, Baltimore (Tumaini Team); Christine Weston, Johns Hopkins University School of Public Health, Baltimore (Research Facilitating Team); Constantine G. Lyketsos, MD, Johns Hopkins University School of Medicine, Baltimore (Executive Board, Behavioral Health Leadership Team); Curtis Leung, MPH, Johns Hopkins Health System, Baltimore (Acute Care Leadership Team); Dalal Haldeman, PhD, MBA, Johns Hopkins Health System, Baltimore (Executive Board); Daniel Ford, MD, MPH, Johns Hopkins University School of Medicine, Baltimore (Executive Board); Daniel J. Brotman, MD, Johns Hopkins University School of Medicine, Baltimore (Executive Board, Acute Care Leadership Team); David Hellmann, MD, Johns Hopkins University School of Medicine, Baltimore (Executive Board); David Parker, Johns Hopkins Health System, Baltimore (Executive Board); Debra Hickman, MDiv, Sisters Together and Reaching, Baltimore (Community Care Leadership Team, Tumaini Team); Demetrius Frazier, Sisters Together and Reaching, Baltimore (Community Care Leadership Team, Tumaini Team); Diane Lepley, RN, MSN, Johns Hopkins Hospital, Baltimore (Acute Care Leadership Team); Douglas E. Hough, PhD, Johns Hopkins University School of Public Health, Baltimore (Cost Savings and Analytic Leadership Team); Edward Beranek, MBA, Johns Hopkins Health System, Baltimore (Executive Board); Eric B. Bass, MD, MPH, Johns Hopkins University School of Medicine, Baltimore (Research Facilitating Team); Eric E. Howell, MD, Johns Hopkins University School of Medicine, Baltimore (Executive Board, Acute Care Leadership Team); Ernest Smith, MBA, Johns Hopkins HealthCare, Glen Burnie, Maryland (Tumaini Team); Felicia Hill-Briggs, PhD, ABPP, Johns Hopkins University School of Medicine, Baltimore (Evaluation Leadership Team); Hsin-Chieh Yeh, PhD, Johns Hopkins University School of Medicine, Baltimore (Evaluation Leadership Team); J. Hunter Young, MD, MHS, Johns Hopkins Medicine International, Lutherville (Community Care Leadership Team); Jennifer Bailey, RN, MS, Johns Hopkins Health System–Johns Hopkins Community Physicians, Baltimore (Community Care Leadership Team); John M. Colmers, MPH, Johns Hopkins Health System, Baltimore (Executive Board); Judy Reitz, ScD, Johns Hopkins Hospital, Baltimore (Executive Board); Kevin D. Frick, PhD, Johns Hopkins University Carey Business School, Baltimore (Cost Savings and Analytic Leadership Team); Laura Torres, LCSW-C, Howard County General Hospital, Columbia, Maryland (Behavioral Health Leadership Team); Lawrence Appel, MD, MPH, Johns Hopkins University School of Medicine, Baltimore (Evaluation Leadership Team); Leon Purnell, Men and Families Center, Baltimore (Community Care Leadership Team, Tumaini Team); Linda Dunbar, RN, PhD, Johns Hopkins HealthCare, Glen Burnie (Executive Board, Community Care Leadership Team, Human Resources Leader); Lindsay Andon, MSPH, Johns Hopkins HealthCare, Glen Burnie (Community Care Leadership Team, Evaluation Leadership Team); Lindsay Hebert, MSPH, Johns Hopkins HealthCare, Glen Burnie (Community Care Leadership Team); Lisa Filbert, RN, MS, NHA, Johns Hopkins Bayview Medical Center, Baltimore (Skilled Nursing Facility Leadership Team); Lisa Wilson, Johns Hopkins University School of Public Health, Baltimore (Research Facilitating Team); Martha Sylvia, MSN, PhD, Medical University of South Carolina, Charleston, South Carolina (Cost Savings and Analytic Leadership Team); Mary Myers, MS RN, Johns Hopkins Home Care Group, Baltimore (Acute Care Leadership Team); Melissa Reuland, MS, Johns Hopkins University School of Medicine, Baltimore (Behavioral Health Leadership Team); Melissa Richardson, MBA, Johns Hopkins Health System, Baltimore (Acute Care Leadership Team); Michael Fingerhood, MD, Johns Hopkins University School of Medicine, Baltimore (Community Care Leadership Team); Michele Bellantoni, MD, CMD, Johns Hopkins University School of Medicine, Baltimore (Executive Board, Skilled Nursing Facility Leadership Team); Michelle Petinga, Johns Hopkins Hospital, Baltimore (Community Care Leadership Team); Mike Rogers, Johns Hopkins University School of Public Health, Baltimore (Tumaini Team); Nola Durkin, Johns Hopkins University School of Medicine, Baltimore (Evaluation Leadership Team); Patricia MC Brown, JD, Johns Hopkins Health System–Johns Hopkins HealthCare, Glen Burnie (Executive Board, Program Directorship); Patti Ephraim, MPH, Johns Hopkins University School of Public Health, Baltimore (Evaluation Leadership Team); Paul B. Rothman, MD, Johns Hopkins University School of Medicine, Baltimore (Executive Board, Program Directorship); Peter Greene, MD, Johns Hopkins University School of Medicine, Baltimore (Executive Board); Raymond Zollinger, MD, MBA, Johns Hopkins Health System–Johns Hopkins Community Physicians, Baltimore (Executive Board, Community Care Leadership Team); Regina Richardson, RN, BSN, MBA, CCM, Johns Hopkins HealthCare, Glen Burnie (Community Care Leadership Team); Robert Wm. Blum, MD, MPH, PhD, Johns Hopkins University School of Public Health, Baltimore (Executive Board); Romsai Tony Boonyasai, MD, MPH, Johns Hopkins University School of Medicine, Baltimore (Acute Care Leadership Team); Samuel C. Durso, MD, MBA, Johns Hopkins University School of Medicine, Baltimore (Executive Board, Skilled Nursing Facility Leadership Team); Sarah Kachur, PHARMD, MBA, BCACP, Johns Hopkins HealthCare, Glen Burnie (Community Care Leadership Team, Cost Savings and Analytic Leadership Team); Scott A. Berkowitz, MD, MBA, Johns Hopkins University School of Medicine, Baltimore (Executive Board, Program Directorship); Shannon M.E. Murphy, MA, Johns Hopkins HealthCare, Glen Burnie (Cost Savings and Analytic Leadership Team); Stephanie Reel, MBA, BS, Johns Hopkins University–University Administration, Baltimore (Executive Board); Steven Kravet, MD, MBA, Johns Hopkins Health System–Johns Hopkins Community Physicians, Baltimore (Executive Board); Steven Mandell, MS, Johns Hopkins University School of Medicine, Baltimore (Executive Board); Tracy Novak, MHS, Howard County General Hospital, Columbia (Community Care Leadership Team); Vince Truant, Matrix Ventures LLC, Baltimore (Community Care Leadership Team); William Baumgartner, MD, Johns Hopkins University School of Medicine, Baltimore (Executive Board); Xuan Huang, Johns Hopkins HealthCare, Glen Burnie (Cost Savings and Analytic Leadership Team); Ya Luan Hsiao, MD, MPH, Johns Hopkins University School of Medicine, Baltimore (Research Facilitating Team); Yanyan Lu, Johns Hopkins HealthCare, Glen Burnie (Cost Savings and Analytic Leadership Team).

    Disclaimer: The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the US Department of Health and Human Services or any of its agencies. The research presented here was conducted by the independent evaluation contractor.

    Additional Contributions: The late Fred Brancati, MD, was a major contributor to the launch of the J-CHiP program. Johns Hopkins Medicine provided generous institutional support in creating the J-CHiP program.

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