Population Health Management Programs Research Articles

169 Association of Asthma Specialty Care and Adverse Outcomes for Children Enrolled in the Arkansas Medicaid Program

OBJECTIVES/GOALS: Specialty care for asthmatic children should prevent adverse asthma outcomes. This study of children receiving care in the Arkansas Medicaid program used a comparative effectiveness research design to test whether allergy specialty care was associated with reduced adverse asthma outcomes. METHODS/STUDY POPULATION: Using the Arkansas All Payer Claims Database we studied Medicaid-enrolled children with asthma using a propensity score greedy nearest neighbor one-to-one matching algorithm. We matched children with (treatment) and without (comparison) an allergy specialist visit in 2018. The propensity score model included 26 covariates (demographic, clinical, and social determinants of health). Multivariable adjusted logistic regression was used to estimate adverse asthma events (AAE: emergency department visit or inpatient hospitalization with a primary or secondary diagnosis of asthma in 2019). RESULTS/ANTICIPATED RESULTS: We identified 3,031 children with an allergy specialist visit in 2018, and successfully propensity-score matched 2,910 of the treatment group with a non-allergy specialist visit comparison group. The rate of AAEs in 2019 was 9.5% for individuals with an allergy specialist visit versus 10.1% among those without a specialist visit (p=0.450). The adjusted regression analysis showed 20.3% lower rates of AAEs (aOR: 0.797; 95% Confidence Interval: 0.650, 0.977; p=0.029) in 2019 for children with an allergy specialist visit in 2018 compared to those that did not. DISCUSSION/SIGNIFICANCE: Utilizing allergy specialist care was associated with better asthma outcomes in our statewide study of Arkansas Medicaid-enrolled children with asthma. Asthma quality metrics based on guideline-based recommendations for allergy specialist care should be considered in population health management programs.

Using Population Health Management Program to Promote Patient Choice for Medical Management Without Dialysis

Using Population Health Management Program to Promote Patient Choice for Medical Management Without Dialysis

Population Health Management: principles, models and areas of application in public health.

There is no single model for Population Health Management (PHM) and different definitions have been proposed. All PHM models and definitions share the overall aim of improving population health and reduce healthcare costs. To achieve these objectives, PHM makes use of conceptual tools such as the Chronic Care Model and predictive medicine, and technical tools such as information systems and computational and record-linkage techniques to collect and analyse data. Using these tools, it makes it feasible to articulate PHM approaches in the following steps: identification of a population, stratification of individuals according to risk levels, mapping of health needs and development of targeted interventions and models of care. PHM has been applied in a variety of national and regional settings, proving to have great potential. However, the success of PHM models depends on a number of factors. In particular, few key points have emerged that must be taken into consideration when planning and implementing PHM programs. They include PHM funding schemes, strategies to ensure people adherence, the equity dimension in its multiple aspects, and the privacy of personal data. In addition to these challenges, there is the need to act in a legislative context appropriate to the implementation of PHM.

373 Primary Care Provider Perceptions of a Population Health Management Program to Improve Kidney Care

373 Primary Care Provider Perceptions of a Population Health Management Program to Improve Kidney Care

Perioperative Medicine: What the Future Can Hold for Anesthesiology.

Perioperative Medicine: What the Future Can Hold for Anesthesiology.

Patient Perceptions of a CKD Population Health Management Program to Improve Kidney Care

Patient Perceptions of a CKD Population Health Management Program to Improve Kidney Care

Implementation of a Pediatric Population Health Asthma Program in Academic Medical Center-Affiliated Urban and Suburban Practices.

A tiered pediatric Asthma Population Health Management Program (APHMP), based on evidence-based practices, that differentially targets populations for intervention based on rising risk for high utilization and disease complications was implemented at 6 urban and suburban practices affiliated with an academic medical center. In addition to standard pediatric asthma care, APHMP adds regular administration of the asthma control test (ACT), provider education on performance variation, and monitoring through the electronic health record-based asthma registry. As patients' use of acute health care services and complications increases, APHMP integrates multidisciplinary interventions, including an asthma coach who conducts environmental assessments in addition to addressing social needs, into their primary care. A retrospective cohort study method was used to assess population-level effects on asthma event rates and practice- and provider-level variation from 2017 to 2019. Consistent with well-documented health disparities in pediatric asthma, the analysis demonstrated that patients who were male (odds ratio [OR] = 1.21, 95% confidence interval [CI] = 1.02-1.43), 4-8 years old (OR = 4.91, 95% CI = 3.27-7.37), Spanish speaking (OR = 1.67, 95% CI = 1.54-1.81), from low-income neighborhoods (OR = 1.56, 95% CI = 1.53-2.46), and with ACT <20 (OR = 2.88, 95% CI = 1.97-4.21) had higher odds of having asthma events. Six percent of patients studied were found to be at risk for high health care utilization and disease complications. Study limitations include the absence of a control group, the mixed model data collection approach, and the effects of seasonal variation on asthma events. Future directions include analyzing disease management program outcomes of incorporating an asthma coach into a patient's primary care team and addressing provider-level variation in asthma event rates.

Improving the Prediction of Persistent High Health Care Utilizers: Retrospective Analysis Using Ensemble Methodology.

BackgroundA small proportion of high-need patients persistently use the bulk of health care services and incur disproportionate costs. Population health management (PHM) programs often refer to these patients as persistent high utilizers (PHUs). Accurate PHU prediction enables PHM programs to better align scarce health care resources with high-need PHUs while generally improving outcomes. While prior research in PHU prediction has shown promise, traditional regression methods used in these studies have yielded limited accuracy.ObjectiveWe are seeking to improve PHU predictions with an ensemble approach in a retrospective observational study design using insurance claim records.MethodsWe defined a PHU as a patient with health care costs in the top 20% of all patients for 4 consecutive 6-month periods. We used 2013 claims data to predict PHU status in next 24 months. Our study population included 165,595 patients in the Johns Hopkins Health Care plan, with 8359 (5.1%) patients identified as PHUs in 2014 and 2015. We assessed the performance of several standalone machine learning methods and then an ensemble approach combining multiple models.ResultsThe candidate ensemble with complement naïve Bayes and random forest layers produced increased sensitivity and positive predictive value (PPV; 49.0% and 50.3%, respectively) compared to logistic regression (46.8% and 46.1%, respectively).ConclusionsOur results suggest that ensemble machine learning can improve prediction of care management needs. Improved PPV implies reduced incorrect referral of low-risk patients. With the improved sensitivity/PPV balance of this approach, resources may be directed more efficiently to patients needing them most.

Evaluating A Nonemergency Medical Transportation Benefit For Accountable Care Organization Members.

Nonemergency medical transportation benefits, often using smartphone application-based ridesharing services, are increasingly being offered as part of population health management programs. However, the impact of these programs on health care use and costs remains understudied. We conducted a mixed-methods evaluation of a nonemergency medical transportation benefit offered to members of a Medicare accountable care organization (ACO) within a large academic health system, the UNC Health Alliance ACO. Participation in the transportation program was associated with a greater number of per person per year outpatient visits (9.2) and higher outpatient spending ($4,420) than in a comparison group. However, there was no difference in inpatient admissions or emergency department visits, and the program was not cost saving. Qualitative analyses revealed that participants were highly satisfied with the program, reporting that it eased financial burdens and made them feel safer, more empowered, and better able to take control of their health. These findings suggest that although transportation programs are commonly introduced as ways to contain health care spending, it may be better to think of them as programs to improve health care access for people facing difficult circumstances.

Assessing the Value of Unsupervised Clustering in Predicting Persistent High Health Care Utilizers: Retrospective Analysis of Insurance Claims Data.

BackgroundA high proportion of health care services are persistently utilized by a small subpopulation of patients. To improve clinical outcomes while reducing costs and utilization, population health management programs often provide targeted interventions to patients who may become persistent high users/utilizers (PHUs). Enhanced prediction and management of PHUs can improve health care system efficiencies and improve the overall quality of patient care.ObjectiveThe aim of this study was to detect key classes of diseases and medications among the study population and to assess the predictive value of these classes in identifying PHUs.MethodsThis study was a retrospective analysis of insurance claims data of patients from the Johns Hopkins Health Care system. We defined a PHU as a patient incurring health care costs in the top 20% of all patients’ costs for 4 consecutive 6-month periods. We used 2013 claims data to predict PHU status in 2014-2015. We applied latent class analysis (LCA), an unsupervised clustering approach, to identify patient subgroups with similar diagnostic and medication patterns to differentiate variations in health care utilization across PHUs. Logistic regression models were then built to predict PHUs in the full population and in select subpopulations. Predictors included LCA membership probabilities, demographic covariates, and health utilization covariates. Predictive powers of the regression models were assessed and compared using standard metrics.ResultsWe identified 164,221 patients with continuous enrollment between 2013 and 2015. The mean study population age was 19.7 years, 55.9% were women, 3.3% had ≥1 hospitalization, and 19.1% had 10+ outpatient visits in 2013. A total of 8359 (5.09%) patients were identified as PHUs in both 2014 and 2015. The LCA performed optimally when assigning patients to four probability disease/medication classes. Given the feedback provided by clinical experts, we further divided the population into four diagnostic groups for sensitivity analysis: acute upper respiratory infection (URI) (n=53,232; 4.6% PHUs), mental health (n=34,456; 12.8% PHUs), otitis media (n=24,992; 4.5% PHUs), and musculoskeletal (n=24,799; 15.5% PHUs). For the regression models predicting PHUs in the full population, the F1-score classification metric was lower using a parsimonious model that included LCA categories (F1=38.62%) compared to that of a complex risk stratification model with a full set of predictors (F1=48.20%). However, the LCA-enabled simple models were comparable to the complex model when predicting PHUs in the mental health and musculoskeletal subpopulations (F1-scores of 48.69% and 48.15%, respectively). F1-scores were lower than that of the complex model when the LCA-enabled models were limited to the otitis media and acute URI subpopulations (45.77% and 43.05%, respectively).ConclusionsOur study illustrates the value of LCA in identifying subgroups of patients with similar patterns of diagnoses and medications. Our results show that LCA-derived classes can simplify predictive models of PHUs without compromising predictive accuracy. Future studies should investigate the value of LCA-derived classes for predicting PHUs in other health care settings.

Development and validation of a prediction model for actionable aspects of frailty in the text of clinicians' encounter notes.

Frailty is a prevalent risk factor for adverse outcomes among patients with chronic lung disease. However, identifying frail patients who may benefit from interventions is challenging using standard data sources. We therefore sought to identify phrases in clinical notes in the electronic health record (EHR) that describe actionable frailty syndromes. We used an active learning strategy to select notes from the EHR and annotated each sentence for 4 actionable aspects of frailty: respiratory impairment, musculoskeletal problems, fall risk, and nutritional deficiencies. We compared the performance of regression, tree-based, and neural network models to predict the labels for each sentence. We evaluated performance with the scaled Brier score (SBS), where 1 is perfect and 0 is uninformative, and the positive predictive value (PPV). We manually annotated 155952 sentences from 326 patients. Elastic net regression had the best performance across all 4 frailty aspects (SBS 0.52, 95% confidence interval [CI] 0.49-0.54) followed by random forests (SBS 0.49, 95% CI 0.47-0.51), and multi-task neural networks (SBS 0.39, 95% CI 0.37-0.42). For the elastic net model, the PPV for identifying the presence of respiratory impairment was 54.8% (95% CI 53.3%-56.6%) at a sensitivity of 80%. Classification models using EHR notes can effectively identify actionable aspects of frailty among patients living with chronic lung disease. Regression performed better than random forest and neural network models. NLP-based models offer promising support to population health management programs that seek to identify and refer community-dwelling patients with frailty for evidence-based interventions.

Reducing Cardiovascular Risk for Patients With Diabetes: An Evidence-Based, Population Health Management Program.

ABSTRACTThose with diabetes are at an increased risk of cardiovascular disease (CVD). Safety net clinics serve populations that bear a significant burden of disease and disparities and are a key setting in which to focus on reducing CVD. An integrated health system provided funding and technical assistance (TA) to safety net organizations (community health centers and public hospitals) in Northern California to decrease the risk of cardiovascular events for patients with diabetes. This was a program called Preventing Heart Attacks and Strokes Everyday (PHASE), which combined an evidence-based medication protocol with population health management and team-based care strategies. The TA supported organizations by sharing best practices, providing quality improvement coaching, and facilitating peer learning. A mixed-methods evaluation found that organizations involved in PHASE improved rates of blood pressure control and cardioprotective medication prescriptions for patients with diabetes. They made progress on these measures through strategies such as leveraging team-based care, providing education on evidence-based protocols, and using data to drive improvements. The evaluation concluded that financially supporting and providing focused TA to safety net organizations can help them build capacity and leverage their strengths to improve outcomes and potentially decrease the risk of heart attacks and strokes in communities.

Integrating E-Prescribing and Pharmacy Claims Data for Predictive Modeling: Comparing Costs and Utilization of Health Plan Members Who Fill Their Initial Medications with Those Who Do Not.

Nonfilling of prescribed medications is a worldwide problem of serious concern. Studies of health care costs and utilization associated with medication nonadherence frequently rely on claims data and usually focus on patients with specific conditions. Past studies also have little agreement on whether higher medication costs associated with higher adherence can reduce downstream health care consumption. To (a) compare the characteristics between people with and without complete medication initiations from a general population and (b) quantify the effect of medication initiation on health care utilization and expenditures with propensity score weighting. We conducted a retrospective cohort study using 2012 and 2013 electronic health records (EHR) and insurance claims data from an integrated health care delivery network. We included 43,097 eligible primary care patients in the study. Annual medication fill rates of initial prescriptions in 2012 were defined as the number of filled prescriptions from claims divided by the number of e-prescriptions from EHRs, while excluding all refills. A claim was considered filled if (a) EHR and claims records were from the same drug class; (b) claims occurred between the date of a current EHR order and that of the next EHR order of the same class; and (c) the maximum fill rate was 100%. The 6 annual outcomes included total costs, medical costs, pharmacy costs, being a high-cost "outlier" (in top 5%), having 1 or more hospitalizations, and having 1 or more emergency department (ED) visits. Individuals were classified as either having completed all medication initiations (100% annual filling rate for initiations) or not. We used propensity score weighting to control for baseline differences between complete and incomplete initial fillers. We adopted linear and logistic regressions to model costs and binary utilization indicators for the same year (concurrently) and next year (prospectively). Approximately 42% of the study sample had complete medication initiations (100% filling rate), while the remaining 58% had incomplete initiations. Individuals who fully filled initial prescriptions had lower comorbidity burden and consumed fewer health care resources. After applying propensity score weighting and controlling for variables such as the number of prescription orders, patients with complete medication initiations had lower overall and medical costs, concurrently and prospectively (e.g., $751 and $252 less for annual total costs). Complete medication initiation fillers were also less likely to have concurrent health care utilization (OR = 0.78, 95% CI = 0.68-0.90 for hospitalization; OR = 0.77, 95% CI = 0.72-0.82 for ED admissions) but no difference in prospective utilization other than for ED visits (OR = 0.93, 95% CI = 0.87-0.99). Identifying the subpopulation of patients with incomplete medication initiations (i.e., filling less than 100% of initial prescriptions) is a pragmatic approach for population health management programs to align resources and potentially contain cost and utilization. No outside funding supported this study. This study applied the Adjusted Clinical Group (ACG) case-mix/risk adjustment methodology, developed at Johns Hopkins Bloomberg School of Public Health. Although ACGs are an important aspect of this study, the goal of the study was not to directly assess or evaluate the methodology. The Johns Hopkins University receives royalties for nonacademic use of software based on the ACG methodology. Chang, Kharrazi, and Weiner receive a portion of their salary support from this revenue. Chang is also a part-time consultant for Monument Analytics, a health care consultancy whose clients include the life sciences industry, as well as plaintiffs in opioid litigation. Alexander is past Chair of FDA's Peripheral and Central Nervous System Advisory Committee; has served as a paid advisor to IQVIA; is a co-founding Principal and equity holder in Monument Analytics; and is a member of OptumRx's National P&T Committee. These arrangements have been reviewed and approved by Johns Hopkins University in accordance with its conflict of interest policies. The other authors have nothing to disclose.

An artificial neural network approach for predicting hypertension using NHANES data

This paper focus on a neural network classification model to estimate the association among gender, race, BMI, age, smoking, kidney disease and diabetes in hypertensive patients. It also shows that artificial neural network techniques applied to large clinical data sets may provide a meaningful data-driven approach to categorize patients for population health management, and support in the control and detection of hypertensive patients, which is part of the critical factors for diseases of the heart. Data was obtained from the National Health and Nutrition Examination Survey from 2007 to 2016. This paper utilized an imbalanced data set of 24,434 with (69.71%) non-hypertensive patients, and (30.29%) hypertensive patients. The results indicate a sensitivity of 40%, a specificity of 87%, precision of 57.8% and a measured AUC of 0.77 (95% CI [75.01–79.01]). This paper showed results that are to some degree more effectively than a previous study performed by the authors using a statistical model with similar input features that presents a calculated AUC of 0.73. This classification model can be used as an inference agent to assist the professionals in diseases of the heart field, and can be implemented in applications to assist population health management programs in identifying patients with high risk of developing hypertension.

Population Health Management during Student Pharmacist Introductory Experiential Education to Expand Clinical Pharmacist Impact.

To evaluate the impact of incorporating pre-advanced pharmacy practice experience (pre-APPE) student pharmacists into three different population health management (PHM) projects. The prospective quality improvement projects incorporated three third-year student pharmacists who developed and conducted individual PHM projects over the course of three to seven months. The projects included hypoglycemia screening, hepatitis C virus and human immunodeficiency virus screening, and statin use evaluations for atherosclerotic cardiovascular disease risk reduction. Under the guidance of a clinical pharmacist, students developed project materials, conducted patient chart reviews, and contacted patients to make interventions such as recommendations for therapy, ambulatory patient monitoring, patient education, and arranging provider follow-up. Student impact was evaluated through the number of patients screened, the number of eligible patients contacted, and the total number of interventions or recommendations made. Student time spent was tracked throughout the projects. Out of 244 patients screened, 198 patients met inclusion criteria and 162 patients were contacted or assessed by a student pharmacist. Students made a total of 319 interventions, including patient education (132), patient monitoring (132), pharmacotherapy recommendations (28), and arranging follow-up (27). On average students screened 33 patients per month, and, per patient, required 8.6 minutes for eligibility assessment and approximately 6 minutes for telephone interviews. This report demonstrates that pre-APPE student pharmacists are well-equipped to design and implement PHM projects. Utilization of student pharmacists in similar PHM programs can expand the pharmacist's impact on patient care in the ambulatory care setting.

Patient Outreaches for Clinical Pharmacy Services: A Population Health Management Program Assessment

Background: Pharmacists in ambulatory care can utilize population health approaches to identify patients needing disease management and improve outcomes. However, population health is only effective when identified patients are successfully outreached and show to appointments. Objective: Describe a population health approach utilized by pharmacists in primary care, report outcomes of outreach attempts and scheduled appointments, and determine whether patient and referral characteristics predict no-show appointments. Methods: Retrospective cohort study of patients referred for pharmacist management of hypertension or chronic pain through population health between 2013-2016. Outreach attempt and appointments outcomes were collected. Patient and referral characteristics were analyzed to determine whether predictive of no-show appointments using logistic regression. Results: Of 450 outreach attempts, 250 (56%) patients were not reached, 164 (36%) scheduled appointments, and 36 (8%) were reached but declined an appointment. Of 164 patients with appointments, 71 (43%) no-showed. Patients with higher systolic blood pressure were more likely to no-show (OR: 1.02, 95% CI: 1.00-1.04). Other characteristics were not predictive of no-show appointments. Conclusion: Successful outreach and showed appointments are essential components of successful population health programs. Using multiple outreach modalities and further identifying factors predictive of no-show appointments to refine the current approach may lead to increased efficiency.

Going Beyond Clinical Care to Reduce Health Care Spending: Findings From the J-CHiP Community-based Population Health Management Program Evaluation: Erratum.

Going Beyond Clinical Care to Reduce Health Care Spending: Findings From the J-CHiP Community-based Population Health Management Program Evaluation: Erratum.

Maximizing Potentially Avoidable Hospitalizations and Cost Savings Beyond Targeting the Most Costly Patients

Background: Many health care organizations use value-based care strategies that include population health management programs and data analytics to stratify their population and identify high-risk and high-cost patients. Most of these programs target the top 5% most expensive patients. However, little is known about these patients prior to reaching the top 5% of cost, or how their characteristics change over time. To address these gaps, we analyzed the differences in characteristics of patients from 3 different cost segments over 5 years (2011-2015). Objective: To evaluate potentially avoidable hospitalizations and associated savings in the health care cost of older patients using Personal Emergency Response Service (PERS). Methods: We conducted a retrospective, longitudinal, multicenter study to evaluate potentially avoidable hospitalizations of 2643 older patients over 5 years (2011-2015). All patients had at least one inpatient and/or outpatient encounter, and at least one episode of home health care during the study period. Additionally, all patients used PERS at home anytime during the study period. We ranked patients by their annual health care cost and then grouped them into the following segments for each respective year: T-segment constitutes the top 5% most expensive patients; M-segment comprises the middle 45% of patients; B-segment includes the bottom 50% least expensive patients. We then evaluated differences in the characteristics of patients in the B-, M- and T-cost segments in each study year. Continuous variables were compared by t test (two-tailed) for normally distributed variables and Kruskal-Wallis Rank Sum test for skewed variables. The chi-square test was used for categorical variables. Results: The three cost segments differed significantly each year (P<.05) with respect to: demographics (age, education), PERS utilization (Incidents, ER transport), health care utilization (hospitalizations, length of stay, 30-, 90-, and 180-day readmissions, outpatient encounters) and medical conditions (number of conditions, Charlson Comorbidity Index). Further, we analyzed the number of potentially avoidable hospitalizations (as defined by CMS) and associated cost savings in each segment. All hospitalizations occurred among patients in the T- and M-segments while the B-segment was hospitalization-free each year. The percentage of avoidable hospitalizations in the M-segment compared with the T-segment was 3 times greater (75% vs 25%, P<.001). While the potential cost saving from avoidable hospitalizations in the entire population increased from $3.0M to $8.2M (2011-2015), the majority of these cost savings were in the M-segment compared with the T-segment (60% vs 40%, P<.001). Conclusions: Although many health care organizations target intensive and costly interventions to their most expensive patients, this analysis suggests there is untapped potential to control costs and improve care beyond focusing on the highest cost patients. Namely, targeting patients in the middle cost segment may offer great opportunity for population management programs to maximize both potentially avoidable hospitalizations and cost savings.

Data and knowledge standards for learning health: A population management example using chronic kidney disease.

The widespread creation of learning health care systems (LHSs) will depend upon the use of standards for data and knowledge representation. Standards can facilitate the reuse of approaches for the identification of patient cohorts and the implementation of interventions. Standards also support rapid evaluation and dissemination across organizations. Building upon widely‐used models for process improvement, we identify specific LHS activities that will require data and knowledge standards. Using chronic kidney disease (CKD) as an example, we highlight the specific data and knowledge requirements for a disease‐specific LHS cycle, and subsequently identify areas where standards specifications, clarification, and tools are needed. The current data standards for CKD population management recommendations were found to be partially ambiguous, leading to barriers in phenotyping, risk identification, patient‐centered clinical decision support, patient education needs, and care planning. Robust tools are needed to effectively identify patient health care needs and preferences and to measure outcomes that accurately depict the multiple facets of CKD. This example presents an approach for defining the specific data and knowledge representation standards required to implement condition‐specific population health management programs. These standards specifications can be promoted by disease advocacy and professional societies to enable the widespread design, implementation, and evaluation of evidence‐based health interventions, and the subsequent dissemination of experience in different settings and populations.

Going Beyond Clinical Care to Reduce Health Care Spending: Findings From the J-CHiP Community-based Population Health Management Program Evaluation.

Addressing both clinical and nonclinical determinants of health is essential for improving population health outcomes. In 2012, the Johns Hopkins Community Health Partnership (J-CHiP) implemented innovative population health management programs across acute and community environments. The community-based program involved multidisciplinary teams [ie, physicians, care managers (CM), health behavior specialists (HBS), community health workers, neighborhood navigators] and collaboration with community-based organizations to address social determinants. To report the impact of a community-based program on cost and utilization from 2011 to 2016. Difference-in-difference estimates were calculated for an inclusive cohort of J-CHiP participants and matched nonparticipants. The analysis was replicated for participants with a CM and/or HBS to estimate the differential impact with more intensive program services. A total of 3268 high-risk Medicaid and Medicare beneficiaries (1634 total J-CHiP participants, 1365 with CM and 678 with HBS). Paid costs and counts of emergency department visits, admissions, and readmissions per member per year. For Medicaid, costs were almost $1200 per member per year lower for participants as a whole, $2000 lower for those with an HBS, and $3000 lower for those with a CM; hospital admission and readmission rates were 9%-26% lower for those with a CM and/or HBS. For Medicare, costs were lower (-$476), but utilization was similar or higher than nonparticipants. None of the observed Medicaid or Medicare differences were statistically significant. Although not statistically significant, the results indicate a promising innovation for Medicaid beneficiaries. For Medicare, the impact was negligible, indicating the need for further program modification.