Utilizing the 4Ms framework to create a structure and process to support voluntary health assessments in affordable housing.

BACKGROUND: A growing number of older adults live in senior affordable housing, many with limited support systems and representing underserved or disadvantaged populations. Staff in these buildings are in a unique position to identify and address the healthcare and biopsychosocial needs of their residents and link them to services and supports. METHODS: Staff in four affordable housing sites received training on the 4Ms approach to caring for older adults and conducting resident health assessments. They learned to collect comprehensive health information using a 4Ms Resident Health Risk Assessment (4Ms-RHRA) and results are entered into a customized electronic database. Embedded flags identify potential risk factors and initiate a follow-up process for documenting interventions and tracking referrals to healthcare and supportive services. RESULTS: Eighty-one percent of the 221 4Ms-RHRAs completed with residents (63% female, mean age 71.1 years, 73% live alone) were flagged for at least one concern (Mean = 2.2 flags). Items addressing What Matters were most frequently flagged: resident's "most important health issue" (55%) and Advance Care Planning (ACP: 48%). In response, staff provided Advance Directive forms and Five Wishes pamphlets to interested residents and reminded residents to review ACP documents annually. CONCLUSION: Training affordable housing staff, precepting faculty, and students to conduct health assessments based on the 4Ms framework and longitudinally track interventions related to resident-centered needs and manage long-term service and supports is a first step in creating an interprofessional workforce capable of addressing the complex needs of older individuals in affordable housing.

The Camden Coalition Care Management Program Improved Intermediate Care Coordination: A Randomized Controlled Trial.

When a randomized evaluation finds null results, it is important to understand why. We investigated two very different explanations for the finding from a randomized evaluation that the Camden Coalition's influential care management program-which targeted high-use, high-need patients in Camden, New Jersey-did not reduce hospital readmissions. One explanation is that the program's underlying theory of change was not right, meaning that intensive care coordination may have been insufficient to change patient outcomes. Another explanation is a failure of implementation, suggesting that the program may have failed to achieve its goals but could have succeeded if it had been implemented with greater fidelity. To test these two explanations, we linked study participants to Medicaid data, which covered 561 (70 percent) of the original 800 participants, to examine the program's impact on facilitating postdischarge ambulatory care-a key element of care coordination. We found that the program increased ambulatory visits by 15 percentage points after fourteen days postdischarge, driven by an increase in primary care; these effects persisted through 365 days. These results suggest that care coordination alone may be insufficient to reduce readmissions for patients with high rates of hospital admissions and medically and socially complex conditions.

Hospital Readmissions by Variation in Engagement in the Health Care Hotspotting Trial: A Secondary Analysis of a Randomized Clinical Trial.

Importance: Variability in intervention participation within care management programs can complicate standard analysis strategies. Objective: To evaluate whether care management was associated with reduced hospital readmissions among individuals with higher participation probabilities. Design, Setting, and Participants: A total of 800 hospitalized patients aged 18 years and older were randomized as part of the Health Care Hotspotting randomized clinical trial, which was conducted in Camden, New Jersey, from June 2014 to September 2017. Data were collected through October 2018. In this new analysis performed between April 6, 2022, and April 23, 2023, the distillation method was applied to account for variable intervention participation. A gradient-boosting machine learning model produced predicted probabilities of engaged participation using baseline covariates only. Predicted probabilities were used to trim both intervention and control populations in an equivalent manner, and intervention effects were reevaluated within study population subsets that were increasingly concentrated with patients having higher participation probabilities. Patients had 2 or more hospitalizations in the 6-month preenrollment period and documented evidence of chronic illness and social complexity. Intervention: Multidisciplinary teams provided services to patients in the intervention arm for a mean 120 days after hospital discharge. Patients in the control group received usual postdischarge care. Main Outcomes and Measures: Hospital readmission rates and counts 30, 90, and 180 days postdischarge. Results: Of 800 eligible patients, 782 had complete discharge information and were included in this analysis (mean [SD] age, 56.6 [12.7] years; 395 [50.5%] female). In the intent-to-treat analysis, the unadjusted 180-day readmission rate for treatment and control groups was 60.1% vs 61.7% (adjusted odds ratio, 0.95; 95% CI, 0.71-1.28; P = .73) and the mean (SD) number of 180-day readmissions was 1.45 (1.89) vs 1.48 (1.94) (adjusted incidence rate ratio, 0.99, 95% CI, 0.88-1.12; P = .86). Among the population with the highest participation probabilities, the mean (SD) 180-day readmission count was 1.22 (1.74) vs 1.57 (1.74) and the incidence rate ratio attained statistical significance (adjusted incidence rate ratio, 0.74; 95% CI, 0.56-0.99; P = .045). Adjusted odds ratios and adjusted incidence rate ratios for 30- and 90-day outcomes reached statistical significance after population distillation. Conclusions and Relevance: This secondary analysis of a randomized clinical trial found that care management was associated with reduced readmissions among patients with higher participation probabilities, suggesting that program operation could be improved by addressing barriers to participation and refining inclusion criteria to identify patients most likely to benefit. Trial Registration: ClinicalTrials.gov Identifier: NCT02090426.

Lessons from the Camden Coalition of Healthcare Providers' First Medicaid Shared Savings Performance Evaluation.

Accountable Care Organizations (ACOs) aim to reduce health care costs while improving patient outcomes. Camden Coalition of Healthcare Providers' (Camden Coalition) work already aligned with this aim before receiving state approval to operate a certified Medicaid ACO in New Jersey. Upon its formation, the Camden Coalition ACO partnered with UnitedHealthcare and, through state legislation, Rutgers Center for State Health Policy (CSHP) was established as its external evaluator. In evaluating the Camden Coalition ACO, Rutgers CSHP built on the Medicare Shared Savings model, but modified it based on the understanding that the Medicaid population differs from the Medicare population. Annual savings rate (ASR) was used to measure shared savings, and was calculated at the Medicaid product level and aggregated up to reflect a single ASR for the first performance year. The calculated performance yielded a range of shared savings from an ASR of 0.4% to 5.3%, depending on which dollar amount was used to create the outlier ceiling (limit at which a subset of members with expensive utilization patterns are excluded) and how the appropriate statewide trend factor (the expected percentage increase in Medicaid costs across the state) was chosen. In all scenarios, the ASR resulted in less cost savings than predicted. The unfavorable results may be caused by the fact that the evaluation was not calibrated to capture areas where Camden Coalition's ACO was likely to make its impact. Future ACO evaluations should be designed to better correlate with the patient populations and practice areas of the ACO.

Cluster Analysis of Acute Care Use Yields Insights for Tailored Pediatric Asthma Interventions.

STUDY OBJECTIVE: We undertake this study to understand patterns of pediatric asthma-related acute care use to inform interventions aimed at reducing potentially avoidable hospitalizations. METHODS: Hospital claims data from 3 Camden city facilities for 2010 to 2014 were used to perform cluster analysis classifying patients aged 0 to 17 years according to their asthma-related hospital use. Clusters were based on 2 variables: asthma-related ED visits and hospitalizations. Demographics and a number of sociobehavioral and use characteristics were compared across clusters. RESULTS: Children who met the criteria (3,170) were included in the analysis. An examination of a scree plot showing the decline in within-cluster heterogeneity as the number of clusters increased confirmed that clusters of pediatric asthma patients according to hospital use exist in the data. Five clusters of patients with distinct asthma-related acute care use patterns were observed. Cluster 1 (62% of patients) showed the lowest rates of acute care use. These patients were least likely to have a mental health-related diagnosis, were less likely to have visited multiple facilities, and had no hospitalizations for asthma. Cluster 2 (19% of patients) had a low number of asthma ED visits and onetime hospitalization. Cluster 3 (11% of patients) had a high number of ED visits and low hospitalization rates, and the highest rates of multiple facility use. Cluster 4 (7% of patients) had moderate ED use for both asthma and other illnesses, and high rates of asthma hospitalizations; nearly one quarter received care at all facilities, and 1 in 10 had a mental health diagnosis. Cluster 5 (1% of patients) had extreme rates of acute care use. CONCLUSION: Differences observed between groups across multiple sociobehavioral factors suggest these clusters may represent children who differ along multiple dimensions, in addition to patterns of service use, with implications for tailored interventions.

Community-Based Health Education Programs Designed to Improve Clinical Measures Are Unlikely to Reduce Short-Term Costs or Utilization Without Additional Features Targeting These Outcomes.

Stakeholders often expect programs for persons with chronic conditions to "bend the cost curve." This study assessed whether a diabetes self-management education (DSME) program offered as part of a multicomponent initiative could affect emergency department (ED) visits, hospital stays, and the associated costs for an underserved population in addition to the clinical indicators that DSME programs attempt to improve. The program was implemented in Camden, New Jersey, by the Camden Coalition of Healthcare Providers to address disparities in diabetes care. Data used are from medical records and from patient-level information about hospital services from Camden's hospitals. Using multivariate regression models to control for individual characteristics, changes in utilization over time and changes relative to 2 comparison groups were assessed. No reductions in ED visits, inpatient stays, or costs for participants were found over time or relative to the comparison groups. High utilization rates and costs for diabetes are associated with longer term disease progression and its sequelae; thus, DSME or peer support may not affect these in the near term. Some clinical indicators improved among participants, and these might lead to fewer costly adverse health events in the future. DSME deployed at the community level, without explicit segmentation and targeting of high health care utilizers or without components designed to affect costs and utilization, should not be expected to reduce short-term medical needs for participating individuals or care-seeking behaviors such that utilization is reduced. Stakeholders must include financial outcomes in a program's design if those outcomes are to improve.

Building a citywide, all-payer, hospital claims database to improve health care delivery in a low-income, urban community.

Developing data-driven local solutions to address rising health care costs requires valid and reliable local data. Traditionally, local public health agencies have relied on birth, death, and specific disease registry data to guide health care planning, but these data sets provide neither health information across the lifespan nor information on local health care utilization patterns and costs. Insurance claims data collected by local hospitals for administrative purposes can be used to create valuable population health data sets. The Camden Coalition of Healthcare Providers partnered with the 3 health systems providing emergency and inpatient care within Camden, New Jersey, to create a local population all-payer hospital claims data set. The combined claims data provide unique insights into the health status, health care utilization patterns, and hospital costs on the population level. The cross-systems data set allows for a better understanding of the impact of high utilizers on a community-level health care system. This article presents an introduction to the methods used to develop Camden's hospital claims data set, as well as results showing the population health insights obtained from this unique data set.