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.
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.
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.
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