Healthcare Policy Researchers Examine Outcomes in Carefully Tailored Care Management


To what extent can carefully targeted care management programs for Medicare beneficiaries bring down costs for those attributed to accountable care organizations (ACOs)? A new study has shown significant results—but only with the highest-acuity patients. Those are the results of a study published in the June issue of the Journal of Medical Internet Research (JMIR).

The article, “Impactability Modeling for Reducing Medicare Accountable Care Organization Payments and Hospital Events in High-Need High-Cost Patients: Longitudinal Cohort Study,” was written by a group of policy exerts: Maureen A. Smith, Menggang Yu, Jared D. Huling, Xinyi Wang, Allie DeLonay, and Jonathan Jaffery.

As the authors write in the article, “We conducted a longitudinal cohort study of 76,140 patients in a Medicare accountable care organization with multiple before-and-after measures of the outcome, using linked electronic health records and Medicare claims data from 2012 to 2019. There were 489 patients in the historic case management program, with 1550 matched comparison patients, and 830 patients in the new program, with 2368 matched comparison patients. The historic program targeted high-risk patients and assigned a centrally located registered nurse and social worker to each patient. The new program targeted high- and moderate-risk patients and assigned a nurse physically located in a primary care clinic. Our primary outcomes were any unplanned hospital events (admissions, observation stays, and emergency department visits), count of event-days, and Medicare payments.”

The fundamental question: could creating specific “benefit” or “impactability” scores for individual patients, using a method known as “impactability modeling,” make a difference in terms of ensuring the optimal level of care management, and the best outcomes? And would prospectively implementing the score, and evaluating the results in a new case management program, help care managers determine how well patients might respond to such carefully directed care management efforts?

So what happened? “In the historic program, as expected, high-benefit patients enrolled in case management had fewer events, fewer event-days, and an average US $1.15 million reduction in Medicare payments per 100 patients over the subsequent year when compared with the findings in matched comparison patients. For the new program, high-benefit high-risk patients enrolled in case management had fewer events, while high-benefit moderate-risk patients enrolled in case management did not differ from matched comparison patients.”

The authors write that, “Although there was evidence that a benefit score could be extended to a new case management program for similar (i.e., high-risk) patients, there was no evidence that it could be extended to a moderate-risk population. Extending a score to a new program and population should include evaluation of program outcomes within key subgroups. With increased attention on value-based care, policy makers and measure developers should consider ways to incorporate impactability modeling into program design and evaluation.”

Maureen Smith, M.D., M.P.H., Ph.D., is a professor of population health sciences and family medicine at the University of Wisconsin-Madison; Menggang Yu, Ph.D., is a professor of biostatistics and medical informatics in the Department of Statistics at the University of Wisconsin-Madison; Jared D. Huling, Ph.D., is an assistant professor in the Division of Biostatistics at the University of Minnesota School of Public Health; Xinyi Wang, M.S., is an informatics epidemiologist at the Washington State Department of Health (Seattle); Allie DeLonay, M.S., is a data scientist at SAS, in Madison, Wis.; and Jonathan Jaffery, M.D., M.S., is a faculty member in the Division of Nephrology within the Department of Medicine, and chief population health officer at UW Health, and president of the UW Health ACO (accountable care organization).

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