Research in CHHS
Click below to learn more about the current research in each department/school:
Highlights from CHHS Faculty Research:
NSF Grant #1303139 (Chumbler - PI)
Collaborative Research: An Allocation Model with Dynamic Updates for Balanced Workload Distribution on Patient Centered Medical Homes
Dean Neale R. Chumbler and Dr. William Mkanta, Associate Professor in the Department of Public Health, along with colleagues at the University of Michigan and Wayne State University were awarded a collaborative research grant funded by the National Science Foundation. The goal of this collaborative Advancing Health Services through System Modeling Research project is to lay a scientific foundation for new business rules and operation sciences useful for the patient centered medical home model (PCMH). The PCMH model is an emerging team-based approach for primary care aimed at improving timely access to care, continuity of care, care cost and comprehensiveness for patient wellness.
Drs. Chumbler and Mkanta employ Medicaid data from 4 states to build an innovative and new prediction tool. This tool will not only add important contributions to the scientific literature, but it will also equip and guide healthcare managers and executives, planners and policy makers with the necessary information to better understand the predictors of costs and utilization among Medicaid recipients.
Drs. Chumbler and Mkanta and their colleagues are in the process of writing two manuscripts for publication. One is currently assessing the cost and determine predictors of hospitalizations/expenditure in hospitalizations involving ambulatory care sensitive conditions (ACSC) among Medicaid enrollees in comprehensive managed care plans. ACSC hospitalizations may be indicative of deficiencies in accessing or utilizing primary care services and are responsible for increased volume of avoidable hospitalizations and preventable cost of care. These trends become more alarming when they emerge among participants of managed care plans that are specifically designed to coordinate care and potentially prevent unfavorable trends of cost and service use. Next, the team will prepare a manuscript describing potential workload and cost savings with corresponding between-state comparisons when the PCMH model is applied to a Medicaid population.
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