1. Accelerating the adoption of bundled payment reimbursement systems: A data-driven approach utilizing claims data
- Author
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Ruben A. Proano, Wenchang Zhang, Margrét V. Bjarnadóttir, Renata Konrad, and David Anderson
- Subjects
Actuarial science ,business.industry ,media_common.quotation_subject ,Public Health, Environmental and Occupational Health ,030204 cardiovascular system & hematology ,Payment ,Data-driven ,03 medical and health sciences ,Identification (information) ,0302 clinical medicine ,Incentive ,Scale (social sciences) ,Health care ,030212 general & internal medicine ,Business ,Safety, Risk, Reliability and Quality ,Safety Research ,Medicaid ,Reimbursement ,media_common - Abstract
Bundled payments as a reimbursement mechanism have the potential to reduce health care expenditures and improve the quality of care by aligning the incentives of payers, providers and, most importantly, patients. The Centers for Medicare and Medicaid Services (CMS) launched the Bundled Payments for Care Improvement (BPCI) program in April 2013 and has set ambitious goals for adopting alternative payment models on a large scale. One of the crucial components for successful implementation of a bundled payment system is the identification of procedural homogeneous groups within an episode of care (a set of services needed to treat a medical condition), to which a flat reimbursement rate can be applied. In this study, we propose a data-driven clustering approach to automatically detect and explicitly represent homogeneous sub-groups of services for a given condition. Manual detection is slow and relies on consensus decisions, but automatic detection can serve as an important foundational input for bun...
- Published
- 2017
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