Abstract

Most of the individuals in the United States are concerned about healthcare affordability and rising healthcare costs. The prevalent healthcare cost reimbursement system, Fee-For-Service (FFS) has been deemed as a key driver for increasing healthcare costs. Bundle payments (BP) has been suggested as an alternative to replace FFS and has shown to reduce the rising healthcare costs. Under BP, the expected set of services involved in treating a diagnosis, or episode of care, is reimbursed by a single payment. We propose a systemic pricing of multiple diagnosis under a Cluster-Based Bundle Payment system (CBBP), where for a given diagnosis, groups of encounters with homogeneous service patterns are reimbursed by a single price. Through a two-stage multicriteria optimization model, we systemically price clusters of encounters to make highly critical episodes of care more affordable by collecting more revenue from less critical clusters across all episodes of care while mitigating the overall financial risks which can facilitate the implementation of BP and CBBP on a larger scale. The criticality of an episode of care and their clusters of encounters is obtained via Analytic Hierarchy Process (AHP) as a function of their average costs levels, average overpayments, and number of encounters. We compare the results of our proposed methodology with a benchmark model where pricing is done using the mean FFS cost for the 153 most expensive episodes of care in the Greater Rochester area for 2007. The proposed methodology offers a systematic approach for reimbursing episodes of care depending on their criticality, and improving the affordability and lowering of overpayment costs across any given range of episodes of care.

Publication Date

12-18-2018

Document Type

Thesis

Student Type

Graduate

Degree Name

Industrial and Systems Engineering (MS)

Department, Program, or Center

Industrial and Systems Engineering (KGCOE)

Advisor

Ruben Proano

Advisor/Committee Member

Katie McConky

Campus

RIT – Main Campus

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