Youn, Seokjun (2019-05). Essays on Payment Reform Models and Capacity Planning in Healthcare. Doctoral Dissertation. Thesis uri icon

abstract

  • My dissertation is inspired by challenging yet encouraging payment policies and operational issues in the U.S. healthcare system. Two of the three essays deliver policy implications for bundled payment reform models that aim to improve the predictability of care outcomes and associated costs by reducing variation in care-delivery practices. To investigate how variation in practice relates to hospital operational performance, my first essay proposes a novel measure of clinical practice variation based on a high-volume inpatient discharge dataset. I find that hospitals may be improperly rewarded for quality improvements if practice variation is ignored, implying that incentives and penalties for hospital operations should be designed to account for such effects. Also, I identify potential drawbacks inherent in the government's status quo policy for selecting participating providers in the bundled payment reform models. To address this issue, my second essay incorporates insights from the first essay and suggests a systematic framework for healthcare provider evaluation and selection. Using a combinatorial auction model equipped with data envelopment analysis as a pre-selection tool, the proposed framework alleviates the inherent decision-making bias of the current system and deploys adequate healthcare providers for target regions, thereby creating an optimized bundled payment program. Lastly, my third essay applies a process improvement perspective to study adaptive capacity planning in ambulatory surgery centers. Timely capacity adjustment is essential for the surgery center planners as each facility is concerned with the cost and capacity implications of adding/removing specific surgical procedures under the transition toward payment reform models. But the related research is limited. In contrast to the traditional top-down approach to capacity planning, my approach proposes a bottom-up strategy based on optimization methods combined with analytics that are informed by operational-level archival patient flow data. I develop several mathematical formulations and heuristics based on scheduling theory to derive the most cost-efficient capacity solution for the multi-stage structure of surgery centers. In the computational study, I further show how uncertain business parameters may affect capacity planning decisions.

publication date

  • May 2019