An iterative approach for case mix planning under uncertainty. (April 2018)

• Record Type: Journal Article
• Authors: Freeman, Nickolas; Zhao, Ming; Melouk, Sharif
• Is Part Of: Omega. Volume 76(2018)
• Page Start: 160
• Page End: 173
• Publication Date: 2018-04
• Subjects: Health care management -- Math programming -- Simulation; Management -- Periodicals; 658.4005
• DOI: 10.1016/j.omega.2017.04.006
• Languages: English
• ISSNs: 0305-0483

Abstract

Highlights: A solution pool approach for case mix planning is proposed. Simulation is employed to evaluate candidate solutions with respect to a wide range of performance measures. We conduct experiments based on a dataset that documents patient admissions in 203 U.S. hospitals. Results show our solution pool approach outperforms a more traditional, single-solution approach. Abstract: Case mix planning refers to allocating available time in the operating rooms composing an operating theater (OT) among different surgical specialties. Case mix planning is an important tool for achieving the goals of a hospital with respect to quality of care and financial position. Case mix planning is becoming increasingly prevalent as hospital reimbursement continues to shift from fee-for-service to reimbursement based on diagnostic-related groups. Existing approaches for case mix planning in the academic and medical literature follow a traditional approach that identifies a single "optimal" solution. To ensure tractability, such approaches often exclude several complicating factors such as uncertain patient arrivals, uncertain operation time requirements, and the arrival of patients requiring urgent care. The exclusions limit the applicability of the solution in practice. Thus, we develop a multi-phase approach that utilizes mathematical programming and simulation to generate a pool of candidate solutions. Using simulation allows us to evaluate each candidate solution with respect to a broad range of strategic and operational performance measures including expected patient reimbursement, overutilization of the OT, and the utilization of downstream recovery wards. Providing a pool of solutions, instead of a single solution, gives decision-makers several options from which they may select based on hospital goals. We conduct experiments based on a large, publicly available dataset that documents patient admissions in 203 U.S. hospitals. In comparison to a more traditional single-solution approach, we show that our solution pool approach identifies case mix plans with higher expected patient reimbursement, lower overutilization of OT time, and lower variability in the number of beds required in downstream recovery wards.