An iterative approach for case mix planning under uncertainty

作     者：Freeman, Nickolas Zhao, Ming Melouk, Sharif 

作者机构：Univ Alabama Dept Informat Syst Stat & Management Sci Tuscaloosa AL 35487 USA Univ Houston Dept Decis & Informat Sci Houston TX USA 

出 版 物：《OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE》 (国际管理科学杂志)

年 卷 期：2018年第76卷

页      面：160-173页

核心收录：

学科分类：12[管理学] 120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 0202[经济学-应用经济学] 02[经济学] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 

主　　题：Health care management Math programming Simulation 

摘      要：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. Published by Elsevier Ltd.