Modeling physician scheduling for high-cost areas in hospitals (Phase 2)

• Applicants: Professor Dr. Jens Otto Brunner; Professorin Dr. Katja Schimmelpfeng
• Subject Area: Operations Management and Computer Science for Business Administration
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 438507036

In many industrialized nations, the healthcare industry continues to face rising demand for healthcare services, immense cost pressure, and a shortage of qualified specialists. In particular, highly qualified physicians represent a scarce resource in hospitals, requiring efficient scheduling. Building on the findings from the first funding period, which focused primarily on staff scheduling and its interdependencies with operating room planning, the focus of the second funding period is on operating room planning with the patient interface. Particular attention will be paid to horizontal (i.e., along the patient flow) and vertical (i.e., strategic case-mix planning, tactical master surgery scheduling, surgical patient planning) integration, and innovative optimization models will be researched. Data-driven mathematical optimization methods (i.e., methods from operations research/management science) will be used to solve the evolving optimization models in the context of patient-centered operating room planning. A methodological focus is placed on the integration of AI-based methods as an essential component or (resp.) complement to mathematical optimization procedures. The cooperation partner owns (collected?) extensive, highly valuable data sets on the planning problems described and provides the necessary medical expertise. Particularly, these datasets provide a very good starting point for the development and application of AI-based planning approaches. The examination, analysis, and optimization of patient-focused operating room planning offers great potential for basic research. This research project will investigate the fundamental structures of mathematical optimization models. The acquired knowledge will, in turn, be used to develop new algorithms. The researched models and developed methods can be easily transferred and adapted to other planning problems in hospitals. Furthermore, the findings will be applicable to the entire healthcare sector and serve as a basis for researchers' further investigations.

• DFG Programme: Research Grants
• Co-Investigator: Professor Dr. Axel R. Heller