Resident Scheduling in Teaching Hospitals

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Abstract

After graduation, physicians receive further training in a medical domain like anesthesiology. There are 57 medical specialties in Germany in total. The high cost pressures of hospitals and the changing view of the medical profession regarding the work-life balance have led to recruitment problems and low employee satisfaction in many places. A promising approach to counter this problem is objective and structured training planning. This research project mainly deals with medical residents’ strategic and tactical-operative training scheduling. In addition to relieving the medical staff currently responsible for the planning process, this research project increases the predictability of structured training. This allows hospitals to increase the quality of their training and, consequently, their attractiveness to other hospitals. In addition, supervisors from different departments can better assess residents’ knowledge and thus keep the level of service, which is particularly important in hospitals, permanently high even when changing residents. From the residents’ point of view, a well-structured training schedule enables a high degree of information. Therefore, residents are no longer surprised by a short-term change of department and have a direct insight into their training progress. A real-world case study evaluates the mathematical formulations and the solution approaches.
Original languageEnglish
Title of host publicationOperations Research Proceedings 2022
Subtitle of host publicationSelected Papers of the Annual International Conference of the German Operations Research Society (GOR), Karlsruhe, Germany, September 6-9, 2022
EditorsOliver Grothe, Stefan Nickel, Steffen Rebennack, Oliver Stein
Place of PublicationCham
PublisherSpringer
Chapter5
Pages35-41
Number of pages7
ISBN (Electronic)9783031249075
ISBN (Print)9783031249068
DOIs
Publication statusPublished - 2023

Publication series

NameLecture Notes in Operations Research
PublisherSpringer, Cham

Funding

This research project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) and Grant no. 405488489.

FundersFunder number
Deutsche Forschungsgemeinschaft405488489
Deutsche Forschungsgemeinschaft

    Keywords

    • OR in health services
    • Mixed integer programming
    • Real-world application
    • Stochastic optimization

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