Balancing control and autonomy in master surgery scheduling: Benefits of ICU quotas for recovery units

Steffen Heider, Jan Schoenfelder, Thomas Koperna and Jens O. Brunner ()
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Steffen Heider: University of Augsburg
Jan Schoenfelder: University of Augsburg
Thomas Koperna: Universitätsklinikum Augsburg
Jens O. Brunner: University of Augsburg

Health Care Management Science, 2022, vol. 25, issue 2, No 9, 332 pages

Abstract: Abstract When scheduling surgeries in the operating theater, not only the resources within the operating theater have to be considered but also those in downstream units, e.g., the intensive care unit and regular bed wards of each medical specialty. We present an extension to the master surgery schedule, where the capacity for surgeries on ICU patients is controlled by introducing downstream-dependent block types – one for both ICU and ward patients and one where surgeries on ICU patients must not be performed. The goal is to provide better control over post-surgery patient flows through the hospital while preserving each medical specialty’s autonomy over its operational surgery scheduling. We propose a mixed-integer program to determine the allocation of the new block types within either a given or a new master surgery schedule to minimize the maximum workload in downstream units. Using a simulation model supported by seven years of data from the University Hospital Augsburg, we show that the maximum workload in the intensive care unit can be reduced by up to 11.22% with our approach while maintaining the existing master surgery schedule. We also show that our approach can achieve up to 79.85% of the maximum workload reduction in the intensive care unit that would result from a fully centralized approach. We analyze various hospital setting instances to show the generalizability of our results. Furthermore, we provide insights and data analysis from the implementation of a quota system at the University Hospital Augsburg.

Keywords: Healthcare; Master surgery schedule; Operating theater; Downstream; Operations research; Operations management (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10729-021-09588-8

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