229. Steering Through Uncertainties: Dynamic Integrated Patient-Room and Nurse-Patient Assignment in Hospital Wards
Contributed abstract in session MD-3: Integrated Planning in Healthcare /1, stream Regular talks.
Monday, 13:50-15:00Room: Room S2
Authors (first author is the speaker)
| 1. | Fabian Schäfer
|
| Supply and Value Chain Management, Technical University of Munich | |
| 2. | Emily Lex
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| Supply and Value Chain Management, Technical University Munich | |
| 3. | Alexander Hübner
|
| Supply and Value Chain Management, Technical University Munich |
Abstract
Optimizing patient-to-room and nurse-to-patient assignments is crucial for efficient hospital workflows, high-quality care, and patient and staff satisfaction. Integrating both assignment problems enables the optimization of additional objectives that depend on the interaction of the two assignment problems. For example, minimizing the walking distances of nurses or assigning the minimum number of nurses to patients in the same room to mitigate negative effects, such as the spread of infections between rooms by nurses or the disturbance of patients. Existing literature tackles the static version of this integrated problem, assuming full prior knowledge of patient and nurse parameters. However, real-world hospital operations are rife with uncertainties, including patient no-shows, emergency admissions, fluctuating length of stays, and unforeseen nurse absences. Enhancing predictability and forecasting reliability necessitates accounting for stochastic variations within the planning horizon. We have developed a decision support model that addresses the dynamic patient-to-room and nurse-to-patient assignment. The model is presented as a mixed integer optimization problem. We present an efficient heuristic to solve the assignment problem under data uncertainty. We conduct computational experiments on real-world and artificially generated instances. A comparative analysis against the static problem formulation underscores the efficacy and superiority of our dynamic extensions.
Keywords
- Patient scheduling
- Decision support
- Staffing and capacity planning
Status: accepted
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