Deep reinforcement learning in patient admission process to optimize bed occupancy and revenue
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Publication date
2025
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04B - Conference paper
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2025 7th International Symposium on Computational and Business Intelligence (ISCBI)
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Pages / Duration
56-60
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IEEE
Place of publication / Event location
Macau
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Abstract
This paper discusses the problem of bed allocation with the example of a Swiss rehabilitation center. The aim is to challenge the benchmark of manual patient management by utilizing deep reinforcement learning (DRL) coupled with discrete event simulation to maximize bed occupancy and revenue. The comparison between random allocation, queuing systems, and deep reinforcement learning indicates the opportunities to optimize patient management at admission. Findings show that DRL agents can outperform random allocation and queuing systems when waiting lists are not utilized automatically. The integration of waiting lists significantly enhances the random system and queues, yet DRL agents maintain the highest results.
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2025 7th International Symposium on Computational and Business Intelligence (ISCBI)
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979-8-3315-3378-6
979-8-3315-3377-9
979-8-3315-3377-9
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Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
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Published
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peer-reviewed
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Closed
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Citation
Jakober, L., Hanne, T., & Dornberger, R. (2025). Deep reinforcement learning in patient admission process to optimize bed occupancy and revenue. 2025 7th International Symposium on Computational and Business Intelligence (ISCBI), 56–60. https://doi.org/10.1109/ISCBI64586.2025.11015348