Abstract
Optimizing the scheduling of operating rooms is quite a challenging task, as different aspects, some of which the medical personnel is not completely aware of, may have a strong impact on the scheduling and need to be taken into account. This work aims at addressing such a problem by proposing a framework that combines process analysis and operations research. Process mining techniques are used for analysing interventional radiology data collected from the information system of a hospital and identifying delays and lagging cases, as well as the causes of these delays. Leveraging the knowledge acquired by looking at data (e.g., the procedures that are more often delayed), an optimization model able to take into account these aspects is designed. This paper describes the preliminary results of a proof-of-concept based on 3 months real-life data. The results show that, taking into account the information discovered from data, allows for obtaining a more accurate scheduling.
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Whether a procedure is clean or dirty is known in advance, and it is not an information we generate according to some estimated frequency.
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Acknowledgments
The research is part of the “Circular Health for Industry” project funded by “Compagnia di San Paolo”, call “Intelligenza Artificiale, uomo e societá”.
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Di Cunzolo, M. et al. (2023). Combining Process Mining and Optimization: A Scheduling Application in Healthcare. In: Cabanillas, C., Garmann-Johnsen, N.F., Koschmider, A. (eds) Business Process Management Workshops. BPM 2022. Lecture Notes in Business Information Processing, vol 460. Springer, Cham. https://doi.org/10.1007/978-3-031-25383-6_15
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