Abstract
Previous studies have used prescriptive process monitoring to find actionable policies in business processes and conducted case studies in similar domains, such as the loan application process and the traffic fine process. However, care processes tend to be more dynamic and complex. For example, at any stage of a care process, a multitude of actions is possible. In this paper, we follow the reinforcement approach and train a Markov decision process using event data from a care process. The goal was to find optimal policies for staff members when clients are displaying any type of aggressive behavior. We used the reinforcement learning algorithms Q-learning and SARSA to find optimal policies. Results showed that the policies derived from these algorithms are similar to the most frequent actions currently used but provide the staff members with a few more options in certain situations.
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Acknowledgement
This research was supported by the NWO TACTICS project (628.011.004) and Lunet in the Netherlands. We would like to thank the experts from the Lunet for their assistance. We also thank Dr. Shihan Wang and Dr. Ronald Poppe for the invaluable discussions.
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Verhoef, B.J., Lu, X. (2024). Using Reinforcement Learning to Optimize Responses in Care Processes: A Case Study on Aggression Incidents. In: De Weerdt, J., Pufahl, L. (eds) Business Process Management Workshops. BPM 2023. Lecture Notes in Business Information Processing, vol 492. Springer, Cham. https://doi.org/10.1007/978-3-031-50974-2_5
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