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A Markov decision process with an ADP-based solution for MRI appointment scheduling in Rijnstate

Dijkstra, Sander (2020) A Markov decision process with an ADP-based solution for MRI appointment scheduling in Rijnstate.

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Abstract:Efficient patient scheduling has significant operational, clinical and economical benefits on health care systems by not only increasing the timely access of patients to care but also reducing costs. However, patient scheduling is complex due to, among other aspects, the existence of multiple priority levels, the presence of patient type-resource compatibility constraints, (highly) variable demand and limited capacity. These aspects of patient scheduling make it extremely difficult for a booking agent to manually assess the impact of his/her decisions in order to more efficiently allocate capacity. We present a near-online method to dynamically schedule patients with different access time targets to one of the MRI scanners in hospital Rijnstate in Arnhem, taking into account patient type-resource compatibility constraints and future appointment requests. The goal is to identify effective ways of allocating available service capacity to incoming appointment requests while minimizing the number of patients whose access time exceeds the prespecified, priority-specific target in a cost-effective manner. We formulate this problem as a discounted infinite-horizon Markov Decision Process (MDP). Because the state space is too large for a direct solution, we solve the equivalent linear program through Approximate Dynamic Programming (ADP) to obtain an Approximate Optimal Policy (AOP). Here we use an affine architecture to approximate the value function of the MDP and solve the equivalent linear program through column generation. Using simulation, we compare the performance of the resulting AOP to both easy-to-use rule-based scheduling approaches and approaches based on current patient scheduling practice in Rijnstate for the practical example based on data provided by the Radiology department of Rijnstate. The results indicate that the AOP outperforms the rule-based scheduling approaches in several scenarios. At the same time we realize that, based on the results, the AOP may not deliver the desired result in all scenarios. That is why we also present an extensions of the MDP model.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:31 mathematics
Programme:Applied Mathematics MSc (60348)
Link to this item:http://purl.utwente.nl/essays/80832
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