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894. Optimizing dynamic reserved resource capacity in appointment scheduling with elective and semi-urgent patients
Invited abstract in session WC-10: Capacity and treatment planning in healthcare, stream OR in Health Services (ORAHS).
Wednesday, 12:30-14:00Room: 11 (building: 116)
Authors (first author is the speaker)
| 1. | Jedidja Lok - Visser
|
| CHOIR, University of Twente | |
| 2. | Gina van Vemde
|
| Isala | |
| 3. | Heleen den Hertog
|
| Isala | |
| 4. | Jan Gerard Maring
|
| Clinical Pharmacy & Connected Care, Isala | |
| 5. | Gréanne Leeftink
|
| CHOIR, University of Twente |
Abstract
In appointment scheduling, it is a common practice to reserve a number of slots for (semi-)urgent demand arrivals, that require service quickly. The other slots are then given to clients that request an appointment upfront. To determine the number of reserved slots, the (semi-)urgent demand arrivals are often modelled as a distribution with static or seasonal distribution parameters. However, in many appointment scheduling processes, more information becomes available about the urgent demand arrivals over time. An example of these processes is a radiology department, where the number of patients present in the hospital could forecast the required number of emergency scans.
In this study, we propose near-optimal scheduling policies that reserve slots for (semi-)urgent clients, using updated information on the arrival distribution of (semi-)urgent clients in the near future. We formulate the sequential decision making problem as a Markov decision process. We test this model on a Dutch real-life case study in the neurology department of Isala Clinics, Zwolle. This neurology department implemented a brain rehabilitation program in combination with an e-coach for stroke patients, where we can use the number of active patients in monitoring to forecast the number of semi-urgent requests for outpatient appointments. We discuss first results on this practical case study and theoretical instances, and present managerial implications of our near-optimal policies.
Keywords
- Scheduling
- Health Care
- Stochastic Optimization
Status: accepted
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