2947. Two-stage stochastic programming for scheduling telemedicine appointments
Invited abstract in session MD-11: Scheduling in healthcare, stream OR in Healthcare (ORAHS).
Monday, 14:30-16:00Room: Clarendon SR 1.03
Authors (first author is the speaker)
| 1. | Charlotte Marshall
|
| Cardiff University | |
| 2. | Daniel Gartner
|
| School of Mathematics, Cardiff University | |
| 3. | Geraint Palmer
|
| School of Mathematics, Cardiff University | |
| 4. | Paul Harper
|
| School of Mathematics, Cardiff University | |
| 5. | Alka Ahuja
|
| TEC Cymru | |
| 6. | Gemma Johns
|
| Tec cymru |
Abstract
With telemedicine coming to the forefront during the COVID-19 pandemic, flexibility in terms of modes of care delivery has emerged. In this work we consider the scheduling of patients' appointments via three different modes of delivery: traditional face-to-face, video conferencing platforms, and telephone. Because the length of patients' appointments are subject to stochastic variation and patients may be no-shows, we introduce a two-stage stochastic programming formulation to schedule these appointments. The first stage aims to maximise patient and clinician preferences for delivery mode, while the second stage minimises patient waiting time. We generate test instances that are informed by clinical practice and validate the model using input from our partners, TEC Cymru, within the U.K.'s National Health Service (NHS). The mathematical modelling will provide booking clerks and admin personnel a tool to schedule appointments more efficiently.
Keywords
- Health Care
- Scheduling
- Stochastic Optimization
Status: accepted
Back to the list of papers