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711. Predicting next week’s bed census: combining medical expertise with data.
Invited abstract in session WD-10: Admission and discharge, stream OR in Health Services (ORAHS).
Wednesday, 14:30-16:00Room: 11 (building: 116)
Authors (first author is the speaker)
| 1. | Hayo Bos
|
| University of Twente | |
| 2. | Stef Baas
|
| University of Twente | |
| 3. | Richard Boucherie
|
| Stochastich Operations Research, University of Twente | |
| 4. | Erwin W. Hans
|
| Industrial Engineering & Business Information Systems, University of Twente, fac. Behavioral Management and Social Science | |
| 5. | Gréanne Leeftink
|
| CHOIR, University of Twente |
Abstract
Bed census predictions play a key role in hospital capacity management decisions, such as ward dimensioning, staffing decisions, patient-to-bed assignment and the development surgery schedules. Currently, predictions are typically solely based on the doctor’s estimate of the Expected Discharge Date (EDD). In this presentation, we propose two probabilistic models to combine the EDD with the LOS distributions retrieved from ERP data. Using the Poisson Binomial distribution and probabilistic convolution, we obtain the full census distribution. We apply our method on real world hospital data, and it turns out to be very accurate.
Keywords
- Health Care
- Forecasting
- Decision Support Systems
Status: accepted
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