164. Bed census prediction combining expert opinion and patient statistics
Contributed abstract in session MD-3: Integrated Planning in Healthcare /1, stream Regular talks.
Monday, 13:50-15:00Room: Room S2
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
Predictions of bed census are crucial for hospital capacity management choices, encompassing ward sizing, staffing, patient bed assignments, and surgical scheduling. Presently, these predictions heavily rely on doctors' estimated Expected Discharge Date (EDD). This presentation introduces two probabilistic models that integrate EDD with LoS distributions derived from data. By employing the Poisson Binomial distribution and probabilistic convolution, we generate full census distributions. Applying our approach to real hospital data demonstrates its ability to provide precise predictions, leading to valuable managerial insights. We will also show the implementation of our model in our partnering hospital.
Keywords
- Forecasting
- Patient flow
- Integrated planning of health services
Status: accepted
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