TY - JOUR
T1 - Bed census prediction combining expert opinion and patient statistics
AU - Bos, Hayo
AU - Baas, Stef
AU - Boucherie, Richard J.
AU - Hans, Erwin W.
AU - Leeftink, Gréanne
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2025/6
Y1 - 2025/6
N2 - 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 paper introduces two probabilistic models that integrate EDD with Length of Stay (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.
AB - 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 paper introduces two probabilistic models that integrate EDD with Length of Stay (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.
KW - UT-Hybrid-D
KW - Bed census distribution
KW - Expected Discharge Date
KW - Operations Research in Health Services
KW - Poisson binomial
KW - Bayesian methods
UR - http://www.scopus.com/inward/record.url?scp=85212842612&partnerID=8YFLogxK
U2 - 10.1016/j.omega.2024.103262
DO - 10.1016/j.omega.2024.103262
M3 - Article
AN - SCOPUS:85212842612
SN - 0305-0483
VL - 133
JO - Omega (United Kingdom)
JF - Omega (United Kingdom)
M1 - 103262
ER -