Hospital length of stay: A cross-specialty analysis and Beta-geometric model
Issued Date
2023-01-01
Resource Type
eISSN
19326203
Scopus ID
2-s2.0-85164846155
Pubmed ID
37440494
Journal Title
PloS one
Volume
18
Issue
7
Rights Holder(s)
SCOPUS
Bibliographic Citation
PloS one Vol.18 No.7 (2023) , e0288239
Suggested Citation
Dehouche N., Viravan S., Santawat U., Torsuwan N., Taijan S., Intharakosum A., Sirivatanauksorn Y. Hospital length of stay: A cross-specialty analysis and Beta-geometric model. PloS one Vol.18 No.7 (2023) , e0288239. doi:10.1371/journal.pone.0288239 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/88062
Title
Hospital length of stay: A cross-specialty analysis and Beta-geometric model
Author's Affiliation
Other Contributor(s)
Abstract
BACKGROUND: The typical hospital Length of Stay (LOS) distribution is known to be right-skewed, to vary considerably across Diagnosis Related Groups (DRGs), and to contain markedly high values, in significant proportions. These very long stays are often considered outliers, and thin-tailed statistical distributions are assumed. However, resource consumption and planning occur at the level of medical specialty departments covering multiple DRGs, and when considered at this decision-making scale, extreme LOS values represent a significant component of the distribution of LOS (the right tail) that determines many of its statistical properties. OBJECTIVE: To build actionable statistical models of LOS for resource planning at the level of healthcare units. METHODS: Through a study of 46, 364 electronic health records over four medical specialty departments (Pediatrics, Obstetrics/Gynecology, Surgery, and Rehabilitation Medicine) in the largest hospital in Thailand (Siriraj Hospital in Bangkok), we show that the distribution of LOS exhibits a tail behavior that is consistent with a subexponential distribution. We analyze some empirical properties of such a distribution that are of relevance to cost and resource planning, notably the concentration of resource consumption among a minority of admissions/patients, an increasing residual LOS, where the longer a patient has been admitted, the longer they would be expected to remain admitted, and a slow convergence of the Law of Large Numbers, making empirical estimates of moments (e.g. mean, variance) unreliable. RESULTS: We propose a novel Beta-Geometric model that shows a good fit with observed data and reproduces these empirical properties of LOS. Finally, we use our findings to make practical recommendations regarding the pricing and management of LOS.