Surgical scheduling via optimization and machine learning with long-tailed data : Health care management science, in press.
Health Care Manag Sci
; 26(4): 692-718, 2023 Dec.
Article
em En
| MEDLINE
| ID: mdl-37665543
ABSTRACT
Using data from cardiovascular surgery patients with long and highly variable post-surgical lengths of stay (LOS), we develop a modeling framework to reduce recovery unit congestion. We estimate the LOS and its probability distribution using machine learning models, schedule procedures on a rolling basis using a variety of optimization models, and estimate performance with simulation. The machine learning models achieved only modest LOS prediction accuracy, despite access to a very rich set of patient characteristics. Compared to the current paper-based system used in the hospital, most optimization models failed to reduce congestion without increasing wait times for surgery. A conservative stochastic optimization with sufficient sampling to capture the long tail of the LOS distribution outperformed the current manual process and other stochastic and robust optimization approaches. These results highlight the perils of using oversimplified distributional models of LOS for scheduling procedures and the importance of using optimization methods well-suited to dealing with long-tailed behavior.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizado de Máquina
/
Hospitais
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Health Care Manag Sci
Assunto da revista:
SERVICOS DE SAUDE
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos