Coxian Phase-Type Regression Models for Understanding the Relationship Between Patient Attributes, Overcrowding, and Length of Stay in Hospital Emergency Departments

Laura M. Boyle; Adele H. Marshall; Mark Mackay

doi:10.1007/978-3-030-39694-7_5

Coxian Phase-Type Regression Models for Understanding the Relationship Between Patient Attributes, Overcrowding, and Length of Stay in Hospital Emergency Departments

Laura M. Boyle^*, Adele H. Marshall, Mark Mackay

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Hospital emergency departments (EDs) operate under significant pressure worldwide. Overcrowding is a frequent occurrence, caused by a combination of high presentation numbers, and long delays in the admission of patients due to a lack of availability of hospital beds. Understanding the factors which influence length of stay (LoS) in the ED is a vital aspect of any strategy to improve patient flow. The determinants of ED patient flow are complex and varied, due to the diverse population of patients competing for limited resources. This research uses Coxian phase-type distributions to cluster patients into groups by their LoS, using a unique diagram for improved communication of patient flow issues. A novel application of survival analysis is presented to simultaneously evaluate the effect of patient attributes, system factors, and overcrowding on ED LoS. The approach is demonstrated with an application to data from a hospital in South Australia.

Original language	English
Title of host publication	Health Care Systems Engineering (HCSE 2019): Proceedings
Editors	Valerie Belanger, Nadia Lahrichi, Ettore Lanzarone, Semih Yalcindag
Publisher	Springer
Pages	53-64
Number of pages	12
ISBN (Print)	9783030396930
DOIs	https://doi.org/10.1007/978-3-030-39694-7_5
Publication status	Published - 16 Apr 2020
Event	4th International Conference on Health Care Systems Engineering, HCSE 2019 - Montréal, Canada Duration: 30 May 2019 → 01 Jun 2019

Publication series

Name	Springer Proceedings in Mathematics and Statistics
Volume	316
ISSN (Print)	2194-1009
ISSN (Electronic)	2194-1017

Conference

Conference	4th International Conference on Health Care Systems Engineering, HCSE 2019
Country/Territory	Canada
City	Montréal
Period	30/05/2019 → 01/06/2019

Keywords

Coxian phase-type distributions
Emergency department overcrowding
Hospital length of stay
Patient flow

ASJC Scopus subject areas

General Mathematics

Access to Document

10.1007/978-3-030-39694-7_5Licence: Unspecified

Cite this

Boyle, L. M., Marshall, A. H., & Mackay, M. (2020). Coxian Phase-Type Regression Models for Understanding the Relationship Between Patient Attributes, Overcrowding, and Length of Stay in Hospital Emergency Departments. In V. Belanger, N. Lahrichi, E. Lanzarone, & S. Yalcindag (Eds.), Health Care Systems Engineering (HCSE 2019): Proceedings (pp. 53-64). (Springer Proceedings in Mathematics and Statistics; Vol. 316). Springer. https://doi.org/10.1007/978-3-030-39694-7_5

Boyle, Laura M. ; Marshall, Adele H. ; Mackay, Mark. / Coxian Phase-Type Regression Models for Understanding the Relationship Between Patient Attributes, Overcrowding, and Length of Stay in Hospital Emergency Departments. Health Care Systems Engineering (HCSE 2019): Proceedings. editor / Valerie Belanger ; Nadia Lahrichi ; Ettore Lanzarone ; Semih Yalcindag. Springer, 2020. pp. 53-64 (Springer Proceedings in Mathematics and Statistics).

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abstract = "Hospital emergency departments (EDs) operate under significant pressure worldwide. Overcrowding is a frequent occurrence, caused by a combination of high presentation numbers, and long delays in the admission of patients due to a lack of availability of hospital beds. Understanding the factors which influence length of stay (LoS) in the ED is a vital aspect of any strategy to improve patient flow. The determinants of ED patient flow are complex and varied, due to the diverse population of patients competing for limited resources. This research uses Coxian phase-type distributions to cluster patients into groups by their LoS, using a unique diagram for improved communication of patient flow issues. A novel application of survival analysis is presented to simultaneously evaluate the effect of patient attributes, system factors, and overcrowding on ED LoS. The approach is demonstrated with an application to data from a hospital in South Australia.",

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Boyle, LM , Marshall, AH & Mackay, M 2020, Coxian Phase-Type Regression Models for Understanding the Relationship Between Patient Attributes, Overcrowding, and Length of Stay in Hospital Emergency Departments. in V Belanger, N Lahrichi, E Lanzarone & S Yalcindag (eds), Health Care Systems Engineering (HCSE 2019): Proceedings. Springer Proceedings in Mathematics and Statistics, vol. 316, Springer, pp. 53-64, 4th International Conference on Health Care Systems Engineering, HCSE 2019, Montréal, Canada, 30/05/2019. https://doi.org/10.1007/978-3-030-39694-7_5

Coxian Phase-Type Regression Models for Understanding the Relationship Between Patient Attributes, Overcrowding, and Length of Stay in Hospital Emergency Departments. / Boyle, Laura M.; Marshall, Adele H.; Mackay, Mark.
Health Care Systems Engineering (HCSE 2019): Proceedings. ed. / Valerie Belanger; Nadia Lahrichi; Ettore Lanzarone; Semih Yalcindag. Springer, 2020. p. 53-64 (Springer Proceedings in Mathematics and Statistics; Vol. 316).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Boyle LM , Marshall AH, Mackay M. Coxian Phase-Type Regression Models for Understanding the Relationship Between Patient Attributes, Overcrowding, and Length of Stay in Hospital Emergency Departments. In Belanger V, Lahrichi N, Lanzarone E, Yalcindag S, editors, Health Care Systems Engineering (HCSE 2019): Proceedings. Springer. 2020. p. 53-64. (Springer Proceedings in Mathematics and Statistics). doi: 10.1007/978-3-030-39694-7_5