University of Oulu

Katie J. Walker, Jirayus Jiarpakdee, Anne Loupis, Chakkrit Tantithamthavorn, Keith Joe, Michael Ben-Meir, Hamed Akhlaghi, Jennie Hutton, Wei Wang, Michael Stephenson, Gabriel Blecher, Paul Buntine, Amy Sweeny, Burak Turhan, Predicting Ambulance Patient Wait Times: A Multicenter Derivation and Validation Study, Annals of Emergency Medicine, 2021,, ISSN 0196-0644, https://doi.org/10.1016/j.annemergmed.2021.02.010

Predicting ambulance patient wait times : a multicenter derivation and validation study

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Author: Walker, Katie J.1,2,3,4; Jiarpakdee, Jirayus5; Loupis, Anne2,4;
Organizations: 1Cabrini Emergency Department, Malvern, Melbourne, Victoria, Australia
2Cabrini Institute, Malvern, Melbourne, Victoria, Australia
3Casey Emergency Department, Berwick, Melbourne, Victoria, Australia
4School of Clinical Sciences at Monash Health, Monash University, Clayton, Melbourne, Victoria, Australia
5Department of Software Systems and Cybersecurity, Monash University, Clayton, Melbourne, Victoria, Australia
6Monash Art, Design and Architecture, Monash University, Caulfield, Melbourne, Victoria, Australia
7School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
8Emergency Department, St Vincent’s Hospital, Fitzroy, Melbourne, Victoria, Australia
9Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Melbourne, Victoria, Australia
10Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
11Ambulance Victoria, Doncaster, Melbourne, Victoria, Australia
12Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
13Department of Community Emergency Health and Paramedic Practice, Frankston, Melbourne, Victoria, Australia
14Monash Medical Centre, Emergency Department, Clayton, Melbourne, Victoria, Australia
15Emergency Department, Box Hill Hospital, Eastern Health, Box Hill, Melbourne, Victoria, Australia
16Eastern Health Clinical School, Monash University, Box Hill, Melbourne, Victoria, Australia
17Emergency Department, Gold Coast University Hospital, Southport, Queensland, Australia
18Faculty of Health Sciences and Medicine, Bond University, Robina, Queensland, Australia
19Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe2021051730029
Language: English
Published: Elsevier, 2021
Publish Date: 2022-05-08
Description:

Abstract

Study objective: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door–to–off-stretcher wait times that are applicable to a wide variety of emergency departments.

Methods: Nine emergency departments provided 3 years (2017 to 2019) of retrospective administrative data from Australia. Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine-learning models were developed to predict wait times at each site and were internally and externally validated.

Results: There were 421,894 episodes analyzed, and median site off-load times varied from 13 (interquartile range [IQR], 9 to 20) to 29 (IQR, 16 to 48) minutes. The global site prediction model median absolute errors were 11.7 minutes (95% confidence interval [CI], 11.7 to 11.8) using linear regression and 12.8 minutes (95% CI, 12.7 to 12.9) using elastic net. The individual site model prediction median absolute errors varied from the most accurate at 6.3 minutes (95% CI, 6.2 to 6.4) to the least accurate at 16.1 minutes (95% CI, 15.8 to 16.3). The model technique performance was the same for linear regression, random forests, elastic net, and rolling average. The important variables were the last k-patient average waits, triage category, and patient age. The global model performed at the lower end of the accuracy range compared with models for the individual sites but was within tolerable limits.

Conclusion: Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables.

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Series: Annals of emergency medicine
ISSN: 0196-0644
ISSN-E: 1097-6760
ISSN-L: 0196-0644
Volume: In Press
Issue: In Press
Pages: 1 - 10
DOI: 10.1016/j.annemergmed.2021.02.010
OADOI: https://oadoi.org/10.1016/j.annemergmed.2021.02.010
Type of Publication: A1 Journal article – refereed
Field of Science: 3142 Public health care science, environmental and occupational health
Subjects:
Copyright information: © 2021 by the American College of Emergency Physicians. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
  https://creativecommons.org/licenses/by-nc-nd/4.0/