Emergency medicine patient wait time multivariable prediction models : a multicentre derivation and validation study |
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Author: | Walker, Katie1,2,3; Jiarpakdee, Jirayus4; Loupis, Anne3; |
Organizations: |
1Emergency Department, Casey Hospital, Berwick, Victoria, Australia 2Health Services, Faculty of Medicine Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia 3Emergency Department, Cabrini Institute, Melbourne, Victoria, Australia
4Department of Software Systems and Cybersecurity, Monash University, Melbourne, Victoria, Australia
5MADA, Monash University, Clayton, Victoria, Australia 6Emergency Department, Austin Health, Heidelberg, Victoria, Australia 7Department of Emergency Medicine, St Vincent's Hospital Melbourne Pty Ltd, Fitzroy, Victoria, Australia 8Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Victoria, Australia 9Biostatistics, Cabrini Health, Malvern, Victoria, Australia 10Faculty of Medicine Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia 11Ambulance Victoria, Doncaster, Victoria, Australia 12Community Emergency Health and Paramedic Practice, Monash University, Melbourne, Victoria, Australia 13Emergency Program, Monash Health, Clayton, Victoria, Australia 14School of Clinical Sciences, Monash University, Melbourne, Victoria, Australia 15Emergency Medicine, Eastern Health, Melbourne, Victoria, Australia 16Eastern Health Clinical School, Monash University, Melbourne, Victoria, Australia 17Emergency, Gold Coast Hospital and Health Service, Southport, Queensland, Australia 18Griffith University School of Medicine, Gold Coast, Queensland, Australia 19Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Pohjois-Pohjanmaa, Finland |
Format: | article |
Version: | accepted version |
Access: | open |
Online Access: | PDF Full Text (PDF, 0.2 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe2021100649448 |
Language: | English |
Published: |
BMJ,
2022
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Publish Date: | 2021-10-06 |
Description: |
AbstractObjective: Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. Methods: Twelve emergency departments provided 3 years of retrospective administrative data from Australia (2017–2019). 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. Model performance was tested on COVID-19 period data (January to June 2020). Results: There were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period. Conclusions: Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors. see all
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Series: |
Emergency medicine journal |
ISSN: | 1472-0205 |
ISSN-E: | 1472-0213 |
ISSN-L: | 1472-0205 |
Volume: | 39 |
Pages: | 386 - 393 |
DOI: | 10.1136/emermed-2020-211000 |
OADOI: | https://oadoi.org/10.1136/emermed-2020-211000 |
Type of Publication: |
A1 Journal article – refereed |
Field of Science: |
113 Computer and information sciences 3121 General medicine, internal medicine and other clinical medicine |
Subjects: | |
Funding: |
The Australian government, Medical Research Future Fund, via Monash Partners, funded this study. Researchers contributed in-kind donations of time. The Cabrini Institute and Monash University provided research infrastructure support. |
Copyright information: |
© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ. Published on this non-commercial repository with the CC-BY-NC license. |
https://creativecommons.org/licenses/by-nc/4.0/ |