Walker K, Jiarpakdee J, Loupis A, et al, Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study, Emergency Medicine Journal Published Online First: 25 August 2021. doi: 10.1136/emermed-2020-211000
Emergency medicine patient wait time multivariable prediction models : a multicentre derivation and validation study
|Author:||Walker, Katie1,2,3; Jiarpakdee, Jirayus4; Loupis, Anne3;|
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
|Online Access:||PDF Full Text (PDF, 0.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021100649448
|Publish Date:|| 2021-10-06
Objective: 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.
Emergency medicine journal
|Pages:||386 - 393|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
3121 General medicine, internal medicine and other clinical medicine
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.
© 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.