Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/27373
Title: Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study.
Austin Authors: Walker, Katie;Jiarpakdee, Jirayus;Loupis, Anne;Tantithamthavorn, Chakkrit;Joe, Keith;Ben-Meir, Michael ;Akhlaghi, Hamed;Hutton, Jennie;Wang, Wei;Stephenson, Michael;Blecher, Gabriel;Paul, Buntine;Sweeny, Amy;Turhan, Burak
Affiliation: Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Pohjois-⁠Pohjanmaa, Finland
School of Clinical Sciences, Monash University, Melbourne, Victoria, Australia
Emergency Medicine, Eastern Health, Melbourne, Victoria, Australia
Eastern Health Clinical School, Monash University, Melbourne, Victoria, Australia
Emergency, Gold Coast Hospital and Health Service, Southport, Queensland, Australia
Griffith University School of Medicine, Gold Coast, Queensland, Australia
Biostatistics, Cabrini Health, Malvern, Victoria, Australia
Faculty of Medicine Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
Ambulance Victoria, Doncaster, Victoria, Australia
Community Emergency Health and Paramedic Practice, Monash University, Melbourne, Victoria, Australia
Emergency Department, Casey Hospital, Berwick, Victoria, Australia
Health Services, Faculty of Medicine Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
Emergency Department, Cabrini Institute, Melbourne, Victoria, Australia
Department of Software Systems and Cybersecurity, Monash University, Melbourne, Victoria, Australia
MADA, Monash University, Clayton, Victoria, Australia
Emergency
Department of Emergency Medicine, St Vincent's Hospital Melbourne Pty Ltd, Fitzroy, Victoria, Australia
Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Victoria, Australia
Emergency Program, Monash Health, Clayton, Victoria, Australia
Issue Date: May-2022
Date: 2021-08-25
Publication information: Emergency medicine journal : EMJ 2022; 39(5): 386-393
Abstract: 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. 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). 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. 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.
URI: https://ahro.austin.org.au/austinjspui/handle/1/27373
DOI: 10.1136/emermed-2020-211000
ORCID: 0000-0002-5313-5852
0000-0001-8537-2011
0000-0001-8392-5612
Journal: Emergency Medicine Journal : EMJ
PubMed URL: 34433615
Type: Journal Article
Subjects: efficiency
emergency care systems
emergency department management
emergency department operations
emergency department utilisation
emergency departments
Appears in Collections:Journal articles

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