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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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