Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study. Issue 5 (25th August 2021)

• Record Type: Journal Article
• 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
• Other Names: author non-byline.; Rosler Rachel author non-byline.; Stephenson Melanie author non-byline.; Hansen Kim author non-byline.; Martini Ella author non-byline.; Rodda Hamish author non-byline.; Lowthian Judy author non-byline.
• Is Part Of: Emergency medicine journal. Volume 39:Issue 5(2022)
• Page Start: 386
• Page End: 393
• Publication Date: 2021-08-25
• Subjects: emergency care systems -- efficiency -- emergency departments -- emergency department management -- emergency department operations -- emergency department utilisation; Emergency medicine -- Periodicals; 616.02505
• DOI: 10.1136/emermed-2020-211000
• Languages: English
• ISSNs: 1472-0205

Abstract

Abstract : 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.