RT Journal Article SR Electronic T1 Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study JF Emergency Medicine Journal JO Emerg Med J FD BMJ Publishing Group Ltd and the British Association for Accident & Emergency Medicine SP 386 OP 393 DO 10.1136/emermed-2020-211000 VO 39 IS 5 A1 Katie Walker A1 Jirayus Jiarpakdee A1 Anne Loupis A1 Chakkrit Tantithamthavorn A1 Keith Joe A1 Michael Ben-Meir A1 Hamed Akhlaghi A1 Jennie Hutton A1 Wei Wang A1 Michael Stephenson A1 Gabriel Blecher A1 Buntine Paul A1 Amy Sweeny A1 Burak Turhan A1 , YR 2022 UL http://emj.bmj.com/content/39/5/386.abstract AB 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.No data are available.