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 Walker, Katie
A1 Jiarpakdee, Jirayus
A1 Loupis, Anne
A1 Tantithamthavorn, Chakkrit
A1 Joe, Keith
A1 Ben-Meir, Michael
A1 Akhlaghi, Hamed
A1 Hutton, Jennie
A1 Wang, Wei
A1 Stephenson, Michael
A1 Blecher, Gabriel
A1 Paul, Buntine
A1 Sweeny, Amy
A1 Turhan, Burak
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.