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.
- emergency care systems
- emergency departments
- emergency department management
- emergency department operations
- emergency department utilisation
Data availability statement
No data are available.
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Handling editor Shammi L Ramlakhan
Twitter @gabyblech, @EpidemicAmy
Collaborators Rachel Rosler: network sponsor, Melanie Stephenson: literature review, Kim Hansen: risk advisor, Ms Ella Martini: consumer, Dr Hamish Rodda: emergency informatics advisor, project sponsor, Dr Judy Lowthian: district nursing researcher.
Contributors Principal investigator: KW. Funding: KJ, KW, MB-M. Study design and protocol: KW, BT, CT, JJ, WW. Study protocol revisions: all authors. Ethics/governance: KW, AL. Site chief investigators: HA, GB, BP, KW, AS. Data collection: AL, HA, BP, KW, AS. Data analysis: JJ, CT, BT. Manuscript: KW, JJ, CT, BT. Manuscript revisions: all authors. Manuscript guaranteed by KW and BT.
Funding The Australian government, Medical Research Future Fund, via Monash Partners, funded this study. Researchers contributed in-kind donations of time. The Cabrini Institute and Monash University provided research infrastructure support.
Competing interests Some authors and collaborators are emergency physicians or directors, and others work in community health (prehospital and district nursing). One collaborator is a consumer. The Australian government, Medical Research Future Fund, via Monash Partners, funded this study. Researchers contributed in-kind donations of time. The Cabrini Institute and Monash University provided research infrastructure support.
Provenance and peer review Not commissioned; externally peer reviewed.
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