Projects per year
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.
- 1 Finished
Investigation of the utility and the user experiences of widespread access to accurate emergency department wait times.
Walker, K., Ben-Mier, M., Joe, K., Papatheohari, J., Rankin, D., Stephenson, M., Martini, E., Lowthian, J., Stephenson, M., Blecher, G., Rodda, H., Turhan, B., Tantithamthavorn, C., Aleti, A. & Jiarpakdee, J.
1/07/19 → 30/06/21