Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study

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 three years of retrospective administrative data from Australia (2017-19). 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 minutes. Individual site model prediction median absolute errors varied from +/-22.6 minutes (95%CI 22.4,22.9) to +/- 44.0 minutes (95%CI 43.4,44.4). Global model prediction median absolute errors varied from +/-33.9 minutes (95%CI 33.4, 34.0) to +/-43.8 minutes (95%CI 43.7, 43.9). Random forest and linear regression models performed the best, rolling average models under-estimated 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.

The influence of ambulance offload time on 30‐day risks of death and re‐presentation for patients with chest pain

“…We included 213 544 people with chest pain transported by ambulance to EDs (mean age, 62 [SD,18] years; 109 027 women [51%]). The median offload time increased from 21 (IQR, [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] minutes in 2015 to 24 (IQR, 17-37) minutes during the first half of 2019. Three offload time tertiles were defined to include approximately equal patient numbers: tertile 1 (0-17 minutes), tertile 2 (18-28 minutes), and tertile 3 (more than 28 minutes).…”

“…18 Proposals for improving ambulance-to-ED transfer times include financial penalties for hospitals with longer offload times, algorithms that predict ambulance waiting times and direct ambulances to less crowded hospitals, dedicated offload zones, and offload nursing coordinators. [19][20][21] However, these measures have limited benefits and may bring their own problems, such as increased mortality risk for patients with myocardial infarction if their ambulance is diverted. 22 As offload times are primarily determined by ED and hospital overcrowding, effective solutions will require increased investment in hospital resources, staffing, and infrastructure, as well as public health education about the appropriate use of emergency ambulance services.…”