Near real-time prediction of urgent care hospital performance metrics using scalable random forest algorithm: A multi-site development

Theresia A. Budiman, Charlotte R. James, Nicholas C. Howlett, Richard M. Wood

Research output: Contribution to journalArticlepeer-review

1 Citation (SciVal)

Abstract

While previous studies have shown the potential value of predictive modelling for emergency care, few models have been implemented for producing near real-time predictions across various demand, utilisation and performance metrics. In this study, 33 independent Random Forest (RF) algorithms were developed to forecast 11 urgent care metrics over a 24-hour period across three hospital sites in an Integrated Care System (ICS) in South West England. Metrics included: ambulance handover delay; emergency department occupancy; and patients awaiting admission. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Symmetric Mean Absolute Percentage Error (SMAPE) were used to assess the performance of RF and compare it to two alternative models: naïve baseline (NB) and Auto-Regressive Integrated Moving Average (ARIMA). Using these measures, RF outperformed NB and ARIMA in 76% (N = 25/33) of urgent care metrics according to SMAPE, 88% (N = 29/33) according to MAE and 91% (N = 30/33) according to RMSE. The RFs developed in this study have been implemented within the local ICS, providing predictions on an hourly basis that can be accessed by local healthcare planners and managers.

Original languageEnglish
Article number100169
Number of pages8
JournalHealthcare Analytics
Volume3
Early online date11 Apr 2023
DOIs
Publication statusPublished - 30 Nov 2023

Bibliographical note

Funding Information:
CJ is funded by NIHR Bristol BRC, United Kingdom (BRC_1215_20011) and NIHR Research Capability Funding, United Kingdom (RCF 21/22 – 4.2). Otherwise, this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability:
The authors do not have permission to share data

Keywords

  • Forecasting
  • Machine learning
  • Predictive analytics
  • Random forest
  • Time series
  • Urgent care

ASJC Scopus subject areas

  • Health Informatics
  • Analytical Chemistry

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