Application of a Machine Learning-Based Decision Support Tool to Improve an Injury Surveillance System Workflow

Appl Clin Inform. 2022 May;13(3):700-710. doi: 10.1055/a-1863-7176. Epub 2022 May 29.

Abstract

Background: Emergency department (ED)-based injury surveillance systems across many countries face resourcing challenges related to manual validation and coding of data.

Objective: This study describes the evaluation of a machine learning (ML)-based decision support tool (DST) to assist injury surveillance departments in the validation, coding, and use of their data, comparing outcomes in coding time, and accuracy pre- and postimplementations.

Methods: Manually coded injury surveillance data have been used to develop, train, and iteratively refine a ML-based classifier to enable semiautomated coding of injury narrative data. This paper describes a trial implementation of the ML-based DST in the Queensland Injury Surveillance Unit (QISU) workflow using a major pediatric hospital's ED data comparing outcomes in coding time and pre- and postimplementation accuracies.

Results: The study found a 10% reduction in manual coding time after the DST was introduced. The Kappa statistics analysis in both DST-assisted and -unassisted data shows increase in accuracy across three data fields, that is, injury intent (85.4% unassisted vs. 94.5% assisted), external cause (88.8% unassisted vs. 91.8% assisted), and injury factor (89.3% unassisted vs. 92.9% assisted). The classifier was also used to produce a timely report monitoring injury patterns during the novel coronavirus disease 2019 (COVID-19) pandemic. Hence, it has the potential for near real-time surveillance of emerging hazards to inform public health responses.

Conclusion: The integration of the DST into the injury surveillance workflow shows benefits as it facilitates timely reporting and acts as a DST in the manual coding process.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19* / epidemiology
  • Child
  • Emergency Service, Hospital*
  • Hospital Information Systems* / organization & administration
  • Humans
  • Injury Severity Score
  • Machine Learning
  • Pandemics
  • Workflow
  • Wounds and Injuries* / classification

Grants and funding

Funding This work was supported by an Australian Research Council Discovery Grant (grant no.: DP170103136).