Appl Clin Inform 2022; 13(03): 700-710
DOI: 10.1055/a-1863-7176
Research Article

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

Jesani Catchpoole
1   Queensland Injury Surveillance Unit, Royal Brisbane and Women's Hospital, Metro North Hospital and Health Service, Queensland, Australia
2   Jamieson Trauma Institute, Royal Brisbane and Women's Hospital, Metro North Hospital and Health Service, Queensland, Australia
3   Australian Centre for Health Services Innovation (AusHSI), School of Public Health and Social Work
,
Gaurav Nanda
4   Purdue University, School of Engineering Technology, West Lafayette, Indiana, United States
,
Kirsten Vallmuur
2   Jamieson Trauma Institute, Royal Brisbane and Women's Hospital, Metro North Hospital and Health Service, Queensland, Australia
3   Australian Centre for Health Services Innovation (AusHSI), School of Public Health and Social Work
,
Goshad Nand
1   Queensland Injury Surveillance Unit, Royal Brisbane and Women's Hospital, Metro North Hospital and Health Service, Queensland, Australia
,
Mark Lehto
5   Purdue University, School of Industrial Engineering, West Lafayette, Indiana, United States
› Author Affiliations
Funding This work was supported by an Australian Research Council Discovery Grant (grant no.: DP170103136).

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.

Protection of Human and Animal Subjects

Human and/or animal subjects were not included in the project. In addition, the study analyzed nonidentifiable data; therefore, consent from patients was not required.




Publication History

Received: 31 January 2022

Accepted: 26 May 2022

Accepted Manuscript online:
29 May 2022

Article published online:
13 July 2022

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