Jesani Catchpoole1,2,3, Gaurav Nanda4, Kirsten Vallmuur2,3, Goshad Nand1, Mark Lehto5. 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. 4. Purdue University, School of Engineering Technology, West Lafayette, Indiana, United States. 5. Purdue University, School of Industrial Engineering, West Lafayette, Indiana, United States.
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. Thieme. All rights reserved.
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. Thieme. All rights reserved.
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