{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:26:33Z","timestamp":1776083193489,"version":"3.50.1"},"reference-count":26,"publisher":"Georg Thieme Verlag KG","issue":"03","funder":[{"name":"Australian Research Council Discovery Grant","award":["DP170103136"],"award-info":[{"award-number":["DP170103136"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2022,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>\n          Background\u2003Emergency department (ED)-based injury surveillance systems across many countries face resourcing challenges related to manual validation and coding of data.<\/jats:p><jats:p>\n          Objective\u2003This 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.<\/jats:p><jats:p>\n          Methods\u2003Manually 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.<\/jats:p><jats:p>\n          Results\u2003The 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.<\/jats:p><jats:p>\n          Conclusion\u2003The 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.<\/jats:p>","DOI":"10.1055\/a-1863-7176","type":"journal-article","created":{"date-parts":[[2022,5,29]],"date-time":"2022-05-29T22:37:45Z","timestamp":1653863865000},"page":"700-710","source":"Crossref","is-referenced-by-count":6,"title":["Application of a Machine Learning\u2013Based Decision Support Tool to Improve an Injury Surveillance System Workflow"],"prefix":"10.1055","volume":"13","author":[{"given":"Jesani","family":"Catchpoole","sequence":"additional","affiliation":[{"name":"Queensland Injury Surveillance Unit, Royal Brisbane and Women's Hospital, Metro North Hospital and Health Service, Queensland, Australia"},{"name":"Jamieson Trauma Institute, Royal 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