Application of a machine learning-based decision support tool in injury surveillance system

BackgroundEmergency department (ED)-based injury surveillance systems across many countries face resourcing challenges related to the validation and coding of data, which largely rely on manual coding. This presentation describes the evaluation of a machine learning-based Decision Support Tool (DST) to assist injury surveillance departments in the validation, coding and use of their data.MethodsManually-coded Queensland Injury Surveillance Unit (QISU) data has been used to develop, train and iteratively refine a machine learning-based classifier to enable semi-automated coding of injury narrative data. This paper describes a trial implementation of the machine learning-based DST in the QISU workflow using a major pediatric hospital’s ED data comparing outcomes in coding time and accuracy pre and post-implementation.ResultsIn total, 3174 injury records in February and March 2020 were analyzed. Statistical analysis shows a 10% reduction in manual coding time after introducing the DST. Concordance study comparing the kappa statistics from both DST-assisted and unassisted data shows increases in accuracy across three data fields;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 COVID-19 pandemic. Hence, it has the potential for near real-time surveillance of emerging hazards to inform public health responses.ConclusionThe integration of the DST into the QISU workflow shows benefits to the unit as it facilitates timely reporting and acts as a DST in the manual coding process.

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

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.