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

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A machine learning (ML) decision support tool (DST) reduced manual coding time by 10% in injury surveillance. This ML tool also improved data accuracy, aiding timely public health responses.

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Area of Science:

  • Public Health
  • Health Informatics
  • Machine Learning

Background:

  • Emergency department (ED) injury surveillance systems face resource challenges due to manual data validation and coding.
  • Manual data processing is time-consuming and can impact the timeliness and accuracy of injury surveillance reports.

Purpose of the Study:

  • To evaluate a machine learning (ML)-based decision support tool (DST) for assisting injury surveillance departments.
  • To compare the efficiency and accuracy of data coding and validation using the ML-DST versus manual methods.

Main Methods:

  • A ML-based classifier was developed and refined using manually coded injury data.
  • The ML-DST was implemented in the Queensland Injury Surveillance Unit (QISU) workflow for processing pediatric ED data.
  • Outcomes, including coding time and accuracy, were compared pre- and postimplementation.

Main Results:

  • Manual coding time decreased by 10% after ML-DST implementation.
  • Data accuracy significantly improved across key fields: injury intent (85.4% to 94.5%), external cause (88.8% to 91.8%), and injury factor (89.3% to 92.9%).
  • The ML-DST enabled timely injury pattern monitoring during the COVID-19 pandemic.

Conclusions:

  • Integrating the ML-DST into injury surveillance workflows enhances efficiency and accuracy.
  • The tool facilitates timely reporting and supports manual coding processes.
  • It holds potential for near real-time surveillance of emerging public health hazards.