TY  - JOUR
PY  - 2022//
TI  - Application of a machine learning-based decision support tool to improve an injury surveillance system workflow
JO  - Applied clinical informatics
A1  - Catchpoole, Jesani
A1  - Nanda, Gaurav
A1  - Vallmuur, Kirsten
A1  - Nand, Goshad
A1  - Lehto, Mark
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - Background Emergency department (ED)-based injury surveillance systems across many countries face resourcing challenges related to manual validation and coding of data. <br><br>OBJECTIVE This paper 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, comparing outcomes in coding time and accuracy pre- and post-implementation. <br><br>METHODS Manually coded injury surveillance 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 Queensland Injury Surveillance Unit (QISU) workflow using a major pediatric hospital's emergency department data comparing outcomes in coding time and accuracy pre- and post-implementation. <br><br>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 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. <br><br>CONCLUSIONS 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1869-0327
UR  - http://dx.doi.org/10.1055/a-1863-7176
ID  - ref1
ER  -