Application of a Machine Learning-Based Decision Support Tool to Improve an Injury Surveillance System Workflow.
Appl Clin Inform
; 13(3): 700-710, 2022 05.
Article
in English
| MEDLINE | ID: covidwho-1873581
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Wounds and Injuries
/
Hospital Information Systems
/
Emergency Service, Hospital
/
COVID-19
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Limits:
Child
/
Humans
Language:
English
Journal:
Appl Clin Inform
Year:
2022
Document Type:
Article
Affiliation country:
A-1863-7176
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