Queensland Injury Surveillance Unit, Royal Brisbane and Women's Hospital, Metro North Hospital and Health Service, Queensland, Australia.
Jamieson Trauma Institute, Royal Brisbane and Women's Hospital, Metro North Hospital and Health Service, Queensland, Australia.
Appl Clin Inform. 2022 May;13(3):700-710. doi: 10.1055/a-1863-7176. Epub 2022 May 29.
Emergency department (ED)-based injury surveillance systems across many countries face resourcing challenges related to manual validation and coding of data.
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.
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.
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.
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.
许多国家的急诊部（ED）基于伤害监测系统面临着与数据手动验证和编码相关的资源挑战。
本研究描述了一种机器学习（ML）基于决策支持工具（DST）的评估，以协助伤害监测部门验证、编码和使用其数据，比较编码时间和准确性的前后变化。
手动编码的伤害监测数据已被用于开发、培训和迭代改进基于 ML 的分类器，以实现伤害叙述数据的半自动编码。本文描述了在昆士兰伤害监测单位（QISU）工作流程中试用 ML 基于 DST 的情况，使用一家主要儿科医院的 ED 数据比较编码时间和实施前后的准确性。
在引入 DST 后，手动编码时间减少了 10%。在 DST 辅助和非辅助数据中，Kappa 统计分析显示，三个数据字段的准确性都有所提高，即伤害意图（非辅助 85.4%，辅助 94.5%）、外部原因（非辅助 88.8%，辅助 91.8%）和伤害因素（非辅助 89.3%，辅助 92.9%）。该分类器还用于生成一份及时的报告，监测在 2019 年新型冠状病毒病（COVID-19）大流行期间的伤害模式。因此，它有可能对新出现的危害进行近实时监测，为公共卫生应对措施提供信息。
将 DST 集成到伤害监测工作流程中显示出益处，因为它促进了及时报告，并在手动编码过程中充当 DST。
