Improving the timeliness and accuracy of injury severity data in road traffic accidents in an emerging economy setting

Carlos Lam, Chang-I Chen, Chia-Chang Chuang, Chia-Chieh Wu, Shih-Hsiang Yu, Kai-Kuo Chang, Wen-Ta Chiu

Research output: Contribution to journalArticle

Abstract

Road traffic injuries (RTIs) are among the leading causes of injury and fatality worldwide. RTI casualties are continually increasing in Taiwan; however, because of a lack of an advanced method for classifying RTI severity data, as well as the fragmentation of data sources, road traffic safety and health agencies encounter difficulties in analyzing RTIs and their burden on the healthcare system and national resources. These difficulties lead to blind spots during policy-making for RTI prevention and control. After compiling classifications applied in various countries, we summarized data sources for RTI severity in Taiwan, through which we identified data fragmentation. Accordingly, we proposed a practical classification for RTI severity, as well as a feasible model for collecting and integrating these data nationwide. This model can provide timely relevant data recorded by medical professionals and is valuable to healthcare providers. The proposed model’s pros and cons are also compared to those of other current models.
Original languageTraditional Chinese
Pages (from-to)mzy115-mzy115
JournalInternational Journal for Quality in Health Care
Volume31
Issue number2
DOIs
Publication statusPublished - 2019

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