Predicting Harm Scores from Patient Safety Event Reports
The identification of the severity of patient safety events promotes prioritized safety analysis and intervention.The Harm Scale developed by the Agency for Healthcare Research and Quality is widely used in the US hospitals.However,recent studies have indicated a moderate to poor inter-rater reliability of the Harm Scale across a number of US hospitals.Although the reasons are multi-folded,biased human judgments are recognized as a prominent factor.We proposed that key information to identify and refine the severity of harm is contained in the narrative data in patient safety reports.Using automated text classification to categorize harm scores is intended to provide reduced subjective judgments and much improved efficiency.We evaluated different types of classification algorithms using a corpus of patient safety reports from a US health care system.The results demonstrate the effectiveness and efficiency of the proposed methods.Accordingly,human biases on the application of harm scores are expected to be largely reduced.Our finding holds promise to serve as a semi-supervised tool during the process of manually reviewing and analyzing patient safety events.
Patient Safety Patient Harm Data Mining
Chen Liang Yang Gong
Louisiana Tech University,Ruston,Louisiana,USA School of Biomedical Informatics,University of Texas Health Science Center,Houston,Texas,USA
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
1075-1079
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）