Accuracy and generalizability of using automated methods for identifying adverse events from electronic health record data: a validation study protocol

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• 作者：Christian M. Rochefort ; David L. Buckeridge…
• 关键词：Adverse events ; Electronic health record ; Acute care hospital ; Automated detection ; Natural language processing ; Patient safety ; Data warehouse
• 刊名：BMC Health Services Research
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：17
• 期：1
• 全文大小：561KB
• 刊物主题：Public Health; Health Administration; Health Informatics; Nursing Research;
• 出版者：BioMed Central
• ISSN：1472-6963
• 卷排序：17

文摘

BackgroundAdverse events (AEs) in acute care hospitals are frequent and associated with significant morbidity, mortality, and costs. Measuring AEs is necessary for quality improvement and benchmarking purposes, but current detection methods lack in accuracy, efficiency, and generalizability. The growing availability of electronic health records (EHR) and the development of natural language processing techniques for encoding narrative data offer an opportunity to develop potentially better methods. The purpose of this study is to determine the accuracy and generalizability of using automated methods for detecting three high-incidence and high-impact AEs from EHR data: a) hospital-acquired pneumonia, b) ventilator-associated event and, c) central line-associated bloodstream infection.