Development and validation of method for defining conditions using Chinese electronic medical record. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Xu, Yuan; Li, Ning; Lu, Mingshan; Myers, Robert; Dixon, Elijah; Walker, Robin; Sun, Libo; Zhao, Xiaofei; Quan, Hude
• Is Part Of: BMC medical informatics and decision making. Volume 16:Issue 1(2016)
• Page Start: 1
• Page End: 8
• Publication Date: 2016-12
• Subjects: Extraction method -- Validation -- Case definition -- Electronic medical record -- Liver disease; Medical informatics -- Periodicals; Clinical medicine -- Decision making -- Periodicals; 610.285
• DOI: 10.1186/s12911-016-0348-6
• Languages: English
• ISSNs: 1472-6947

Abstract

Abstract Background The adoption of the electronic medical record (EMR) is rapidly growing in China. Constantly evolving, Chinese EMRs contain vast amounts of clinical and financial data, providing tremendous potential for research and policy use; however, they are only partially standardized and contain free text or unstructured data. To utilize the information contained in Chinese EMRs, the development of data extraction methodology is urgently needed. The purpose of this study is to develop and validate methods to extract clinical information from the Chinese EMR for research use. Methods Using 2010 to 2014 EMR data from YouAn Hospital, a large teaching hospital affiliated with Capital Medical University in Beijing, China, we developed extraction methods including 40 EMR definitions for defining 6 liver disease, 5 disease severity conditions, and 29 comorbidities and treatments. We conducted a chart review of 450 randomly selected EMRs. Using physician chart review results as a reference, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to validate each EMR definition. Results The sensitivity of the 6 EMR definitions for liver diseases ranged from 78.9 to 100.0 %, and PPV ranged from 82.1 to 100.0 %. The sensitivity of the 5 definitions on disease severity conditions ranged from 91.0 to 100.0 %, and PPV ranged from 79.2 to 100.0 %. Among the 29 EMR definitions for comorbidities and treatments, 23 had sensitivity over 90.0 % and 25 had PPV over 80.0 %. The specificity and NPV for all 40 EMR definitions were over 90.0 %. Conclusion The extraction method developed is a valid way of extracting information on liver diseases, comorbidities and related treatments from YouAn hospital EMRs. Our method should be modified for application to other Chinese EMR systems, following our framework for extracting conditions.