Publication Details

Qian, S., Munyisia, E., Yu, P., Hailey, D. M. & Reid, D. (2017). Precision of EMR Data: The Case For a Drug and Alcohol Service. 16th World Congress on Medical and Health Informatics (MedInfo2017)


Error-laden data can negatively affect clinical and operational decision making, research findings and funding allocation. This study examined the number and types of data errors in an electronic medical record (EMR) system in a Drug and Alcohol service. Specifically, errors in service data were examined. 9,379 errors were identified from ten error reports generated between March 2015 and May 2016, three months after the implementation of the EMR system. These errors were grouped into four types: mismatched data fields (60.5%), duplicate medical record error (3.2%), date/time error (8.8%) and blank field error (27.4%). The errors can be prevented by adding functions such as alert messages in the EMR system. How and why the errors occur need to be investigated in future studies.