Assessing the quality of electronic medical records in academic hospitals: A multi-center study in Iran

Abstract

ObjectiveThe present study aimed to assess the quality of electronic medical records (EMR) retrieved from hospital information systems (HIS) of three educational hospitals in Mashhad, Iran.MethodsIn this multi-center, cross-sectional study, inpatient electronic records collected from three academic hospitals were categorized into five data groups, namely demographics (D); care handler (CH), indicating the doers of the medical actions; diagnosis and treatment (DT); administrative and financial (AF); and laboratory and Para clinic (LP). Next, we asked 25 physicians from the three academic hospitals to determine data elements of medical research and education value (called research and educational data) in every group. Flowingly, the quality of the five data groups (completeness * accuracy) was reported for entire sampled data and those specified as research and educational data, based on the exact concordance between electronic medical records and corresponding paper records. HISRA, standing for HIS recording ability, was also assessed compared to data elements of standard paper forms.ResultsFor entire data, HISRA was 58.5%. In all hospitals, the highest data quality (more than 90%) belongs to D and AF data groups, and the lowest quality goes to CH and DT groups (less than 50%, and 60%, respectively). For research and educational data, HISRA was 47%, and the quality of D and AF data groups were the highest (nearly 100%), while CH and DT stood around 50% and 60% in order. The quality of the LP data group was almost 85% in all hospitals but hospital C (well over 30%). Total data quality for the hospitals was almost less than 70%.ConclusionsThe low quality of electronic medical records was mostly a result of incompleteness, while the accuracy was relatively good. Results showed that the HIS application development mainly focused on administrative and financial aspects rather than academic and clinical goals.