Improving data quality by implementing an electronic medical record seems to depend on the context

Abstract Background The European healthcare system is reliant on its digital transformation to deal with challenges like rising expenditures or workforce shortage. The digital transformation is inevitably accompanied by the implementation of electronic medical records (EMR) and the ongoing adaptation of existing ones. Systematic reviews indicate that this process can have an impact on medical records data quality (DQ) [1]. At micro level, DQ is essential to ensure high quality of care. At macro level, sufficient DQ is a prerequisite for big data analyzability. In this field, completeness is a commonly analyzed dimension of DQ and empirical results indicate that completeness can improve but also deteriorate as a result of the described implementation or adoption of EMRs [2]. The aim of this work was to investigate the implementation of EMRs in comparable settings and to observe and discuss possible differences in the change in DQ. Methods Data was collected on three surgical clinics of a German academic teaching hospital before and after the implementation of an EMR. Paper-based and electronic medical records were compared. Analysis focused on ten items that were commonly documented in both record types (e.g. pain). T-tests and χ²-tests were used to compare average completeness per record type and percentage of completeness per item. Results A total of N = 659 records was analyzed. Overall, results show a significant improvement in completeness from an average of 6.0/10 items in the paper-based record type to 7.2/10 in the EMR (p<.05). At clinic level, improvement rates vary from 0.9 to 1.4. At the level of the specific items, significant deteriorations are visible in certain clinics. Conclusions Results suggest that DQs variability is context-dependent (e.g. on the clinic’s turnover rate or its patient’s length of stay). Due to the unavoidable digital transformation, a detailed context and needs analysis involving all stakeholders should be carried out before any changes are made. Key messages • The application of advanced analytics such as big data or AI training is reliant on the availability of high-quality datasets. • Electronic medical records have been demonstrated to enhance data quality, but it remains uncertain how and why improvements appear to be context-dependent.