Improving the quality of EHR recording in primary care: a data quality feedback tool.

OBJECTIVE: Electronic health record (EHR) data are used to exchange information among health care providers. For this purpose, the quality of the data is essential. We developed a data quality feedback tool that evaluates differences in EHR data quality among practices and software packages as part of a larger intervention. METHODS: The tool was applied in 92 practices in the Netherlands using different software packages. Practices received data quality feedback in 2010 and 2012. RESULTS: We observed large differences in the quality of recording. For example, the percentage of episodes of care that had a meaningful diagnostic code ranged from 30% to 100%. Differences were highly related to the software package. A year after the first measurement, the quality of recording had improved significantly and differences decreased, with 67% of the physicians indicating that they had actively changed their recording habits based on the results of the first measurement. About 80% found the feedback helpful in pinpointing recording problems. One of the software vendors made changes in functionality as a result of the feedback. CONCLUSIONS: Our EHR data quality feedback tool is capable of highlighting differences among practices and software packages. As such, it also stimulates improvements. As substantial variability in recording is related to the software package, our study strengthens the evidence that data quality can be improved substantially by standardizing the functionalities of EHR software packages.

Detailed clinical modelling approach to data extraction from heterogeneous data sources for clinical research.

The reuse of routinely collected clinical data for clinical research is being explored as part of the drive to reduce duplicate data entry and to start making full use of the big data potential in the healthcare domain. Clinical researchers often need to extract data from patient registries and other patient record datasets for data analysis as part of clinical studies. In the TRANSFoRm project, researchers define their study requirements via a Query Formulation Workbench. We use a standardised approach to data extraction to retrieve relevant information from heterogeneous data sources, using semantic interoperability enabled via detailed clinical modelling. This approach is used for data extraction from data sources for analysis and for pre-population of electronic Case Report Forms from electronic health records in primary care clinical systems.