With the increasing adoption of health information technology and the growth in the resulting electronic repositories of clinical data, the secondary use of electronic health record (EHR) data has become one of the most promising approaches to enabling and speeding medical research. Unfortunately, EHR data are known to suffer from significant data quality problems exceeding those associated with data collected in traditional research settings.
Although awareness of EHR data quality problems is growing, methodological approaches to measuring data quality remain largely ad hoc and overly-reliant upon the availability of a "gold-standard," which may not be available or reliable. Clinical researchers must generally use their own judgment in addressing this complicated problem. The lack of appropriate or reliable EHR data quality assessment methodology limits the efficiency and validity of research performed with EHR data. A further complication is the task-dependent nature of data quality. What constitutes sufficient data quality for one study is frequently inappropriate for another.
Systematic methods for the performance and reporting of data quality assessment should be a central tenant of EHR data reuse. All stakeholders—including researchers, clinicians, policy-makers, and patients—should be able to assess and understand the limitations of the data underpinning medical research.
Through a combination of systematic review of the literature and interviews with EHR data consumers, we have developed a simple framework and associated recommendations to guide data quality assessment. This is not a black box process; you will be the one to make the final determination of whether or not your dataset is of sufficient quality. 3x3 DQA will lead data consumers through a logical and coherent exploration of the quality of a dataset, making it easier to decide whether or not the dataset is of sufficient quality.
The Scope Identification Tool is an interactive question-and-answer tool that adapts to the user's responses in order to determine the methods of data quality assessment that are most appropriate for an intended study and dataset. Once all the questions have been completed the user will be presented with the relevant portions of the 3x3 DQA framework and the associated recommendations for assessment and reporting.
If you already have at least basic information about your dataset and have started planning your study, please complete the Scope Identification Tool prior to performing the assessments outlined in the paper or electronic versions of 3x3 DQA.