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As the adoption of electronic health records (EHRs) has made it easier to access and aggregate clinical data, there has been growing interest in conducting research with data collected during the course of clinical care.1 2 The Natonal Institutes of Health has called for increasing the reuse of electronic records for research, and the clinical research community has been actively seeking methods to enable secondary use of clinical data.3 EHRs surpass many existing registries and data repositories in volume, and the reuse of these data may diminish the costs and inefficiencies associated with clinical research. Like other forms of retrospective research, studies that make use of EHR data do not require patient recruitment or data collection, both of which are expensive and time-consuming processes. The data from EHRs also offer a window into the medical care, status, and outcomes of a diverse population that is representative of actual patients. The secondary use of data collected in EHRs is a promising step towards decreasing research costs, increasing patient-centered research, and speeding the rate of new medical discoveries. Despite these benefits, reuse of EHR data has been limited by a number of factors, including concerns about the quality of the data and their suitability for research. It is generally accepted that, as a result of differences in priorities between clinical and research settings, clinical data are not recorded with the same care as research data.4 Moreover, Burnum5 stated that the introduction of health information technology like EHRs has led not to improvements in the quality of the data being recorded, but rather to the recording of a greater quantity of bad data. Due to such concerns about data quality, van der Lei6 warned specifically against the reuse of clinical data for research and proposed what he called the first law of informatics: ‘[d]ata shall be used only for the purpose for which they were collected’. Although such concerns about data quality have existed since EHRs were first introduced, there remains no consensus as to the quality of electronic clinical data or even agreement as to what ‘data quality’ actually means in the context of EHRs. One of the most broadly adopted conceptualizations of quality comes from Juran,7 who said that quality is defined through ‘fitness for use’. In the context of data quality, this means that data are of sufficient quality when they serve the needs of a given user pursuing specific goals. Past study of EHR data quality has revealed highly variable results. Hogan and Wagner,8 in their 1997 literature review, found that the correctness of data ranged between 44% and 100%, and completeness between 1.1% and 100%, depending on the clinical concepts being studied. Similarly, Thiru et al, 9 in calculating the sensitivity of different types of EHR data in the literature, found values ranging between 0.26 and 1.00. In a 2010 review, Chan et al10 looked at the quality of the same clinical concepts across multiple institutions, and still found a great deal of variability. The completeness of blood pressure recordings, for example, fell anywhere between 0.1% and 51%. Due to differences in measurement, recording, information systems, and clinical focus, the quality of EHR data is highly variable. Therefore, it is generally inadvisable to make assumptions about one EHR-derived dataset based on another. We need systematic methods that will allow us to assess the quality of an EHR-derived dataset for a given research task. Our review primarily differs from those highlighted above in its focus. The previous reviews looked at data quality findings, while ours instead focuses on the methods that have been used to assess data quality. In fact, the earlier reviews were explicitly limited to studies that relied on the use of a reference standard, while we instead explore a range of data quality assessment methods. The contributions of this literature review are an empirically based conceptual model of the dimensions of EHR data quality studied by clinical researchers and a summary and critique of the methods that have been used to assess EHR data quality, specifically within the context of reusing clinical data for research. Our goal is to develop a systematic understanding of the approaches that may be used to determine the suitability of EHR data for a specific research goal.