While the data stored in electronic health records is primarily clinical in nature, intended to help physicians diagnose patients and record treatments, researchers interested in examining rare diseases and other genetic health factors are increasingly turning to those EHRs as a source of data. Instead of relying on questionnaires or interviews by staff to collect information, researchers can now use a DNA sample linked to a patient’s electronic record as a rich resource to investigate a broad range of phenotypes and their associated expressions.
Joshua C. Denny, Assistant Professor of Biomedical Informatics and Medicine at Vanderbilt University, recently published an article outlining the challenges and benefits of using EHR data for genetic research. As EHR systems become more robust and more widely used, healthcare organizations are participating in DNA “biobanking”, an effort spearheaded by the National Human Genome Research Institute (NHGRI) and Electronic Medical Records and Genomics (eMERGE) network. Kaiser Permanente is one of the largest institutions involved in linking patient DNA to EHRs, collecting saliva samples from more than 100,000 of its members in order to create a genetic map of risk factors for heart disease and high cholesterol, among other health concerns.
Denny notes that using EHR data in this manner is cost-effective for researchers, and provides a much larger sample size much more quickly than traditional data collection methods. Information can be reused in various forms, as well, and contains a wide enough variety of data to be useful for numerous topics of study. Since much of the clinical information is collected in a structured format, it’s easy to search and extract. Using natural language processing programs to scan narrative data for pertinent keywords has greatly expanded the usefulness of EHRs for research purposes.
However, many challenges remain before researchers can fully rely on EHRs. Since genetic information is so closely linked to ancestry and ethnic origins, accurate collection of this information from the patient is vital for tracking population health risks along ethnic or racial lines, an area where providers are currently falling short. Another major challenge, according to Denny, is “derivation of accurate collections of cases and controls for a given disease of interest, usually achieved through creation and validation of phenotype selection logics. These algorithms take significant time and effort to develop and often require adjustment and a skilled team to deploy at a secondary site.” Healthcare providers who are busy trying to meet meaningful use criteria or other demands on their technology and resources may not be interested in adding another step to their implementation process.
But Denny sees these as temporary pauses along the march towards leveraging EHR data for research purposes. “As genomics move beyond discovery into clinical practice,” he asserts, “the future of personalized medicine is one in which our genetic information could be simply a click of the mouse away. In this future, DNA-enabled EHR systems will assist in more accurate prescribing, risk stratification, and diagnosis. Genomic discovery in EHR systems provides a real-world test bed to validate and discover clinically meaningful genetic effects.”
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