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Published in the April 20 issue of Science Translational Medicine, the study said researchers were able to cull patient information in EMRs from routine doctors' visits at five national sites that all used different brands of medical record software. The information allowed researchers to accurately identify patients with type 2 diabetes, dementia, peripheral arterial disease, cataracts, and cardiac conduction.
A study of this kind often relies on recruiting thousands of patients to collect health data for genetic clues to disease, which is expensive and time consuming, researchers said.
"The hard part of doing genetic studies has been identifying enough people to get meaningful results," said lead investigator Abel Kho, M.D., an assistant professor of medicine at Northwestern University Feinberg School of Medicine and a physician at Northwestern Memorial Hospital. "Now we've shown you can do it using data that's already been collected in electronic medical records and can rapidly generate large groups of patients."
To identify the diseases, Kho and colleagues searched the EMRs using a series of criteria such as medications, diagnoses, and laboratory tests. They then tested their results against the gold standard--review by physicians. The physicians confirmed the results, Kho said. The EMRs allowed researchers to identify patients' diseases with 73% to 98% accuracy.
According to the study's results: "Most EMRs captured key information (diagnoses, medications, laboratory tests) used to define phenotypes in a structured format. We identified natural language processing as an important tool to improve case identification rates. Efforts and incentives to increase the implementation of interoperable EMRs will markedly improve the availability of clinical data for genomics research."
The researchers also were able to reproduce previous genetic findings from prospective studies using the EMRs. The five institutions that participated in the study collected genetic samples for research. Patients agreed to the use of their records for studies.
"With permission from patients, you could search electronic health records at not just five sites but 25 or 100 different sites and identify 10,000 or 100,000 patients with diabetes, for example," Kho said.
The larger the group of patients for genetic studies, the better the ability to detect rare effects of the genes and the more detailed genetic sequences that cause a person to develop a disease.
The study also showed across-the-board weaknesses in institutions' EMRs. The institutions didn't do a good job of capturing race and ethnicity, smoking status, and family history, which are important areas of study, Kho said. "It shows we need to focus our efforts to use electronic medical records more meaningfully," he added.
The study, which was supported by the National Human Genome Research Institute with additional funding from the National Institute of General Medical Sciences, comes at a time when health delivery organizations are accelerating the adoption of EMRs/EHRs under the Medicare and Medicaid EHR Incentive Programs, which provides financial incentives for the adoption and meaningful use of EMRs.