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Science Translational Medicine: Improving Genetic Studies with Electronic Records

Electronic medical records could speed up personalized medicine by improving genetic studies, according to a new report in the 20 April issue of the journal Science Translational Medicine.

The findings suggest that by using electronic medical records, health providers can help research teams collect comprehensive, accurate data that reveal the relationship between disease and a person’s genetic makeup.

Increased use of electronic medical records can dramatically boost the availability of participant data for genetic studies, yielding more powerful results without having to go through the time and expense of enrolling patients in clinical trials for different diseases.

“The hard part of doing genetic studies has been identifying enough people to get meaningful results,” said lead investigator Abel Kho, 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.”

Kho and colleagues used electronic medical record or EMR data to identify diseases of interest in genetic studies. The researchers began by mining EMR data (such as lab tests and medications) for five medical conditions: dementia, cataracts, peripheral artery disease, diabetes, and cardiac conduction.

The team generated algorithms to find patients with these conditions at five research clinics and hospital groups specifically set up to use EMRs and to collect samples for genetic studies. When patients receive care at these institutions, they agree to the use of their records for research as part of their consent to treatment. In addition, the authors obtained individual patient permission for this study.

The algorithms, based on the EMR data, act like a set of rules used to detect patients with a particular disease. Kho and colleagues correctly identified dementia in 73% of patients at one institution using the algorithms. At two other institutions, the algorithms correctly identified disease in 71% and 90% of patients.

The researchers checked the accuracy of the algorithms against traditional methods of diagnosis like doctors’ notes and medical charts, and found diagnoses from EMR data correctly matched doctors’ diagnoses.

The authors note that a few improvements to EMR data, like consistently including factors like smoking status and family history, might make them more useful.


Read the report, “Electronic Medical Records for Genetic Research: Results of the eMERGE Consortium,” by A.N. Kho et al.

Visit Science Translational Medicine.


Nadia Ramlagan

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