Study: EMRs could aid in disease research

With the U.S. rapidly adopting electronic health records, there's a big opportunity to use data from the records to aid in disease research. Researchers at Northwestern University showed that tapping electronic medical records provides a way to rapidly and cheaply identify patients with specific diseases for genetic studies.

The group mined five different EMR systems for such data as medications, diagnoses and lab tests to find patients with certain conditions--type 2 diabetes, dementia, peripheral arterial disease, cataracts and cardiac conduction. The electronic data enabled the researchers to determine a patient's disease with 73 percent to 98 percent accuracy. The group, which is part of the NIH-supported Electronic Medical Records and Genomics Network (eMERGE), published its findings on April 20 in the journal Science Translational Medicine.

Electronic health records could become a treasure trove of data for genetic research on diseases. With genomic sequencing becoming faster and cheaper, there's potential to someday make populating EHRs with rich genetic details routine in healthcare. These data would give a huge boost to the research community, which is studying raw data from human genomes and analyzing specific genes to understand their roles in disease progression. Today, however, EHRs don't generally have anything close to genomic information on patients, and the records themselves lack interoperability, making it difficult to conduct nationwide searches of patient data.

Still, the group at Northwestern shows that searching electronic records for specific patient data is already becoming an option for researchers.

"The hard part of doing genetic studies has been identifying enough people to get meaningful results," Dr. Abel Kho, a lead investigator of the study and an assistant professor of medicine at Northwestern University Feinberg School of Medicine, said in a statement. "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."

- here's the release from Northwestern
- read the abstract from Science Translational Medicine