Kaiser to use $54M award to sift EHRs for clues to healthcare’s greatest challenges.

By Mary Mosquera

The National Institutes of Health awarded Kaiser Permanente $54 million in grants for projects that will tap clinical information from the provider’s mammoth electronic health record database to study links between genes and conditions such as heart disease, obesity, diabetes and aging.

The NIH funding originated with the HITECH Act, which allocated $400 million to NIH to support comparative effectiveness research.

The largest of the 22 awards provides $24.8 million to study the influence of genes and the environment on health, disease and longevity over time and across diverse groups of people. The grants will fund genotyping of 100,000 Kaiser members in Northern California. The University of California in San Francisco is also a partner in the research.

The analysis will link genetic information with historical clinical data taken from health surveys and Kaiser’s electronic health record database, according to Raymond Baxter, senior vice president for Kaiser. Researchers will add to the study environmental information, such as air and water quality and proximity to parks and healthy foods.

Dr. Richard Hodes, NIA director of the National Institute on Aging, said genetic information generated by the project may help researchers discover genetic factors that explain differences between people in response to medications.

“This would help doctors provide patients with the best medicines for them individually,” he said.

The grant package included $7.2 million in funding to develop a cardiovascular surveillance system for a collaborative of 14 different health plans across the U.S.

A $3.3 million grant will create a National Research Database to organize Kaiser Permanente’s electronic health records.

A $1,005,372 portion of the funding will be used to integrate the Kaiser Permanente electronic medical record to measure rehabilitation outcomes for stroke patients. Another $99,971 was allocated to study the use of natural language processing to extract data from the electronic medical record.

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