Health systems will need new ways to manage all the data involved in personalized medicine, Litt says. “They already have storage issues today and genomic data is an order of magnitude greater,” he says. “Hospital CIOs have to ingest it, store it, and then present it so clinicians can make use of it.” They also need appropriate clinical decision support at the point of care, he adds. “Most current EHRs have no way to present this data, so hospitals have to make a choice whether to build that in or link to an external source. I think it’s more likely they will do the latter. This is not an area of expertise for the EHR vendors. I think for all these reasons, the topic scares the heck out of most CIOs.”
If progress is being made in terms of presenting clinically relevant genetic information at the point of care, many gaps remain in terms of standards, integration, decision support and work flow. Speaking at the AMIA Symposium in Washington, D.C., last fall, Kevin Hughes, M.D., co-director of the Avon Comprehensive Breast Evaluation Center at Massachusetts General Hospital in Boston, described some of these gaps in greater detail. In the ideal world, he said, a clinician would pull structured data out of the EHR to support a genetic consultation. That would include access to decision support and risk algorithms to determine what might be needed for an individual patient. The clinician could send genetic test requests as structured data, including a structured family history, and get back a structured result, which could help feed a rapid learning health system.
“We don’t have any of this,” Hughes told the AMIA audience. Genetic lab tests are being sent back and forth on paper, he added. All that information is being stored as free text in the EHR, where it becomes unmanageable. He noted that although his EHR at Mass General may show in its notes section that a patient tested positive for the BRCA1 mutation, the clinical decision support section says the patient has no increased risk of breast cancer. “In the absence of structured data, the decision support has no idea that this patient is a mutation carrier,” he said. “Not only are EHRs not interoperable,” he added, “they can’t even talk to themselves.”
“We need clinical decision support, we need knowledge bases, and we need a rapid learning health system, and unless the data is standardized we will not get there,” Hughes stressed. “Health IT solutions must collect, receive and transmit standards-based family history and genetic data.” Guidelines and knowledge bases must be machine-readable and deployed as web services. “Closed, proprietary systems that are not interoperable are holding us back.”
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