NRC Seeks To Marry Biomedical Research, Patient EHR DataNRC Seeks To Marry Biomedical Research, Patient EHR Data

Health IT Boosts Patient Care, Safety

Tapping into the wealth of data stored in electronic health records (EHRs) could advance medical research and improve clinical outcomes for patients with cancer, diabetes, and heart disease, according to a new report from the National Research Council (NRC). With that possibility in mind, the NRC is calling for a new data network, one that integrates the molecular makeup of diseases with clinical data from patients' digitized medical records.

The study, Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease, noted that such a network would speed up the time it takes for doctors to gain access to new biomedical research, which in turn can help with diagnosis and treatment at the point of care. At the same time, medical researchers will have access to clinical data on large populations of patients that can further advance their research work.

In an interview with information Healthcare, Charles Sawyers, co-chair of the committee that authored the report, and the inaugural director of the Human Oncology and Pathogenesis Program at Memorial Sloan-Kettering Cancer Center, said one of the report's most surprising and important findings was "the potential of the electronic medical record at the point of care to serve as a powerful clinical research tool, hence our enthusiasm for marrying genomic data into this interface."

The report envisions a data network based on a "new taxonomy" that will define diseases by their underlying molecular causes and other factors in addition to their physical signs and symptoms.

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Under the current International Classification of Diseases (ICD), taxonomy refers to a system in medical practice that tracks and classifies diseases and other health problems. This taxonomy provides standardized diagnoses that hospitals and clinicians use in their work, as do payers who rely on the information to determine reimbursement.

A transition from ICD-9 to ICD-10 code sets is currently underway and must be completed by October 1, 2013, but the new version still will have disease classifications based primarily on signs and symptoms and is "not designed optimally to incorporate or exploit rapidly emerging molecular data, incidental patient characteristics, or socio-environmental influences on disease," the report said.

By leaving out such critical data, the tendency has been to focus on symptoms rather than underlying causes, which carries the risk of misclassifying diseases. The committee went on to say that many subgroups of certain diseases have diverse molecular causes and are classified as one disease, while multiple diseases share a common molecular cause, but are not categorized in the same disease classification.

The committee said that classifying diseases based on genetic makeup already has proven helpful in determining which drugs work best in treating disease. In a set of trials on patients with non-small-cell lung cancer, for example, a drug was shown to produce dramatic anti-tumor effects in approximately 10% of the patients while other patients did not respond to the drug.

After it was found that the treatment could shrink the size of tumors, the drug was approved and used on a broad range of lung cancer patients. However, for most other patients rather than provide a cure, the drug had no effect other than to increase costs and side effects. Further patient observations revealed that those who responded to the drug carried specific genetic mutations, which allowed doctors to design a more effective clinical trial.

As the desire to move toward precision medicine evolves, the committee recommended a modernization and reorientation of the information systems used by researchers and healthcare providers to attain the new taxonomy. The report calls for creating a "knowledge network of disease" that integrates the rapidly expanding range of information on what causes diseases and allows researchers, healthcare providers, and the public to share and update this information.

According to the report, the first stage in developing the network would involve creating an "information commons" that links to individual patients a wealth of molecular data, medical histories--including information on social and physical environments--and health outcomes.

The second stage would construct the network and require data mining of the information commons to highlight the data's interconnectedness and integrate it with evolving research. Fundamentally, data would be continually updated by the research community and extracted directly from the EHRs of participating patients.

Susan Desmond-Hellmann, co-chair of the committee that authored the report and chancellor of the University of California, San Francisco, told information Healthcare that the federal government's incentive programs, which have spurred the adoption of EHRs, "will definitely have a positive impact" on developing the health information technology infrastructure that will enable the new data network to provide a more accurate classification of diseases.