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Upgrades Needed for Big Medical Data

Scientists argue electronic health records, patient data storage must evolve to keep pace with scientific knowledge

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April 2, 2013 | by Marla Paul

CHICAGO --- Even as physicians across the nation transition to electronic health records, commonly known as EHRs, these data systems are not sophisticated enough to handle or store the amount of electronic information created by currently available medical technology, argue the authors of a new viewpoint published in The Journal of the American Medical Association (JAMA).

According to the authors, this chasm will only continue to grow as “big data,” including next-generation genomic sequencing, becomes cheaper and more available to health care providers. As fields such as genomics, epigenomics and proteomics advance, the ability to store large-scale raw data for future reference with patients is critical, and current EHRs are not up to the task. 

“EHRs are designed to facilitate day-to-day patient care,” says study author Justin Starren, chief of the division of health and biomedical informatics in the department of preventive medicine at Northwestern University Feinberg School of Medicine. “EHRs are not designed to store large blocks of data that do not require rapid access, nor are they currently capable of integrating genomics clinical decision support.”

When diagnostics tests create large amounts of data, the authors write, only a small portion of relevant information is transferred to a patient’s EHR. For example, radiology images average 104 MB per patient among Northwestern Medicine partners in Chicago. Only the radiologist’s conclusions about the image, via text report, are transferred to the patient’s EHR.  This is much smaller. The total storage needed for EHR data, such as text reports, laboratory values and clinician notes is only around 375 kB per patient.

This arrangement has generally worked because physicians rarely have needed to refer to prior diagnostic tests. Most times health care providers are looking for values that can be measured against an established healthy average. Cholesterol or sodium levels, for example, do not generally require physicians to review and reinterpret the data from previous tests.

With the rise of genomics, epigenomics, proteomics and metabolomics – referred to as ‘omics’ by scientists – in research and patient care, however, the data are different.

“An individual’s genetic sequence changes little over a lifetime, but science’s understanding of that sequence changes rapidly,” explains Starren. “Areas of DNA that were once considered genetic ‘junk’ are now known to play important roles in gene regulation and disease. We need dynamic systems that can reanalyze and reinterpret stored raw data as knowledge evolves, and can incorporate genomic clinical decision support.” 

While whole genome sequencing in the clinical setting is within the realm of affordability for academic medical centers today, storage for future reinterpretation presents a problem: each patient sequencing generates between five to 10 GB of data per individual—50-fold greater than imaging. Data storage systems designed for EHRs will quickly become overwhelmed.

Starren and the authors argue that health care providers and health systems must act today, rather than waiting for an entirely new generation of EHRs to emerge. The authors propose dedicated ancillary storage systems as an interim solution to store and analyze raw omics data.

“This approach adds value by providing a location to store variants of unknown significance until enough knowledge emerges to move these variants into clinical practice,” says Starren. “The number of clinically significant variants is limited now, but the availability of next generation sequencing will greatly accelerate this flow.”

The authors note that large organizations like Northwestern will likely operate their own ancillary omics systems, while smaller practices may use reference laboratories. Genomics clinical decision support systems may be part of the omics ancillary system, they write, but the decision system can also be external to the organization.

“The time for omics ancillary systems is now,” concludes Starren. “Already, groups such as the Electronic Medical Records and Genomics (eMerge) consortium, which includes Northwestern University, are developing systems that can integrate large-scale genomic data with clinical workflows. The limitations of current EHR technology must not prevent science from bringing this knowledge to patients.”

The viewpoint, “Crossing the Omic Chasm: A Time for Omic Ancillary Systems” is currently available in the online edition of JAMA. Marc S. Williams, M.D., Geisinger Health System, and Erwin P. Bottinger, M.D., Mount Sinai School of Medicine, served as co-authors on the article. They are also the co-chairs of the eMERGE EHR Integration Workgroup.