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Data systems in healthcare are lacking when it comes to the storage and handling of increasingly complex medical information, according to a new study published in the Journal of the American Medical Association.
Physicians are moving en masse to electronic health records, but existing data systems aren't sophisticated enough to make optimal use of ever-expanding patient information, according to one of the report's authors, Justin Starren, chief of the division of health and biomedical informatics in the department of preventive medicine at Northwestern University Feinberg School of Medicine.
This problem that will only be exacerbated as data grows apace – fueled by innovations such as next-generation genomic sequencing – and becomes cheaper and more available to health care providers.
As genomics, epigenomics, proteomics and metabolomics and other so-called 'omics' advance, the study shows, the ability to store large-scale raw data for future reference with patients is critical – and existing EHRs are not up to the task.
The study, "Crossing the Omic Chasm: A Time for Omic Ancillary Systems," was written by Starren with Marc S. Williams, MD, of Geisinger Health System, and Erwin P. Bottinger, MD, of Mount Sinai School of Medicine.
"EHRs are designed to facilitate day-to-day patient care," says Starren in a press statement. "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."
Even as diagnostics tests generate more and more information, just some of that data is actually transferred to a patient's EHR, the report shows.
For example, even as radiology images average more than 100 megabytes per patient among Northwestern Medicine partners in Chicago, only the radiologist's textual conclusions about the images are transferred to the EHR. The total storage needed for EHR data such as text reports, laboratory values and clinician notes is only around 375 kilobytes per patient.
So far, says Starren, that's been OK, since physicians have rarely needed to refer to prior diagnostic tests. But with the increasing use of genomics, epigenomics, proteomics and metabolomics, optimal patient care requires more robust data systems.
"An individual's genetic sequence changes little over a lifetime, but science's understanding of that sequence changes rapidly," said 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 is problematic, according to the report. Each patient sequencing generates between five to 10 gigabytes of data per individual – more than 50 times what imaging does.
Simply put, according to the authors, data storage systems designed for EHRs will quickly become overwhelmed.
Starren, Williams and Bottinger argue that healthcare providers and health systems must act today, rather than waiting for an entirely new generation of EHRs to emerge. They 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," said Starren. "The number of clinically significant variants is limited now, but the availability of next generation sequencing will greatly accelerate this flow."
Large organizations such as Northwestern will likely operate their own ancillary omics systems, while smaller practices may use reference laboratories, the authors note. 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.