Friday, March 15, 2013

Crossing the Omic Chasm. A Time for Omic Ancillary Systems

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Justin Starren, MD, PhD; Marc S. Williams, MD; Erwin P. Bottinger, MD
JAMA. 2013;():1-2. doi:10.1001/jama.2013.1579.
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Published online March 14, 2013
Figures in this Article
Despite the information gains from genome-wide association studies and next-generation sequencing (NGS), there remains a chasm between this scientific knowledge and daily clinical practice. Leveraging recent advances in genomics to improve patient care will require electronic health record (EHR) systems that incorporate genomic clinical decision support (CDS). The eMerge (Electronic Medical Records and Genomics)12 consortium is bridging this chasm by developing interoperable systems that can integrate large-scale genomic data with clinical workflows. According to a recent Institute of Medicine report,3 the current document-centric approach to omic (eg, genomic, epigenomic, proteomic, metabolomic) data will not scale, making storage of raw omic data in current-generation EHRs not feasible. Although commercial EHRs may eventually evolve to handle omic data efficiently, dedicated omic ancillary systems will be essential in the interim.
Historically, “EHR” has been taken to refer to the system used in day-to-day patient care, whereas systems that collect and manage specialized data, such as laboratory, pharmacy, and imaging, have been considered “ancillary” systems. Before discussing how an omic ancillary system would function, it is useful to consider how omic data differ from conventional health data. The simplest difference is the amount of data. Current EHRs are optimized to manage large numbers of discrete and actionable items to facilitate clinical care. They are not designed to store large blocks of data that do not require rapid access. When diagnostic tests create large data sets, ancillary systems are developed to manage the data, and only a subset of clinically relevant information is transferred to the EHR. The archetypal example is imaging. Radiologic images are stored in a picture archiving and communication system (PACS). Radiologists interpret the images to generate a report, and only the text report is stored in the EHR database. An EHR may interface with a PACS to display images, but it does not store them. This dichotomy is evident by comparing the amount of data stored in the EHR vs that stored in ancillary systems. For example, at Northwestern University, the EHR averages 375 kB per patient, whereas the PACS averages 104 MB per patient (a 277-fold difference). Typical NGS identifies 3 to 10 million variants per individual,4 requiring 5 to 10 GB of storage (50-fold greater than imaging). Compression may reduce the absolute storage requirement but will not reduce the number of variants to be considered.
One maxim taught to medical students is never to order any test unless the results of the test may affect a treatment decision. The implication of this maxim is that clinicians know how they will interpret each piece of information before that information is collected. Most clinical laboratory tests measure variable parameters (eg, serum sodium level) compared with set reference values, allowing interpretation using the relevant clinical context. It is unlikely that current understanding of sodium metabolism will change so radically that reinterpretation of historical sodium values is necessary.
Omic data are different. An individual's germline genetic sequence changes little over a lifetime, but understanding of that sequence is changing rapidly. For years the DNA between coding regions was called “junk,” but it is now known that this DNA plays an important role in gene regulation.56 The 1000 Genomes project has identified tens of millions of different genomic variants; the clinical significance of these variants is mostly unknown, but current understanding is rapidly changing. Unlike serum sodium levels, the clinical implications of NGS obtained today will keep changing for years as knowledge evolves. This necessitates systems that dynamically reanalyze and reinterpret stored static genomic results in the context of evolving knowledge.
The Figure shows the possible data paths that omic data may take from collection to use. Today, most genomic results are delivered as textual reports (route A). Small panels of genes can be reported as conventional laboratory results (route B).7 Although some results will likely continue to be stored this way, NGS will overwhelm both routes. Conventional human-centric approaches to genomic results do not support reinterpretation as new knowledge emerges and will be gradually replaced by computer-centric approaches.
Figure. Integration of Genomic Data With Electronic Health Records
Integration depends on the format and complexity of the data. Note that data paths convey high-level data flows and do not necessarily imply point-to-point connections. CLIA indicates Clinical Laboratory Improvement Amendments.
Increasingly, NGS and other high-dimensionality omic testing will become routine, creating large, complex data sets and setting the stage for “omic” ancillary systems. 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. Three paths could facilitate transfer of actionable genomic information to the EHR: (1) results of the genomic analysis could be manually reviewed, converted to a textual report, and presented to the clinician (route C); (2) “computable observations” could be created and stored within the EHR, where the observations can be used to trigger conventional CDS rules (route D); or (3) an external CDS system could be incorporated that is queried by the EHR at appropriate points in the clinical workflow (route E). Although that CDS system is shown as part of the omic ancillary system, it is also possible that the CDS system could be external to the organization.
Large organizations will likely operate their own omics ancillary systems, in the same way that they maintain other ancillaries. For smaller practices, reference laboratories may add omic ancillary services to their existing services. The number of clinically significant variants is currently limited, but the availability of affordable NGS will greatly accelerate this flow. Omic ancillary systems are one way to bridge the omic chasm without waiting for an entirely new generation of EHRs to emerge.


Corresponding Author: Justin Starren, MD, PhD, Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 750 N Lake Shore Dr, 11th Floor, Chicago, IL 60611 (
Published Online: March 14, 2013. doi:10.1001/jama.2013.1579
Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Bottinger reported that he is named on a previous patent application for personalized clinical decision support.
Funding/Support:The eMERGE Network was initiated and funded by National Human Genome Research Institute through the following grants: U01HG006828 (Cincinnati Children's Hospital Medical Center/Harvard); U01HG006830 (Children's Hospital of Philadelphia); U01HG006389 (Essentia Institute of Rural Health); U01HG006382 (Geisinger Clinic); U01HG006375 (Group Health Cooperative); U01HG006379 (Mayo Clinic); U01HG006380 (Mount Sinai School of Medicine); U01HG006388 (Northwestern University); U01HG006378 (Vanderbilt University); and U01HG006385 (Vanderbilt University serving as the Coordinating Center).
Role of the Sponsors: The funders had no role in the preparation, review, or approval of the manuscript.
Additional Contributions: We thank all the members of the eMERGE EHR Integration workgroup for input on this article.


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