Published online
March 14, 2013
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)1- 2
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.5- 6
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.
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.
AUTHOR INFORMATION
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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