Is the main barrier for in healthcare analyzing and connecting the massive amounts of data present in electronic medical records, or is it generating the right data at the right level? To really move healthcare forward, argues Michael Groner, VP of engineering and chief architect, and Trevor Heritage, we need to move research-level testing (whole exome sequencing, genomics, clinical proteomics) outside of the research environment and make it widely available to primary care physicians. According to Groner, only when we amass large collections of such data will the true value of big data analytics methods be realized in medicine.
“It’s untenable to expect every physician or health care provider interested in improving patient care through the use of genomics testing to make the costly capital and other investments required to make this science a practical reality that impacts day-to-day patient care. Instead, the aim should be to connect the siloed capabilities associated with genomics testing into a simple, physician-friendly workflow that makes the best services accessible to every provider, regardless of geography or institutional size or affiliation…The true barrier to clinical adoption of genomic medicine isn’t data volume or scale, but how to empower physicians from a logistical and clinical genomics knowledge standpoint, while proving the fundamental efficacy of genomics medicine in terms of improved patient diagnosis, treatment regimens, outcomes and improved patient management.”
It’s a great dream, and parts of it will be realized in the future, but ignores many of the realities of in-the-trenches medical practice and medical science. Genomics medicine will simply not improve the diagnostic acumen for many clinical problems; it’s just the wrong method. Some examples include fractures, appendicitis, stroke, heart attacks, and many others. Sequencing my genome will not diagnose my diverticulitis. This has nothing to do with making genomic science and whole genome analytics a practical reality, but rather matching the tools to the appropriate medical problem and scale. Genomics is quite good at providing information about genetic risk of conditions, but not necessarily diagnosing them. Knowing that somebody has the BRCA1 breast cancer gene mutation does not tell you if they actually have breast cancer, and if they do which breast it’s in, whether it has metastasized, and where.
Groner’s larger point about the need to use data science to make personalized medicine a real-time reality, however, is well taken. For example, the new guidelines for treatment of cholesterol abnormalities with statins, powerful cholesterol lowering drugs, are based on a risk score that no provider can calculate in their head. Personalized medicine could evolve to generate a personalized risk assessment, based on a risk score for cardiovascular disease. Beyond this, one could imagine the risk score being modified by a proteomics analysis of subtle serum proteins and their associated contributions to cardiovascular risk, and a genomic analysis of hereditary risk. Integrating this evidence and providing clinicians with some measure of how to weight the predicted risk factors when making treatment decisions, are true growth areas for medical genomics and health informatics.