Aided by big-data and cloud computing, "personalized medicine" is enabling doctors and researchers to evaluate the potential of existing drugs in different individuals and make better clinical decisions.
One of the best selling prescription drugs in the world is Clopidogrel, mostly selling under the trade name Plavix. Clopidogrel is prescribed to most patients with risk of blood clots, and to all patients receiving a stent. The problem with Clopidogrel -- as with many other drugs -- is the variable effects in patients: It works well for some but in others has almost no effect. The usual approach of physicians is to increase the dose, until they find that the drug doesn't work at all and then try another one.
But now we know that a specific gene, CYP2C19, is responsible for metabolizing clot-dissolvent medication such as Clopidogrel. The impact is enormous. Now most patients who are candidates to receive a stent are tested for variants of the CYP2C19 gene, with physicians seeking alternative procedures for the ones who can't metabolize Clopidogrel.
In order to differentiate the variants of those genes, it is necessary to perform Genome-Wide Association Studies (GWAS) on a significant number of affected individuals, with full genome sequencing performed to identify the responsible genes. But, to map the results and cross reference all genomic data, massive computational power is required. While the speed of genome sequencing has increased 1000-fold in the past ten years and the cost is approaching the $1,000 mark (the first genome took 13 years and cost $2.7 billion), cross-referencing all that information is still a huge challenge. A typical genome sequence of an individual yields around 3 million sequence variants compared with the reference genome.
This is where big-data and cloud computing are indispensable tools for researchers. The combined power of virtual machines and the storage capacity can do the work much faster today than in 2006, when the first GWAS were performed.
In the near future, when the genome of each person will form part of their Electronic Health Records, researchers would be able to perform GWAS over a (hopefully anonymized) massive database of patients with the click of a mouse, and forecast drug interactions before the pharmaceutical has been produced.
Almost 7 percent of hospitalizations in the US each year are related to adverse drug reactions resulting in hundreds of deaths. GWAS and pharmacogenomics can reduce the number of bad prescriptions, but the information needs to be in the hands of the patients and physicians.
That, of course, is where the CIO comes in. With CIOs hard-pressed to meet the goals of Meaningful Use, maintain security and privacy with an increasing number of threats, and respond to the very real HIPAA issues with BYOD, the last thing CIOs need is another priority. However, bringing a robust DNA sequencing and storage capability to your hospital is a must. The ability to provide your doctors with the information that will allow them to make safer, more effective decisions, reducing risk and costs for your hospital while providing improvements in patient care and revenue, is priceless. Of course, those are the real goals of any healthcare CIO.