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February 04, 2015
Precision medicine in the era of big data
By: Arda Ural

From Accenture Strategy for Life Sciences

For the last four years, I have had the opportunity to meet with a group of MBA and MHA (Masters in Health Administration) students from the Healthcare and Biotech Club as well as the Consulting Club of Cornell Johnson School of Business to give a conference about the latest developments and outlook in life sciences. The topic is selected by the students, and this year we focused on “Precision Medicine in the Era of Big Data”.

Coincidentally, two nights earlier, President Obama urged the Congress to support a new “precision medicine” initiative he will put forth in order “to bring the nation closer to curing diseases like cancer and diabetes.” This statement is another testimony to the fact of tailoring treatments to a patient’s genomic profile and life style will help bend the unsustainable healthcare cost curve, and focus treatments on improved health outcomes versus fee for service efforts focused on simply billing for patient visits or procedures.

For personalized medicine to be a reality at the clinical level, certain features are necessary:

  • Broad adoption of electronic medical records;

  • Genomic data for clinical decision-making;

  • Providers’ access to clinical decision support tools powered by healthcare analytics;

  • A personalized health plan informed by the individually stratified risk profile of a patient’s propensity for a particular disease or condition.

There were three topics at the conference that stimulated a broader conversation.

First, the recent introduction of innovative Hep C treatments by Gilead and AbbVie, triggered a discussion around pharmaceutical pricing and the shifting of emphasis to value. With the advances in medicine, clinicians cannot just manage the symptoms or progression of the disease but they can actually cure the patient by completely eradicating the viral load, as is the case for Hep C Genotype 1 patients.

The challenge is whether the market can bear the high price of these true blockbuster therapies, even with cure rates of over 95%. One would think pricing such cures would be a relatively well-defined HEOR modeling and conjoint pricing exercise. However, in reality, very-publicly debated deals between Express Script, CVS and other payers and PBMs ensued with the two pharmaceutical companies battling for market share.

This phenomenon is another strong indication that the healthcare ecosystem shaped by the Affordable Care Act will continue to demand evidence for system value rather than the traditional commercial approach in which the pharma company prices the products at a rate that the market will bear and push the marketing messages to providers through massive sales forces. Both companies are fervently competing, and offering care models to providers and patients to augment the outcomes of their near 100% cure rates as demonstrated in clinical trials.

The next discussion topic was centered on the ability to differentiate the signal from the noise in the era of Big Data. The US population generates several petabytes of data every day, comparable to the content contained by the Library of Congress. This considerable size of unstructured data offers opportunities to identify trends and patterns, but it also presents a huge amount of noise confounding the efforts of algorithms to make sense of it all. A helpful read on the subject is the bestseller “The Signal and the Noise: Why So Many Predictions Fail - but Some Don't” by Nate Silver, who was a guest speaker at an Accenture Analytics event in NYC in May of 2013.

Finally, another interesting discussion was about the impact of heath information technology on provider and patient interaction in the exam room. The adoption of EMR technology has attained a critical mass in the US (and around the world) with incentives provided within the HITECH Act, passed by the Congress as part of the Stimulus Package in 2009.

This is an excellent example of how technology adoption will also trigger some unintended consequences such as providers potentially missing some diagnosis by spending more time looking at the computer screen to enter their observations. The literature* is growing with Sociotechnical Analyses describing how technological advances will shape patient provider interaction with both positive and negative effects. As with any new technology, we will need to push the balance in favor of the benefits while managing the risks.

We are in a truly thrilling era in the healthcare industry. I am excited to see what topics this bright group of MBA/MHA students will be asking about for next year’s discussion focus.

Arda Ural, PhD


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