Big data in healthcare – are we focused on the health of the business or the patient?

Recently, I was talking to Mike Luby, a Pharma thought leader (he's the Founder, President and CEO of BioPharma Alliance and TargetRx, Inc.). Mike thinks that there are many exciting areas in which we can apply analytics in the field of healthcare. We at Vencore agree. Analytics can help the bottom line of many businesses, but we can also use these tools to improve health outcomes. When we think about these advances in the use of data analytics, we might break it down into three categories:

  1. Improving business practices,
  2. Discovering new drugs, and
  3. Improving patient care.

Pharmaceutical companies could use these vast quantities of data to improve their business practices in many ways. They could – to name just a few -- analyze their manufacturing processes, supply chain issues, and sales and marketing practices.

Drug discovery is a second promising use for large data sets. We already see exciting applications through molecular modelling, gene mapping, personalized medicine, and companion diagnostics. There are assays now designed to improve the efficacy of existing drugs by optimizing the dose administered for each individual patient.  The Vencore research and development team has been working with the New York University Medical Center on a project to map the DNA sequences implicated in schizophrenia.

Improving patient care is the third category in which we see big data playing a role in today’s world of life sciences.  Here we see potential for countless applications of data mining. Some hospital systems are using these data to create more effective discharge instructions for patients. Others are creating an avatar to interact directly with patients to remind them to take their medication. There is the mobile sensor collection platform – smart phones with sensors – that is being used to assess a patient’s quality of life and then use that information to measure the efficacy of a drug. At Vencore, we are improving patient care by finding patients with rare disease before they are diagnosed.

We are acutely aware of the temptation to be big data analysts just because that is the rage right now. We must continue to create holistic solutions and make sure that we analyze and use big data in the appropriate context. We don’t want to add to the noise. 

Just as a physician must decide what – if any – information a blood test adds to the clinical picture, so too must data scientists draw the correct conclusion from the analytics they perform.

Once we have fine-tuned these techniques in rare disease, we can find more contexts in which to apply our analytics. For example, we could look for underserved patients who can’t access care for their sickle cell crises.

I can’t wait to tackle that one. Another advance, waiting to happen.

Tara Grabowsky, MD
Chief Medical Officer