How big data is beginning to change how medicine works

The face of medical care is rapidly changing thanks to major advancements in the capture, proliferation, and analysis of medical data. Technologies like the electronic health records (EHRs) and personal health records (PHRs) are drastically improving the way data is aggregated and shared. Now the hope is that big data analytics will help to make sense of seemingly endless streams of medical information.

As many doctors are painfully aware, outcome-oriented care is no longer a buzzword but a reality. The Center for Medicare and Medicaid Services has started to implement a program where payments are based on the ability of providers to meet key National Quality Strategy Domains (e.g. care criteria). Public payers are testing this new methodology, and private payers are expected to soon follow.

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Hospitals, payers, and doctors have the opportunity to apply big data analytics to test key clinical variables in a real world setting. Previously, doctors would base their treatment choices on clinical trials and research studies. These studies used a clear test and control group, with idealized parameters, as well as a reasonable sample size. In a non-clinical trial setting, doctors were unable to conduct any reasonable studies because every patient and patient environment was unique. Coupled with low sample sizes and many other factors influencing outcomes, traditional testing methods and elementary analytics were not feasible.

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Big data analytics companies such as Applied Predictive Technologies (APT) are bringing capabilities to healthcare that make real world testing of drugs and treatment protocols a clear strategic opportunity for providers and payers. Hospitals, and even individual providers, can use big data analytics to generate a range of insights — everything from relatively basic analysis such as determining the frequency of certain diseases in a given population to complex analytics, like optimizing treatment protocols or verifying the efficacy of a new drug in a real world setting.

Our colleague at APT, Faruk Abdullah, tells us, “Real world testing and predictive analytics will allow physicians to make recommendations that will be more tailored for an individual patient, because each plan will be optimized, taking into account appropriate factors such as lifestyle, medical history, and even likelihood of correlated future illnesses.”

These big data analytics applications can also be relevant for the FDA, which may want to see how drugs perform in a non-test environment to ensure the appropriate patient populations are receiving the drug. I also expect pharmaceutical companies to actively scour this data to track drug efficacy post-release or identify markets that could “benefit” from increased penetration.

I am eager to see how the data evolution improves outcomes for doctors and patients.

Daniel Kivatinos is COO and co-founder of Drchrono. His focus has been in the technology space since 2001 as a software engineer and entrepreneur. He will be presenting at HealthBeat 2014 with Dr. Angela Walker on October 28, 2014 at 11:20am on the Main Stage. Come listen to him discuss “What’s in the black bag for today’s mobile physician.”