The federal government's newest health research institute, PCORI, is using big data to take a patient-centric approach to medical research.
Across the federal government, officials are finding ways to improve the quality of public services by tapping into the potential of big data. Public health is no exception.
Between now and 2025, the amount of health data produced by electronic health records (EHR), physicians notes, clinical trials, and even social media, is expected to grow fifty-fold. This proliferation of health data is helping to accelerate the development of new models of health service provision in the U.S.
Insurers and physicians are increasingly adopting more cost-effective risk-sharing approaches, replacing the traditional fee-for-service method. For example, the formation of accountable care organizations (ACOs) – a major component of the President’s signature healthcare law – relies on the availability of good data to enable healthcare providers to pool risk more efficiently.
Yet, the Affordable Care Act (ACA) is only one of a number of initiatives aiming to transform the way the federal government uses data to improve public health outcomes and manage costs.
Last July, the National Institutes of Health (NIH) allocated $96 million to exploring the application of big data to biomedical research. The Food and Drug Administration (FDA) currently cross-references data from pharmaceutical trials against the health records of millions of Americans to identify safety risks in post-market drugs. The Centers for Disease Control and Prevention (CDC) matches over-the-counter drug sales with social media trends to track yearly flu outbreaks.
The largest and most ambitious effort of them all is known as the Patient-Centered Outcomes Research Institute (PCORI). Between now and September 2015, PCORI will work to digitally link the complete medical histories of almost 30 million Americans from hospital networks in New York and Chicago, Kaiser Permanente, and other groups.
Authorized by Congress as a part of the ACA in 2010, PCORI represents the most comprehensive effort to date to employ big data to advance comparative-effectiveness research. Its goal is simple: use large-n samples to track the relative benefits and risks of alternative treatments to help decision-makers identify those most effective.
Actually executing the necessary clinical research, however, poses a much greater challenge. Researchers face the daunting task of integrating hundreds of data streams from nation-wide providers, as well as developing new methods and analytic tools to sift through hundreds of terabytes of health information.
PCORI’s executive director Joe Selby, believes this process lends itself to improving patient care in addition to furthering scientific research:
“By focusing on outcomes and data from real-world settings, we’re more likely to obtain relevant answers to the many important questions patients have. Consider patients newly diagnosed with cancer. They want to know not just survival rates, but the impact of different therapies on their quality of life or ability to work. Such outcomes matter to patients, but research often fails to address them.”
Though unlike NIH, which funds more than $30 billion in research annually, PCORI can operate only a handful of studies per year on its $500 million budget and must convince lawmakers of the institute’s value by 2019 or else face sunset.
Nevertheless, PCORI officials are hopeful that a more holistic, patient-centric approach to public health research can serve as an effective complement to work done by other federal agencies.
While the landscape of public health has shifted dramatically in the last five years, mission remains the same. Tools like big data are allowing researchers at PCORI to examine the most persistent challenges in medicine with a fresh set of eyes - perhaps just what is needed to unlock the next breakthrough.
To learn more about how federal agencies are using big health data, follow the link below to read GBC’s latest issue brief: Real-Time Analytics: The Key to Keeping Pace With Big Health Data
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