Playing Catch-Up with Data

October 8, 2014
Playing Catch-Up with Data
Pharmacovigilance encompasses clinical care optimization, a big data model for efficient targeting of tests and treatments and vigilance for adverse events. [Source: Big Data]

Alex Philippidis

Personalized medicine has long offered more hype than hope. But as genetic knowledge has multiplied in recent years, researchers—and more importantly, computing tools—have begun catching up with all that far-flung data, harnessing it into new databases and systems that offer the best prospects yet for delivering on the promise of precision treatments.

In several papers published this year for which he was corresponding author, Leo Anthony Celi, M.D., M.P.H., clinical research director for MIT’s Laboratory of Computational Physiology, joined David J. Stone, M.D., a visiting professor at University of Virginia and faculty associate at UVA Center for Wireless Health, and others in discussing the challenges that can be addressed with new computing systems, and the key data such systems must capture for clinicians.

In June, Drs. Celi and Stone joined Andrew J. Zimolzak, M.D., a research fellow at Children’s Hospital Boston, in proposing an operational vision for real-time incorporation of external health data through “dynamic clinical data mining” (DCDM), which they envision as driving next-generation electronic medical records (EMRs) as well as “turning medical practice into a data-driven, logical, and optimized system.”

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