“Big Data” versus the “Right Data

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Ever-more powerful algorithms are “crawling” through the petabytes of insurance claims, PROM’s [3], pharmacy, genome and other healthcare databases in an attempt to drive more efficient therapies and medications and to help control spiraling medical costs.  However, the results have lagged the promise.  See here and here for two examples (from the American Medical Association, and Harvard Medical School.)

A major reason for disappointment with “big data” is the absence of data which are reliable (collected real-time), longitudinal (integrated across the patient’s entire treatment path) and clinically-relevant (represents the scientific and clinical criteria associated in the respected medical literature with better outcomes.)

Such useful data are notoriously difficult to obtain. By definition, they should be collected before, at and after the point of care by treating physicians or their assistants. However, those healthcare professionals are already overburdened by their clinical responsibilities, as well as other data-entry requirements increasingly demanded by government and insurers. Practicing physicians are often uncertain as to which clinical/scientific data should be collected.  Pre-treatment and post-discharge outcomes data are, at best, episodic and poorly correlated with clinical data.   Moreover, they are often not standardized and are difficult to obtain.

inCytes™ was developed by leading product-independent clinicians and medical scientists.  It is now being used to address the foregoing obstacles.  It is an important tool. efficient and low-cost for any group – hospitals, ACO’s, payors, regulators, clinicians and researchers — seeking to develop evidence-based, superior, predictable and lower-cost outcomes over the long term for broad patient groups.

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