Clinical care decision-making can be improved though the use of a “closed-loop” information processing cycle that integrates sensed and recorded data on individuals (initially centered around a combination of highly-multiplexed, longitudinal protein measurements, and electronic healthcare records). This approach applies, analyzes, and visualizes those data using “causality cases” (authoritative findings about how sensed data relate to diagnosis), so as to enable personalized diagnostic and treatment guidance for consideration by the patient, the care team, and the healthcare administrator. We integrate scans of the proteome with a “learning” information processing system. The resulting system, soon capable of measuring thousands of proteins simultaneously from very small blood sample sizes, can be the basis for significant improvement at the system level in the healthcare system, helping improve average outcomes while decreasing total costs. Advances in both data processing technology and biotechnology have reached a level that make this approach feasible, and if applied broadly and effectively, can provide a scalable mechanism for achieving the “triple aim” of simultaneously improving care outcomes, lowering costs, and enhancing the patient experience.
Sector Vice-President and Chief Technology Officer, Northrop GrummanNo slides available
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