3 health systems, precision med vendor partner to personalize care

Three well-known healthcare delivery systems are teaming with a precision medicine vendor to develop algorithms to personalize care delivery.

Participating providers include LifeBridge Health in Baltimore; Riverside Health System in Newport News, Va.; and St. Luke’s University Health Network in Bethlehem, Pa.

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The organizations will work with Progknowse to develop predictive algorithms using machine learning technology for precise and actionable therapeutic recommendations to improve and personalize care delivery. Progknowse is building a clinical and genomic dataset using predictive analytics that will be compatible with the Connect performance improvement platform developed by Premier, a group purchasing and healthcare services vendor.

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Progknowse also is giving its partners access to anonymized clinical and administrative data for scientists and the machine learning tools they use to build algorithms that measure and predict patient outcomes based on thousands of individual predictors.

“We enjoy working with other like-minded regional health systems to build a platform for precision medicine that is both innovative and affordable,” says Charles Frazier, MD and chief medical information officer at Riverside Health System. “By applying these predictive algorithms to our health data, we hope to accurately predict patients’ outcomes of a specific treatment to help patients, family and clinicians select the best treatments for them.”

Aldo Carmona, MD, and senior vice president of clinical integration at St. Luke’s, says the opportunity to combine the use of Premier’s platform and its database with predictive machine learning models from Progknowse will facilitate precision medicine for patients. “Collaborative projects like this are critical to taking advantage of our expanding data and bringing efficient, cost-effective care to our patients,” he adds.

Progknowse is using Premier’s platform to access national datasets that contain de-identified clinical outcomes data on nearly half of U.S. patient discharges. This enables scientists to create algorithms that advance and personalize treatment based on clinical ad genetic information.

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