Machine Learning Tool To Fight Death With Data Science
By Christine Kern, contributing writer
Risk prediction platform predicts population health costs and prescribes patient care optimization.
According to a Frost & Sullivan report, strong opportunities exist for Big Data in healthcare via population health management, clinical decision support, and the use of real-world data. In fact, solutions that directly impact care delivery and outcomes will be the focus over the next five years.
Julie Skeen, Healthcare IT Strategist at Infogix, told Health IT Outcomes, “Big Data may not by itself be able to cure cancer or AIDS, but by applying analytics to human DNA and the DNA of major diseases is already producing positive results for patients. By looking at Big Data, medical researchers can help patients get the best treatment for the type of disease they have, minimize the negative impact of those treatments and in the end save lives.”
Capitalizing on the potential for predictive analytics, KenSci announced it raised $8.5M in a series A funding — led by Ignition Partners with participation from Osage University Partners and Mindset Ventures — to accelerate innovation for KenSci’s machine learning platform and expand operations to support the company’s rapidly growing customer base.
“We invested in KenSci based on the fact the problems KenSci addresses are increasingly central to value-based care. KenSci has successfully integrated advanced machine learning with complex healthcare workflows to deliver tangible ROI for some of the biggest health systems within 90 days,” said John Connors, managing partner at Ignition Partners. “Because of this, KenSci’s risk prediction platform has already improved outcomes for thousands of lives and helped providers save millions of dollars, and this is only the beginning.”
As healthcare shifts to a value-based model, payers and providers are increasingly under pressure to proactively bend the risk curve in population health by transitioning to a preventive healthcare model. KenSci applies machine learning to patient data to predict clinical risk, financial risk, and operational risk, accelerating health systems’ transitions to value-based care.
“The challenge in healthcare analytics is not in the lack of data but in the ability to connect and combine data meaningfully to unearth patterns and predict risks. Legacy systems of records have created walled gardens, unwittingly causing poor health outcomes and creating an upward spiral in healthcare costs,” said Samir Manjure, CEO and co-founder of KenSci.
The KenSci platform is integrated with healthcare providers’ existing EMR, EHR, and other patient data systems and applies machine learning to help doctors determine which patients are at greatest risk for a variety of conditions. It assists physicians in determining the optimal path to preventing the condition, or the best course of treatment, should it be required.