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Healthcare Making the Most of Predictive Analytics

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Healthcare Making the Most of Predictive Analytics

With the saturation of electronic health records, healthcare organizations have a wealth of patient data that could assist with predicting outcomes and lowering costs. But many aren’t using predictive analytics for clinical or financial outcomes for their organizations.

Predictive analytics is defined as data mining, machine learning, and statistical modeling of historical patient data to predict future outcomes. A 2018 survey of healthcare payer and provider executives by the Society of Actuaries found that 87% of executives say that predictive analytics is important to the future of their business and 60% believe that predictive analytics can save their organization 15% of more in the next five years. Only half of providers surveyed and 45% of payers reported current use of predictive analytics. Within five years, 87% of providers and 83% of payers say they plan to begin or grow use of predictive analytic tools at their organizations.

Most large hospitals and health plans have access to the tools needed to use predictive analytics effectively, but smaller organizations are still falling behind, says Bharat Rao, PhD, principal in the advisory services practice at KPMG LLP.

“I would say the small physician offices, which are directing a lot of the care in the U.S., are not using predictive analytics in the most effective ways. They have more difficulty getting a handle on their data,” says Rao. “But even the large health systems and stand-alone hospitals, though they may have the tools to use predictive analytics, many aren’t using them effectively.”

With the amount of data being captured, many healthcare organizations are struggling with how to use it in the smartest ways, says Beth Godsey, vice president of advanced analytics and informatics for Vizient, Inc., a member-driven, healthcare performance improvement company, in Irving, Texas.

“Healthcare organizations are sitting on a wealth of patient data and yet have only begun to scratch the surface on utilizing predictive analytics and machine learning to extract its most important insights,” Godsey says. “I frequently hear from some of the top hospitals in the country, ‘how do I thoughtfully bring predictive insights to my clinicians that can improve not only their work flow efficiency, but also patient care?’”

Using predictive analytics for high-risk patients

One of the most valuable use cases for predictive analytics is identifying and engaging high-risk patients. An analysis by Relias Analytics of raw claims data from the Illinois Behavioral Health Home Coalition to identify and predict outcomes of high users of care was able to reduce inpatient admissions by 57%, emergency department visits by 31%, and total cost of care by 40%. Annual savings associated with the patients in the study was $1 million.

Nate Regimbal, digital transformation senior manager with Grant Thornton, an accounting and consulting firm, says that predictive analytics techniques can identify patients at high risk for being late to or missing an appointment.

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