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Data mining: health care must look to adopt predictive analytics

November 29, 2016
Health IT
From the November 2016 issue of HealthCare Business News magazine

Still, nearly one-third of the survey takers are on the fence. In addition to the 19 percent who said they have no plans to use predictive analytics, another 11 percent are unsure whether they will use it in the future or not.

The provider’s ‘wish list’ for predictive analytics
Both current users of predictive analytics and prospective users agreed that the top priority for its use is to alert caregivers to interventions that may prevent health declines among high-risk patients, according to the survey. The survey findings show that providers are hungry for analytics that will help them identify and treat these patients before their health deteriorates.
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Asked to name the top three priorities for the use of the technology, respondents assigned the second and third spots to predicting financial outcomes (such as patient cost or likelihood of patients to pay their bills) and improving the ability of providers to negotiate contracts with insurers. Other priorities for predictive analytics identified by survey takers were projecting patient health outcomes and satisfaction, and improving the quality of diagnoses. Last on the list of priorities was the forecasting of staffing and supply chain needs (27 percent).

Clearing the barriers
Despite ambivalence about using predictive analytics, which appears to be largely rooted in concerns about a lack of supporting people and tools, the evidence is clear that this technology saves lives and slashes costs in the hospital. At health care organizations like Allina Health, El Camino Hospital and Mission Health, predictive analytics is helping to reduce readmissions and improve patient outcomes, while saving millions of dollars. Given what hospitals can achieve — and the resulting impact on population health — the health care industry should come together to hasten the widespread adoption of predictive analytics sooner rather than later.

About the author: Levi Thatcher, Ph.D., is director of data science for Health Catalyst, a leader in health care data warehousing, analytics and outcomes improvement.

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