Health / Pharma

Using Predictive Analytics To Track Heart-Failure Risk

Carilion Clinic, a network of healthcare centers based in Roanoke, Va., wanted a more accurate way to identify patients at risk for heart failure, so it turned to Big Data analysis and Electronic Health Records (EHR).

What’s at stake: Heart failure affects about 5.1 million U.S. adults, according to the Centers for Disease Control and Prevention. With predictive analytics, health care providers can track early signs of heart failure risk before illness strikes. In addition to saving lives, health care costs can be kept in check as doctors improve medical outcomes.

The clinic sought a system that could record and highlight patient attributes that indicate a high risk of a cardiac episode. Attributes to be collected included physiological data such as maximum systolic blood pressure and patients’ use of prescription heart medications such as alpha blockers and beta blockers. Patients also may have previously been diagnosed with chronic obstructive pulmonary disease or obesity, or experienced life events–such as a change in occupation or marital status–that are known heart stressors.

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