By using predictive models against credit card spending data, Carolinas HealthCare System wants to be able to identify and speak to high-risk patients before they get sick.
Information on consumer spending can provide a more complete picture than the glimpse doctors get during an office visit or through lab results, says Michael Dulin, chief clinical officer for analytics and outcomes research at Carolinas HealthCare. The Charlotte-based hospital chain is placing its data into predictive models that give risk scores to patients. Within two years, Dulin plans to regularly distribute those scores to doctors and nurses who can then reach out to high-risk patients and suggest changes before they fall ill. “What we are looking to find are people before they end up in trouble,” says Dulin, who is a practicing physician.
For a patient with asthma, the hospital would be able to assess how likely he is to arrive at the emergency room by looking at whether he’s refilled his asthma medication at the pharmacy, has been buying cigarettes at the grocery store, and lives in an area with a high pollen count, Dulin says. The system may also look at the probability of someone having a heart attack by considering factors such as the type of foods she buys and if she has a gym membership. “The idea is to use Big Data and predictive models to think about population health and drill down to the individual levels,” he says.
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