The Pittsburgh health plan, for instance, has developed prediction models that analyze data like patient claims, prescriptions and census records to determine which members are likely to use the most emergency and urgent care, which can be expensive. Data sets of past health care consumption are fairly standard tools for predicting future use of health services.
But the insurer recently bolstered its forecasting models with details on members' household incomes, education levels, marital status, race or ethnicity, number of children at home, number of cars and so on. One of the sources for the consumer data U.P.M.C. used was Acxiom, a marketing analytics company that obtains consumers' information from both public records and private sources.
With the addition of these household details, the insurer turned up a few unexpected correlations: Mail-order shoppers and Internet users, for example, were likelier than some other members to use more emergency services.
Of course, buying furniture through, say, the Ikea catalog is unlikely to send you to the emergency-room. But it could be a proxy for other factors that do have a bearing on whether you seek urgent care, says Pamela Peele, the chief analytics officer for the U.P.M.C. insurance services division. A hypothetical patient might be a catalog shopper, for instance, because he or she is homebound or doesn't have access to transportation.
"It brings me another layer of vision, of view, that helps me figure out better prediction models and allocate our clinical resources," Dr. Peele said during a recent interview. She added: "If you are going to decrease the costs and improve the quality of care, you have to do something different."
The U.P.M.C. health plan has not yet acted on the correlations it found in the household data. But it already segments its members into different "market baskets," based on analysis of more traditional data sets. Then it assigns care coordinators to certain members flagged as high risk because they have chronic conditions that aren't being properly treated. The goal, Dr. Peel
The very idea of using consumer data-mining and marketing segmentation on patients troubles some technology and health law experts. Their concern is that such practices could ultimately result in the inequitable provision of medical care.
"This intensive, intrusive kind of data analytics that leads to differential treatment of customers, even if we are fine with it in the business context, needs to be disclosed in the medical context," says Frank Pasquale, a professor in health care regulation at the Seton Hall University School of Law.
Analyzing details about household attributes and habits of individual consumers is a longstanding practice in retailing, travel and finance. Credit card marketers, for instance, may analyze consumers' buying patterns and financial wherewithal to decide whether to pitch them elite-level special-privilege cards.
Now two factors are converging to speed the adoption of these techniques in medicine.