There may be a link between your Internet use and how often you end up in the emergency room.

At least that’s one of the curious connections to emerge from a health care analysis project at the insurance division of the University of Pittsburgh Medical Center.

U.P.M.C. is a $12 billion nonprofit enterprise that owns hospitals in western Pennsylvania as well as a health insurance plan with about 2.4 million members. It is at the forefront of an emerging field called predictive health analytics, intended to improve patients’ health care outcomes and contain costs. But patients themselves are often unaware of the kinds of intimate details about their households that insurers and hospitals may use to try to sway their treatment decisions.

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.