'To Share or Not to Share. That is Not the Question' - A Privacy Preserving Procedure for Sharing Linked Data

Abstract

Recently the New York Times reported on the University of Pittsburgh Medical Center using commercial data from Acxiom to develop analytical models to “intended to improve patients’ health care outcomes and contain costs.” The article summarizes the process as follows:

"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."

Not surprisingly, the legal and ethical implications of this practice has raised concerns:

"'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."

At first glance, there appears to be no way out of the dilemma. It is clear that sharing individual data can be perceived as intrusive and possibly unethical. On the other hand not sharing precludes the possibility of substantial benefits to both individuals and organizations. We contend that posing this question as either to share or not share individual data is to ignore the possibility of a compromise. It is possible for healthcare and other data to be linked and analyzed allowing us to realize most of the benefits (preserving statistical relationships) for a small price (little or no disclosure of private information). In this study, we describe a procedure for achieving this objective.

Keywords: data sharing, data privacy, confidentiality, privacy-preserving methods

JEL Classification: I18