I’ve always argued: it’s not so much whether Google or Amazon know my search history or shopping preferences, it’s whether those data are then used to make decisions about whether I get access to credit or a job or to make healthcare decisions about me and my family.
The healthcare issue is raised in a NY Times article about how one hospital chain and insurance provider is using online consumer shopping data to help generate predictive models about patient costs and make treatment decisions.
The University of Pittsburgh Medical Center is using “predictive health analytics” to improve patient care and contain overall costs. But what’s the priority?
According to the NY Times:
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.
Acxiom is one data source that enables consumer shopping data and behavior to be factored into the model:
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.
Consumers are then segmented into different “market baskets.” Here’s the concern, which the article points out:
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.
Any for-profit or cost-conscious enterprise, regardless of the industry, will be tempted to do similar things with consumer data to contain costs or boost profitability. That’s the logic of the market. As you can see it becomes very dubious if Person A is being offered certain outcomes (job, loan, treatment) based on demographic and online behavioral data vs. Person B.
If these analytics help the University of Pittsburgh Medical Center actually deliver better care (while containing costs) that’s a beneficial outcome. But online consumer behavioral data shouldn’t be a part of it. Whether I shop at Amazon shouldn’t play in any way in decision-making about my healthcare.
On some level the genie is out of the bottle. But as a society we must find a way to balance the use of “big data” by companies and industry with consumers’ personal privacy. This is the central philosophical and policy challenge going forward.
But for a more practical and immediate view of consumer privacy and what’s happening in the market attend our free webinar this Wednesday with Jules Polonetsky, “The Top 5 Things Marketers Must Know about Location and Privacy.” It will offer a look at current and emerging privacy rules and give you a chance to ask specific questions about privacy.
This will be especially valuable for those involved in mobile and location-based marketing.
Polonetsky is the founder of the Future of Privacy Forum and one of the nation’s foremost experts on privacy. But he’s also trying to find workable solutions for both consumers and business.
Please join us this Wednesday, July 2, 10 am PDT /1 pm EDT. Register for the webcast.