Why Big Data Won't Cure Us.

The biggest challenge for the use of “big data” in health care is social, not technical. Data-intensive approaches to medicine based on predictive modeling hold enormous potential for solving some of the biggest and most intractable problems of health care. The challenge now is figuring out how people, both patients and providers, will actually use data in practice. To understand how data-intensive solutions could have an impact on health care, our research team talked to frontline providers in impoverished and rural areas, technology enthusiasts in mobile health and health IT startups, clinicians and researchers in major research hospitals, Quantified Self members at data-driven meetup presentations of massive amounts of tracking data, and attendees at the growing number of conferences for health technology and innovation up and down both coasts. I found the buzz as feverishly loud around health information innovation as it was during my research on the first dot-com boom. One of our findings from this research seems at first blush so obvious that it is hard to believe it has been overlooked in the design and implementation of health-care innovation technologies. Namely, people imagine data in very different ways. Understanding this key fact about data helps us understand why so-called “big data” solutions to health care are so difficult to implement in practice. Doctors, patients, and health-care entrepreneurs all value data in very different ways. One physician simply said, “I don't need more data; I need more resources.”* Saying this in Silicon Valley or at TedMed would be tantamount to heresy. Ditto for those of us who work in research and spend our careers collecting, massaging, managing, analyzing, and interpreting data. From the doctor's perspective, though, data require (and do not save) extra interpretive, clerical, and managerial labor. This perspective on “data,” at least with regard to current clinical practice, is that data use up more resources than the benefits they provide. In other words, most doctors think data innovation means more work for them, not less, and takes away time from what they see as their key priorities in providing quality care. In another setting, we observed nurse-practitioner case managers in a Medicare demonstration project working with a simple algorithm parsing patient-entered health data. Combined with case management, these data provided a look into the daily health of chronically ill elderly patients and a pathway for the care when it was needed. The data in that project were tightly tied to medical expertise within an existing clinic where a trusted person could initiate a chain of care responses. Although widely recognized as a clinical success, Medicare pulled the plug on the project for financial reasons—expertise is expensive. These two reactions to data-intensive pilot projects highlight the dilemmas of data-analytic approaches to health care. Businesses are in the thrall of the possibilities of ever-increasing predictive analysis on expanding troves of generated data. While the business and technology sectors see data as valuable, doctors often see data as costs, risks, and liabilities. And for many in health care, data are not seen as a source of value, but of additional work. Without the work needed to make data valuable and useful in particular settings in particular contexts in health care, big data will never solve problems. To turn a technology truism on its head, data in health care will never be free. And yet, the ways in which health technology innovators have talked about the power of data neglects key aspects of the social interoperability or integration of data into health solutions. How will such data be integrated into care providers' work practices; through the complex routines of clinics and hospitals; and into existing legal, social, political, and economic frameworks? These questions are enormous. Until we solve these questions of social interoperability, the risks presented by “big data” in health will outweigh the benefits to any particular individual, regardless of whether we're talking about terabyte-scale analytics or the “small-data” of n=1 individuals. What follows is an outline of how to tackle these questions based upon what our team has seen throughout our research.