Healthy Predictions? Questions for Data Analytics in Health Care
65 Pages Posted: 20 Feb 2016 Last revised: 1 Mar 2016
Date Written: February 19, 2016
The Patient Protection and Affordable Care Act (“Affordable Care Act” or ACA), health information technology (HIT) adoption, and increasing implementation of electronic medical records, are all propelling health care into the world of big data. Big data, analytics, and predictive algoithms are poised to play a large part in the transformation of health-care delivery in the United States, determining who will benefit and, unfortunately, who may suffer from its insights. Health-care reform depends on cost savings derived from the application of sophisticated data analytics to the ever-expanding mass of data collected from and about individual patients. Health data analytics can lead to improved care, new scientific discoveries, and better medical treatment. Encouraging healthy behaviors, eliminating health disparities, and addressing the underlying determinants of health in society are important national goals. It is unclear, however, whether massive data collection about personal health and individual social status, both within the health-care system and outside of it, will serve the goal of addressing historical discrimination in health care, or whether data analytics will lead to the loss of individual privacy, unequal treatment of individuals, and the perpetuation of health inequality. Data amassed from electronic health records (EHRs), private sector health website visits, personal health devices, mobile health applications, and social networks, are being linked together in a big data environment. Secondary use of health data by employers, insurers, marketers, and others heightens concerns. The collection and use of massive amounts of data about individuals, fed into a fragmented health analytics framework, may impose personal and societal costs if not carefully constructed. Furthermore, a predictive analytics environment in health care may affect some groups differently than others, not decreasing health dis-parities but segmenting populations and resulting in differential care. Health-care providers and policy makers should ask hard questions about how harms to personal privacy can be avoided, stigmas prevented, and threats of unbridled commercialization ameliorated.
Keywords: ACA, healthcare privacy, discrimination, data analytics
JEL Classification: I18, K32
Suggested Citation: Suggested Citation