The Legal And Ethical Concerns That Arise From Using Complex Predictive Analytics In Health Care

TLDR

Predictive analytics uses electronic algorithms to forecast events in real time, leveraging big data to improve patient health and reduce costs, but it also raises policy, ethical, and legal challenges. The article examines the major challenges of implementing predictive analytics in health care and proposes broad recommendations across the four phases of a model’s life cycle. The authors recommend that model developers establish governance structures that involve patients and other stakeholders from the earliest development stages. Developers may use pre‑existing patient data without explicit consent if they comply with federal regulations on human subjects research and health information privacy.

Abstract

Predictive analytics, or the use of electronic algorithms to forecast future events in real time, makes it possible to harness the power of big data to improve the health of patients and lower the cost of health care. However, this opportunity raises policy, ethical, and legal challenges. In this article we analyze the major challenges to implementing predictive analytics in health care settings and make broad recommendations for overcoming challenges raised in the four phases of the life cycle of a predictive analytics model: acquiring data to build the model, building and validating it, testing it in real-world settings, and disseminating and using it more broadly. For instance, we recommend that model developers implement governance structures that include patients and other stakeholders starting in the earliest phases of development. In addition, developers should be allowed to use already collected patient data without explicit consent, provided that they comply with federal regulations regarding research on human subjects and the privacy of health information.