What Genetic Testing Teaches About Long-Term Predictive Health Analytics Regulation
44 Pages Posted: 1 May 2019 Last revised: 18 Jan 2020
Date Written: April 1, 2019
The ever-growing phenomenon of predictive health analytics is generating significant excitement, hope for improved health outcomes, and potential for new revenues. Researchers are developing algorithms to predict suicide, heart disease, stroke, diabetes, cognitive decline, future opioid abuse, and other ailments. The researchers include not only medical experts, but also commercial enterprises such as Facebook and LexisNexis, who may profit from the work considerably. This Article focuses on long-term disease predictions (predictions regarding future illnesses), which have received surprisingly little attention in the legal and ethical literature. It compares the robust academic and policy debates and legal interventions that followed the emergence of genetic testing to the relatively anemic reaction to predictions produced by artificial intelligence and other predictive methods. The paper argues that like genetic testing, predictive health analytics raise significant concerns about psychological harm, privacy breaches, discrimination, and the meaning and accuracy of predictions. Consequently, as alluring as the new predictive technologies are, they require careful consideration and thoughtful safeguards. These include changes to the HIPAA Privacy and Security Rules and the Americans with Disabilities Act, careful oversight mechanisms, and self-regulation by health care providers. Ignoring the hazards of long-term predictive health analytics and failing to provide data subjects with appropriate rights and protections would be a grave mistake.
Keywords: predictive health analytics, artificial intelligence, machine learning, genetic testing, HIPAA Privacy Rule, Americans with Disabilities Act, discrimination, health privacy
JEL Classification: K32
Suggested Citation: Suggested Citation