Predictive analytics—a branch of data analysis that generates predictions about future outcomes through the power of computers to process large amounts of data using statistical modeling and machine learning—is increasingly applied in health care. While it has the potential to improve patient health and lower health care costs, the ability to peer into people’s future health status has also raised significant concerns about privacy and patient self-determination. Part I of this Note explains predictive analytics and machine learning in healthcare; it discusses data sources (which may not all be medical records) and examines several predictive analytics models. It concludes by assessing the risks posed by predictive health analytics, including psychological harms to patients and discrimination by healthcare insurers, healthcare providers, and employers. Part II summarizes existing federal data privacy and nondiscrimination legislation relevant to healthcare information in order to assess where the law leaves gaps regarding the regulation of predictive health data. By comparing predictive health analytics with genetic testing—another method of predicting an individual’s risk of disease where laws have been enacted to protect perceived “misuses” of test results—Part III reaches conclusions about how the law could treat the use of predictive health analytics and makes recommendations about future protections for patients.

* Executive Articles Editor, Southern California Law Review, Volume 94; J.D. Candidate 2021, University of Southern California Gould School of Law; B.A. Organismic and Evolutionary Biology 2014, Harvard University. I would like to thank Professor Alexander M. Capron for his invaluable guidance and insights during the drafting of this Note. I would also like to thank the Southern California Law Review Staff for their incredibly detailed and diligent assistance throughout the editing process. Last but far from least, a heartfelt thank you to my grandfather, Jerry D. Wu, M.D., and my parents, Lenora and Ted Funahashi, for their unwavering encouragement, love, and support.

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