Cyber - Project - Cybersecurity of Health Care Data in Population-Based Health Information Exchanges

Cybersecurity of Health Care Data in Population-Based Health Information Exchanges

Health information exchanges (HIEs) integrate electronic health records from multiple healthcare facilities in a geographic region, and secondary use by researchers of the data from these large generalizable samples have tremendous potential for benefitting population health and informing public policy. Although state and federal laws provide some architecture for protecting healthcare information privacy, most of these laws predate and do not contemplate the recent explosion in the number of online information sources, leaps in computing capabilities, and rise in hacking incidents. The absence of standard data sharing guidelines with rigorous privacy guarantees has made health care organizations afraid to share data, posing significant hurdles for the population health research use of HIE data. An interdisciplinary group of faculty with expertise in population health, epidemiology, computer science, statistics, health policy, bioethics and law will address these challenges from two perspectives: (1) societal, legal and health policy aspects, assessing the public’s views regarding HIE privacy risks and proposing legal and policy measures that would better achieve the balance of risks and benefits the public desires; and (2) cybersecurity aspects, addressing the key technological challenges for designing secure encryption systems and statistical techniques and identifying critical gaps where novel solutions are needed.

Researchers

https://profiles.stanford.edu/lorene-nelson

Lorene Nelson

Associate Professor of Health Research and Policy

Epidemiology and genetic of neurodegenerative diseases (Parkinsons disease, amyotrophic lateral sclerosis), epidemiologic methods, pharmacoepidemiology.

https://profiles.stanford.edu/mark-cullen

Mark Cullen

Director, Center for Population Health Sciences, Senior Associate Dean for Research and Senior Fellow at SIEPR

Professor of Medicine (Primary Care and Population Health)

Social and environmental determinants of health; role of workplace physical environment and work organization as causes of chronic disease and disability

http://crypto.stanford.edu/~dabo/

Dan Boneh

Co-director of the Stanford Computer Security Lab and Co-director of the Stanford Cyber Initiative

Rajeev Motwani Professor in the School of Engineering and Professor of Electrical Engineering

Professor Boneh heads the applied cryptography group and co-direct the computer security lab. Professor Boneh's research focuses on applications of cryptography to computer security. His work includes cryptosystems with novel properties, web security, security for mobile devices, and cryptanalysis. He is the author of over a hundred publications in the field and is a Packard and Alfred P. Sloan fellow. He is a recipient of the 2014 ACM prize and the 2013 Godel prize. In 2011 Dr. Boneh received the Ishii award for industry education innovation. Professor Boneh received his Ph.D from Princeton University and joined Stanford in 1997.

https://law.stanford.edu/directory/michelle-m-mello/

Michelle Mello

Professor of Law and Professor of Health Research and Policy

Michelle Mello (BA ’93) is a leading empirical health law scholar whose research is focused on understanding the effects of law and regulation on health care delivery and population health outcomes. She holds a joint appointment at the Stanford University School of Medicine in the Department of Health Research and Policy.

https://profiles.stanford.edu/nigam-shah

Nigam Shah

Associate Professor of Medicine (Biomedical Informatics Research) and of Biomedical Data Science

Dr. Nigam Shah is associate professor of Medicine (Biomedical Informatics) at Stanford University, Assistant Director of the Center for Biomedical Informatics Research, and a core member of the Biomedical Informatics Graduate Program. Dr. Shah's research focuses on combining machine learning and prior knowledge in medical ontologies to enable use cases of the learning health system.