De-identification: Protecting the Identities of HEAL’s Superheroes

Thu, 6/16/2022 - 1:00pm - 2:00pm


In this webinar, Stephen W. Erickson, Ph.D., of the HEAL Data Stewardship Group will describe methods and approaches for de-identifying study subjects in accordance with the 1996 HIPAA Privacy Rule. Topics will include the two methods available to satisfy HIPAA’s de-identification standards (Expert Determination and Safe Harbor); software tools and other online resources for de-identification; potential risks and how to minimize them; and steps to take if the identity of study subject(s) are ever compromised.

Following this brief presentation, Dr. Erickson will lead a panel discussion plus Q&A representing three participating repositories in the HEAL Data Ecosystem: Emily Dressler (Pain ERN), Bettina Hoeppner (COARs), and Emily Smith (NRN).

The NIH HEAL Initiative® aims to create a novel web platform connecting data from existing repositories to allow researchers to share, find, and analyze data, with the goal of collaboratively accelerating scientific discovery and developing solutions to address the national opioid and pain management public health crises. The more than 600 awards involved in the initiative are producing massive amounts of data and will be submitting them to repositories as rapidly as possible. Maximizing the scientific impact and utility of these data by making them Findable, Accessible, Interoperable, and Reusable (FAIR), however, must be done while keeping the health information and privacy of HEAL’s study subjects robustly secure.


Stephen W. Erickson, Ph.D., is a senior research statistician in RTI International’s GenOmics, Bioinformatics, and Translational Research Center. He has been developing analytical methods for high-dimensional biological data and researching the etiology of complex human disease since 2004, and has performed studies of gene expression, microRNAs, copy number variation, site-specific DNA methylation patterns, single nucleotide polymorphisms in candidate genes, and genome-wide association studies. Through his research, he has advanced the understanding of the multifactorial causes of nonsyndromic birth defects, including conotruncal and obstructive congenital heart defects, and has collaborated with a broad range of biomedical investigators. 

Emily Dressler, Ph.D., is an associate professor and vice chair in the Department of Biostatistics and Data Science at Wake Forest School of Medicine. She is the lead biostatistician with the Wake Forest National Cancer Institute Community Oncology Research Program Research Base. She earned her Ph.D. in the Division of Biostatistics at the Medical University of South Carolina, and her primary research interest is in clinical trials. She has expertise in adaptive phase I trial designs, such as the continual reassessment method, and has developed methods that incorporate ordinal toxicity grading and mixed toxicity/efficacy outcomes. Dr. Dressler has more than 10 years of experience overseeing statistical and data considerations for trials, including study design, monitoring, data intake/management, and analyses.

Emily A. Smith is a research systems programmer/analyst in the Center for Clinical Research Informatics at RTI International. She has more than 10 years of experience managing epidemiologic studies and conducting data management and reporting. Ms. Smith leads data management teams through study startup, data collection, and study closeout, directing activities such as data monitoring programming and processing, development of data quality checks, and development of reporting techniques to improve study data quality. As a SAS programmer, she is experienced in developing macros and reusable system code that can be easily applied to new projects for items such as data download and processing, reporting, and standardization for public-use datasets. She has assisted in the de-identification of public-use datasets for multiple trials in the Neonatal Research Network, for which RTI serves as the Data Coordinating Center.

Bettina Hoeppner, Ph.D., M.S., is an Associate Professor in Psychology at Harvard Medical School, Associate Director of Research of the Massachusetts General Hospital (MGH) Recovery Research Institute, and statistician at the MGH Clinical Trials Network and Institute. She is an experimental psychologist with research interests in mHealth technologies, smoking cessation, and engagement in auxiliary addiction services (e.g., mutual help, recovery community centers), where she frequently uses mixed methods approaches to inform treatment development and engagement with existing resources. She earned her two M.S. degrees (statistics, experimental psychology) and Ph.D. (experimental psychology) from the University of Rhode Island.  

For More Information, Contact:

Julie Hayes at [email protected]  


You May Also Be Interested In:

Open House for HEAL Data: Discussions With HEAL-Recommended Repositories

The more than 600 awards involved in the initiative are producing data at rapid rates, and identifying the right repository to house HEAL data is essential for researchers to share, discover, and develop solutions to urgently put an end to these interconnected crises and save lives.

Read More

Metadata 102: Empowering Search on the HEAL Platform

The NIH HEAL Initiative® aims to leverage its vast research portfolio, accompanying data, and research community to accelerate scientific discovery and develop solutions to address the national opioid and pain management public health crises. 

Read More

Demystifying Data Sharing

In this webinar, Kira Bradford of the HEAL Data Stewardship Group will lead a panel discussion around demystifying data sharing. The panel will feature HEAL Program Officers involved in data sharing and access; they will provide their perspectives and clarifications around data sharing as well as answer questions live during the webinar. 

Read More