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Yale University – Mayo Clinic CERSI

Tuesday, June 11, 2019

About the Presentation

Data integrity and governance are key requirements for any data management platform. Within biomedical research and clinical trials, assurance of data validity and the documentation of the analytic process are critical for translating data into high quality evidence. While improving research transparency requires a multifaceted approach, the use of emerging cryptographic technologies, such as blockchain, may reduce the risk of data manipulation and boost the confidence in conclusions made by the scientific community. Similarly, as predictive algorithms and artificial intelligence are adopted in clinical workflows, a clear audit trail of the data used to train and validate these models will be needed.

To improve the visibility of data quality and analysis, we developed TrialChain, a blockchain-based platform that can be used to validate data integrity in large, biomedical research studies. We implemented TrialChain as a semi-private network to allow for a high degree of data security while also allowing for distributed, public validation of data assets. Original data and modifications from downstream analyses can be logged within TrialChain and rapidly validated when needed. The TrialChain platform provides a data governance solution to audit the acquisition and analysis of biomedical research data, providing cryptographic assurance of data authenticity.

About the Presenter

Dr. Schulz is an Assistant Professor of Laboratory Medicine and computational health care researcher at Yale School of Medicine. He is also the Director of Informatics for the Department of Laboratory Medicine, Director of the CORE Center for Computational Health, and Medical Director of Data Science for Yale New Haven Health System. He received a PhD in Microbiology, Immunology, and Cancer Biology and an MD from the University of Minnesota.

Dr. Schulz has over 20 years’ experience in software development with a focus on enterprise system architecture and has research interests in the management of large, biomedical data sets and use of real-world data for predictive modeling. At Yale, he has led the implementation of a distributed data analysis and predictive modeling platform, for which he received the Data Summit IBM Cognitive Honors award. Other projects within his research group include computational phenotyping and the development of clinical prescriptive models for precision medicine initiatives. His clinical areas of expertise include molecular diagnostics and transfusion medicine, where he has ongoing work assessing the use, safety, and efficacy of pathogen-reduced blood products.

Recording

Link: https://collaboration.fda.gov/pzu4pm67nfnm/