Health Data Marketplace - Challenges, Possibilities, and the Path Forward

Abstract

AI's explosive growth has created unprecedented demand for healthcare data at scale, yet establishing trusted health data marketplaces remains elusive despite clear benefits. This special ConV2X Decentralized Health Podcast explores the persistent obstacles preventing these platforms from emerging, examines the competing forces shaping this landscape, and discusses actionable pathways to unlock permission based data access for advancing AI driven healthcare breakthroughs. Topics covered include: What is a health data marketplace exactly and what features it needs to have to be successful? Who should care about such a marketplace and why? Are there any marketplaces in other industries that we can think of as a reference here? What data would be traded in this marketplace and what is the utility and value for the different participants? More specifically, how to assign value to data? What are the incentives at play here and is there a Prisoner's dilemma of the existing players and stakeholders in the eco-system? What is holding us back from realizing such a marketplace? In particular, can you comment on how the regulatory framework helps or hinders progress; please comment on the US and EU cases? Given all this, is there a viable path to establishing the health data marketplace and what is the next critical step for it? How are capital markets seeing this opportunity? Are the VCs putting funding in making this idea happen? How? What have they exactly funded so far and what has come out of it? How can this community help realize this vision? What is your call to action? Speakers Francisco Curbera, PhD, is Senior Vice President for Engineering and Research at Accrete.ai, where he leads the development of knowledge-based AI technologies and solutions. Previously he held senior management roles at Swift, IBM Watson Health and the IBM Research division, driving research and development in emerging technologies, including blockchain, health informatics and AI. He received his Ph.D. from Columbia University and is author 17 patents and of over 50 publications. Shahram Ebadollahi, PhD, MBA, is an entrepreneur, advisor, board member and currently provides strategic consultation to companies in the broad area of AI in enterprise. His clients are pharma companies, AI compute and infrastructure companies, and private equity firms. Previously, he was the Chief Data and AI Officer of Novartis where he was responsible for the areas of data science, artificial intelligence, data management and strategy globally across the Novartis corporation. Before joining Novartis in September of 2018, Dr. Ebadollahi spent 14 years with IBM and had series of senior executive roles. He was the strategist and technical founder of IBM Watson Health, IBM’s first ever business focused on applications of technology and AI to the domain of healthcare and life sciences. In that role he oversaw the areas and teams for innovations, development, alliances and served as the Chief Science Officer of IBM in healthcare. He also started IBM’s efforts in use of Blockchain technology in health and formed the Health Utility Network which was later spun off as a standalone company. Prior to starting IBM Watson Health, he founded the Health Informatics Research and Computational Health, with the aim of innovating and applying data science and advanced analytics to the broad domain of healthcare and life sciences. Dr. Ebadollahi received his PhD and MBA from Columbia University in the City of New York. He serves as Independent Board Director as well as Scientific Advisory Board for a number of companies. In addition, he has been a member of the advisory board for Columbia University’s Data Science Institute and serves on the Board of Visitors of the School of Engineering at Columbia University. He has published and overseen graduate level research in the area of analytics for healthcare and has conducted research with funding from national institutions such as NIH. He is a frequent speaker and panelist at events at the intersection of technology innovation and healthcare. Olivier Elemento, PhD, is a tenured full professor of physiology and biophysics at Weill Cornell Medicine (WCM) and Cornell University. Since 2017, he has been the Director of the Englander Institute for Precision Medicine, a large multi-disciplinary institute that uses precision medicine technologies and informatics to uncover the molecular mechanisms of disease and individualize disease treatment and prevention. He is also the Associate Director of the Institute for Computational Medicine, Director of the Laboratory of Cancer Systems Biology, Co-Leader of the Genetics, Epigenetics, and Systems Biology Program in the Meyer Cancer Center at Weill Cornell Medicine. Dr. Elemento’s research group combines Big Data, Artificial Intelligence with experimentation and genomic profiling to accelerate the discovery of cancer cures. Dr. Elemento and his team have published over 460 scientific papers in the area of precision medicine, genomics, epigenomics, artificial intelligence, computational biology and drug discovery. He has received several awards including the NSF CAREER Award, Siegel Family Award for Outstanding Medical Research, the Irma T. Hirschl Career Scientist Award and the Daedalus Fund for Innovation Award. He has mentored over 30 graduate students and postdoctoral fellows, several of which have become faculty members, joined pharmaceutical companies, and two of his students were chosen as 30 under 30 in Healthcare by Forbes Magazine in 2016. Dr. Elemento and his research group have developed new assays and analytical pipelines for cancer genome and epigenome analysis, clinical sequencing and precision medicine. They led the development of the first New York State approved whole exome sequencing test for oncology. They developed new methods for assessing tumor-driving pathways, the immune landscape of tumors and predicting immunotherapy responders. In addition, they developed methodologies to repurpose existing drugs to target specific pathways, predict drug toxicity and identify synergistic drug combinations. Their research has been highlighted in broad audience media outlets, including Popular Science, CBS, Gizmodo, Huffington Post.

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