In July 2022, Mayo Clinic Platform announced a 10-year collaboration with St. Louis-based Mercy, one of the 25 largest U.S. health systems, with the goal of using data science and years of deidentified patient outcomes to find diseases earlier and start patients on paths to better health more quickly. Now Mayo Clinic Platform is expanding that distributed data network, Mayo Clinic Platform Connect, to include Hospital Israelita Albert Einstein in Brazil, Sheba Medical Center in Israel and University Health Network (UHN) in Canada.
Mayo Clinic says that with the combination of privacy-protected, cloud-based technology architecture and the growth of artificial intelligence and machine learning, this aggregated clinical deidentified data generates patterns to pinpoint disease earlier and identify best treatment options.
Mayo Clinic Platform says it provides secure, cloud-based access to de-identified clinical data across three continents. Connect uses Mayo Clinic Platform’s proprietary “Data Behind Glass” approach that enables each organization to work with an extensive set of de-identified data without moving it among the organizations. Each healthcare system keeps control over its de-identified data throughout the process.
Mayo Clinic Platform says the global alliance establishes a data-sharing network that transcends language barriers and accelerates AI-based solutions by using current data science and years of clinical data to create better patient outcomes around the world.
"We describe the data needed for fair, equitable AI as having depth (types of information), breadth (number of patients) and spread (heterogeneity),” said John Halamka, M.D., president of Mayo Clinic Platform, in a statement. “To transform healthcare globally, we must expand our distributed data networks to every continent. We must protect privacy, adhere to international laws and regulations, and incorporate knowledge from every language. Today, three premier medical centers in South America, Canada and the Middle East are joining our network. They will inspire and guide other regions and systems to join our worldwide effort.”
"We are thrilled to be part of this historic alliance to transform the future of health,” said Eyal Zimlichman, M.D., chief transformation officer and chief innovation officer at Sheba Medical Center and director and founder of ARC Innovation at Sheba Medical Center, in a statement. “Creating a truly global network that will break down language barriers and enable the inclusion of diverse populations, we are unlocking the potential of AI solutions to revolutionize health care worldwide. This is not just a game-changer, but a visionary leap toward data-driven health care,"
Connect will add a number of additional U.S. and global members in the coming months, Mayo Clinic said. In joining Mayo Clinic and Mercy, Hospital Israelita Albert Einstein, Sheba Medical Center and UHN will contribute to AI models that overcome language barriers, using data from global populations across different geographies to improve accuracy, reduce model bias and create more diverse, and therefore stronger, treatment recommendations for patients.
"In addition to accelerating the possibilities of leveraging and transforming data-based care, Mayo Clinic Platform Connect will allow the inclusion of the Latin American population, currently underrepresented in research, in global studies with cutting-edge technology and artificial intelligence," says Sidney Klajner, president of Einstein, in a statement. "There is a clear need to consider this population profile, taking into account the context and genetic characteristics. This will be reflected in the development of more equitable health projects, expanding the supply of low-cost digital resources for vaccines and medicines, for example. We are honored to be founding members of this important network led by Mayo Clinic."
The alliance will initially focus on patient outcomes through:
• Information collaboration: Secure cloud-based use of Data Behind Glass allows each collaborator to base decisions on a wider range of clinical outcomes gathered over time. The information will help scientists analyze patterns of effective disease treatment and, more importantly, disease prevention in new ways, based on reviews of incremental clinical patient data over time.
• Solution and algorithm development, validation and deployment: The resulting AI-based solutions will provide proven treatment paths based on years of patient outcomes, representing the next generation of proactive and predictive medicine that can be used by care providers around the world.
