Innovative Joint Program in Computational Precision Health at UC Berkeley and UCSF Aims to Improve Quality and Equity of Health Care
The University of California, Berkeley and the UC San Francisco on Oct. 20 jointly launched a new, one-of-a-kind program in computational precision health, a significant step toward advancing this new field and, ultimately, improving the quality and equity of health care.
The partnership positions the two world-renowned universities at the forefront in creating a new field at the intersection of medicine, statistics and computation. By creating a joint faculty group between UC Berkeley and UCSF, the two universities will simultaneously advance computing and data science with biomedicine and health, enabling solutions that would not have been imagined by either discipline alone.
The research of the joint faculty will enable new and more personalized techniques for the prevention, diagnosis, treatment and management of disease. Working together, the two universities will develop new methods for data-driven clinical care, early detection and intervention, new ways to predict outcomes, and new targeted treatments which are both more effective and have fewer side effects. This ground-breaking faculty and educational program will transcend traditional boundaries among institutions, disciplines and scholarly communities to transform the future of health and health care.
The program will train the next generation of researchers to design, build and test innovations such as machine learning, digital health and clinical decision support systems within real-world clinical and public health settings to ensure that solutions meet real-world needs. Recognizing that algorithms are currently being created that too often exacerbate rather than mitigate racial and other biases, the program will ensure that equity, fairness, accountability and transparency will be hallmarks of all of its educational and research activities.
A PhD degree program is anticipated by 2023 that will be jointly conferred by UC Berkeley and UCSF, leveraging the institutions’ research strengths: computer science and statistics at Berkeley, health care and health sciences at UCSF, and population and public health sciences at both campuses.
Many patients and patient populations face risks to their health from the use of data that are incomplete, data systems and infrastructure that are not well connected, and inherent bias.
Ida Sim, MD, PhDUCSF Professor of Medicine and Co-Director of Computational Precision Health program
A gift of $50 million has been provided to UCSF and Berkeley by an anonymous donor to support the international search and hiring of four new faculty members in the next year and initiate development of the graduate program. The two institutions have committed to collectively secure an additional $100 million in funding from other sources such as philanthropy, federal grants and industry partnerships.
The joint program is being led by co-directors from both campuses who have expertise in medicine, public health and data science: Ida Sim MD, PhD, professor of medicine at UCSF and Maya Petersen MD, PhD, associate professor of biostatistics and epidemiology at Berkeley.
Maya Petersen, MD, PhD. Photo by UC Berkeley
“The current evidence-based approach in medicine has led to health interventions and treatments that work – on average – for selected and often non-representative patients,” said Sim, a primary care physician and informatics researcher. “Many patients and patient populations face risks to their health from the use of data that are incomplete, data systems and infrastructure that are not well connected, and inherent bias in how data are collected and analyzed.”
“We can work to address these issues by equipping our students to understand the underlying contexts of equitable health care while simultaneously developing computational solutions to help instead of harm,” she said.
Petersen and Sim believe that advanced data analytics and digital infrastructures are needed to allow next-generation computational health solutions to learn from every patient intervention and enable every patient to benefit from the latest data and evidence on successful health interventions. This vision can only be achieved, they say, if the underlying computational and analytic tools are conceived, tested and validated for the health care needs of diverse individuals and communities.
“For an example of why this is needed, just look at the COVID-19 response in the U.S. and globally,” said Petersen, who developed mathematical models for San Francisco and Bay Area communities that helped predict cases and hospitalizations during the pandemic. “There’s so much more we can do to deliver on our promise and improve our healthcare and public health systems. We must serve the needs of populations and communities as well as individuals, and work to address existing systemic inequities in health.”
“Through this program, we aim to create a pipeline of diverse leaders committed to this goal, including those communities most impacted by existing health inequities. Embedded in all core courses in our curriculum will be a focus on diversity, equity and inclusion – asking who are solutions built for, and who are solutions built by – human-centered design, and the consideration of the ethical implications and societal impacts of this work,” Petersen said.
Shared administration of the program by Berkeley’s Division of Computing, Data Science, and Society and UCSF’s Bakar Computational Health Sciences Institute, along with the involvement of 39 faculty in multiple schools and departments at both UC campuses, will help facilitate additional education and research collaborations in computational precision health.