UC Berkeley Center to Focus on Refining Healthcare Innovations

The University of California Berkeley has created a Center for Healthcare Marketplace Innovation to translate artificial intelligence and behavioral economics healthcare research into real-world advances in patient outcomes and reduced medical costs.

The center, announced by the College of Computing, Data Science and Society and the Haas School of Business, will be housed within the Institute for Business Innovation at Berkeley Haas.
The center will focus on three pillars: conducting research to advance the science of innovation incentives in healthcare; encouraging interdisciplinary collaboration on projects and solutions; and partnering with healthcare providers, insurers, government agencies and others to test and refine the novel interventions.

The center will soon begin piloting a new generative AI model that offers clinical coaching to medical professionals. “AI is going to be central to healthcare delivery in 10, 15 years from now,” said Jonathan Kolstad, Ph.D., an associate professor of economic analysis and policy at Berkeley’s business school, in a statement. “We’re at this inflection point. By understanding the technology, the systemic incentives and the human abilities in the healthcare system, we have a tremendous opportunity to help shape those dynamics.”

UC Berkeley said the center is close to signing multiple large-scale, multimodal data access agreements with healthcare partners. The data is typically tightly held, and it can take years for academics to access it, said Ziad Obermeyer, M.D., an associate professor and Blue Cross of California Distinguished Professor at the Berkeley School of Public Health, in a statement. “That limits what research can be done to tackle health problems and the usefulness of related AI, which is only as good as the data it has access to train on, he said. Making it easier to access that data – and keeping it secure and used ethically – will unleash possibilities for research and impact in computational health.”

The center is also setting up an industry feedback platform, where large healthcare providers and others can share with researchers what problems they’re trying to solve for their patients, clinicians and systems. This input could lead to research and provide on-the-ground insights to inform the center’s efforts.

Obermeyer’s work offers a blueprint of what the center’s impact could look like in practice. Through his research, Obermeyer found there was a need to improve physicians’ diagnoses of a patient’s probability of heart attack, an action that can trigger tests and other urgent care. Working with a major healthcare system, he developed an algorithm that could support doctors in emergency rooms as they screen patients and make crucial life or death decisions.
He’s now conducting randomized trials to see if the machine learning method he developed for an academic paper can become a real-world medical solution used in emergency rooms.

“There’s a lot of really cool computational stuff happening, but it’s being built with very little understanding of the actual function of the healthcare system – of the complicated incentives of what it would take to have an algorithm, a prediction model, a solution be deployed to really change either healthcare outcomes or costs,” added Kolstad. “This kind of center that works to bridge these mechanisms can be very, very influential.”