Research
Computational Precision Health develops new methods in computer science, machine learning and AI, statistics, data science, and health informatics to enable and evaluate new health interventions for real-world impact.
Computational Precision Health is a new paradigm for developing and deploying adaptive precision interventions for real-world impact. The science of CPH is the development and use of novel computational algorithms, tools, and architectures to increase precision in problem formulation, develop precision solutions that adapt to person, place, time, and context, and deploy and evaluate these solutions more efficiently and effectively.
Precision Problem Formulation: New computational methods in causal inference enable us to ask deeper and more precise questions than ever before. We can do more than predict. We can begin to understand why things happen, how to intervene, and when to change our actions. We can formulate problems that capture the complexity of the real world, including the reality that patients have more than one disease at a time, and that health care systems are highly complex organizations that often function within tight resource constraints, structural biases, and perverse incentives.
CPH faculty include practicing clinicians and leaders in health care operations and policy, ensuring that problem formulation is appropriately scoped and grounded in the real world.
Computational Precision Solutions: CPH solutions emerge from iterative cycles of problem formulation, development and testing of computational approaches, and real-world evaluation of prototype solutions. Solutions aim to augment human intelligence – whether of clinicians, patients, family caregivers, or public health practitioners – to deliver personalized interventions that incorporate uncertainty, time, and context.
CPH is building open software architectures and partnering with designated Computational Health in Practice (CHiP) sites at UCSF Health and beyond to facilitate rapid iteration and testing of computational precision solutions.
Precision Deployment: Unlike a drug that never changes once developed and marketed, the very nature of computational solutions is that their underlying models and real-world performance will drift as data, decision, and valuation contexts change over time. Novel standards-based and privacy-preserving architectures and evaluation methods are needed to ensure that computational solutions continue to deliver fair and equitable outcomes and are demonstrably safe and effective over time in each and every setting.
CPH faculty are experts at novel approaches to evaluation design, including use of new causal inference methods to evaluate real-world evidence, and are leaders in regulatory science and policy for AI/ML and digital health.
Core Research Areas
The majority of CPH faculty research falls into one or more of the following core research areas.
See select recent publications by CPH faculty
Research Highlights
Identifying Reasons for Contraceptive Switching from Real-World Data Using Large Language Models
Many factors related to medication switching are only captured in unstructured clinical notes and can be difficult to extract. Dr. Irene Chen and colleagues evaluated the abilities of GPT-4, with no previous training, to identify reasons women changed contraceptives, demonstrating that GPT-4 could accurately extract reasons for switching.
AI can Fight Racial Bias
Dr. Ziad Obermeyer discovers that by training an algorithm to listen to patients—not doctors—researchers can diagnose and fix racial biases built into medical knowledge.
Optimizing risk-based breast cancer screening policies with reinforcement learning
Dr. Adam Yala et al developed Tempo, a reinforcement-learning based framework for deriving personalized screening policies. Tempo policies were significantly more efficient than existing clinical guidelines across diverse test sets from four hospitals.
