AI Interventions for Healthcare and Poli

Artificial intelligence is rapidly reshaping how health systems deliver care, allocate resources, communicate with patients, and make policy decisions. Yet the promise of AI in health care and policy remains largely unrealized because translating technical tools into effective, equitable, and deployable interventions requires skills that sit at the intersection of behavioral science, implementation science, and policy analysis.

This course is designed to develop students' capacity to critically evaluate, design, and advocate for AI-based interventions in health policy contexts. Rather than treating AI as a black box or a panacea, students will be invited to engage with it as a design object open to criticism, iterative (re)creation, and contextualization.  Importantly, this course does not ask whether or how a healthcare organization should adopt AI, or to identify AI implementation opportunities. While valuable questions, this course instead teaches students how to design, build, and critically interrogate AI as a behavioral intervention tool. Students will build working prototypes of conversational AI interventions using browser-based tools, requiring no prior coding experience, while developing the analytical frameworks needed to evaluate evidence, identify harms, and make the case for or against scaling an intervention in the real world.

By the end of this course, students will have designed and stress-tested an AI health intervention from behavioral diagnosis through prototype to policy brief. They will be equipped to engage as informed critics and practitioners in a health policy landscape increasingly shaped by algorithmic decision-making and AI driven patient engagement whether as administrators evaluating vendor tools, analysts advising on AI governance, or leaders responsible for deploying technology equitably across diverse populations.