AI for Health Care Leaders

6 weeks | Live online

Module 1: State of the Industry: The Current AI Landscape in Health Care

Focus: Understand where AI adoption in health care actually stands, and what past technology cycles predict about this one.

Key Topics: History of machine learning and AI in health care; instructive analogs from past technology transitions including EHRs and genomics; cross-industry lessons from finance, manufacturing, and retail; current high-value use cases; the hype cycle and where promises diverge from results; core vocabulary and mental models for non-technical leaders

Module 2: Under the Hood: What AI Is and Is Not, for Clinical Risk

Focus: Build enough technical fluency to test a vendor's claims and judge whether an approach fits the clinical risk involved.

Key Topics: AI, machine learning, deep learning, and large language models distinguished; how LLMs work, intuition without equations; hallucination, bias, data dependency, and brittleness; what AI cannot do — reasoning gaps, causality, accountability; emerging capabilities including multimodal AI, agents, and reasoning models; testing vendor claims against technical reality; the questions to ask before commitment

Module 3: Governance, Oversight, and Risk

Focus: Decide who owns AI accountability in your organization and build the oversight to support it.

Key Topics: Centralized, federated, and hybrid governance models; Chief AI Officer versus embedding AI in existing functions; IT versus clinical ownership; cross-functional AI committees including clinical, legal, compliance, and patient and community voice; the internal resources and expertise a health system needs to oversee AI; privacy and HIPAA in the age of LLMs; cybersecurity risks specific to AI systems; FDA, CMS, state law, and emerging federal frameworks; liability, ethics, bias, and equity in clinical AI

Module 4: Strategy, Prioritization, and Partnerships

Focus: Prioritize AI initiatives against organizational goals, and decide whether to build, buy, or partner.

Key Topics: What a real AI strategy looks like versus a vision statement; problem-driven prioritization across back office, revenue cycle, clinical operations, and patient experience; roadmaps with milestones and dependencies; communicating to boards, clinical staff, and community partners; build versus buy versus partner by organizational maturity; evaluating AI vendors beyond the demo; contracts, IP ownership, and health data rights; co-development models, investment and venture approaches, and avoiding lock-in

Module 5: Making It Work: Implementation, Adoption, and Measurement

Focus: Structure a rollout clinicians will adopt, and measure whether it created value.

Key Topics: Why most AI implementations fail and how to prevent it; implementation science applied to AI; human-centered design and co-design with clinicians and staff; change management for AI, addressing fear and building trust; measuring what matters across clinical, operational, financial, equity, and staff-experience dimensions; case deep dive on ambient documentation and lessons from real deployments

Module 6: Looking Forward: Workforce, Literacy, and Leadership

Focus: Prepare the organization for what comes next, and present your capstone.

Key Topics: Emerging AI capabilities and their likely health care applications over the next several years; workforce transformation including augmentation, displacement, and reskilling; building organizational AI literacy at every level; the leader's role in vision, culture, and sustained organizational learning; capstone presentations and peer feedback

The cross-domain cohort continues with sessions on leading an organization through an AI transition, with topics including governance and accountability, aligning boards and stakeholders, and the judgment calls that recur regardless of sector. Working through these questions alongside a general counsel or a CFO is the point - most of the people who will challenge your AI proposal at home do not work in health care.

Participants return to their health care cohort to present their capstones and defend their recommendations before faculty and peers, closing with structured feedback and discussion of what it takes to move the proposal forward at home.

The program lasts 24 weeks and includes a three-day in-person symposium in Chicago.

The program fee is US $15,000.

This program is designed for health care and AI leaders responsible for strategy, adoption, governance, and value creation. This includes senior executives, clinical leaders, academic and research leaders, executives across pharma, insurance, medical devices, and health IT, as well as innovation, strategy, AI, and functional leaders. It is also relevant for senior consultants and advisors guiding AI strategy, operating model redesign, governance, and responsible adoption.

A minimum of 10 years of relevant work experience is required.

You will learn to evaluate AI capabilities, prioritize high-value initiatives, build business cases, and develop a phased enterprise AI roadmap. You will also assess vendors and partnerships, establish governance, manage health care-specific risks, align stakeholders, and measure clinical, operational, financial, and equity outcomes.

Upon successful completion of the program, you will receive a certificate from The University of Chicago and a digital badge that can be shared on LinkedIn or added to a professional portfolio.

The three-day symposium in Chicago combines frontier AI insights, cross-industry peer exchange, and executive decision-making. Participants explore governance and stakeholder alignment before presenting and defending their capstone recommendations in a boardroom simulation.

Complete the program application form to begin the admissions process. Additional application and enrollment requirements will be provided by the admissions team.

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