AI Program for Professionals in Health Care Systems

The program is delivered by Harvard Chan faculty and health system and AI practitioners. Rifat Atun serves as Dean for Education and Innovation, Harvard T.H. Chan School of Public Health, while the faculty and industry practitioner group includes experts from Massachusetts General Hospital, Harvard Medical School, Boston Children’s Hospital, Heim Health, Ferrum Health, Akido Labs, Qure.ai, and the Coalition for Health AI.

To determine which type of AI applies to your role, first identify the health care challenge you want to address and then align it with the right AI approach. The program’s “Families of AI” framework helps you develop the systems-level judgment to make this distinction.
● Predictive AI: Best for roles focused on capacity planning, workforce management, and scheduling. It uses data to forecast demand and optimize resources.
● Generative AI: Useful for clinical operations, reducing administrative work, and patient communication. It can summarize information, draft clinical notes, and automate routine documentation.
● Agentic AI: Suited to innovation, informatics, and systems leadership where complex, multi-step workflows need to be coordinated across multiple AI capabilities with human oversight.
Ultimately, the program helps you evaluate which AI approach can create the most value for your specific health care challenge using Harvard’s High-Value Health System framework.

Examples include predictive staffing and demand forecasting, generative AI for clinical documentation, medical imaging, care coordination, clinical workflow orchestration, unified patient-data platforms, telehealth, AI-enabled care delivery, and health system governance.

Yes. The program covers trustworthy AI, governance, and equity oversight, including clinical safety and accuracy, explainable AI, algorithmic bias mitigation, risk categorization using the CHAI Risk Categorization Tool, responsible data management, privacy, security, and adverse-event reporting. You will also explore how these considerations inform the governance of AI in health care systems.

Yes. Throughout the program, you will apply rigorous deployment and governance frameworks to real-world health system cases.
Additionally, you will complete a module-by-module Capstone Project where you design an implementation-ready AI solution for a real-world health-system scenario. This project helps you integrate technical, ethical, and operational factors into a final proposal ready to present directly to executive decision-makers

The program is designed specifically for health care leaders, clinical executives, and decision-makers responsible for adopting, deploying, and scaling AI across health systems. It is highly relevant to:

• Health Care Administrators and Program Managers
• Health System, Clinical, and Payer Leaders
• Health Care Consultants
• Health Informatics, Analytics, and Innovation Leaders
• Public, Population, and Government Health Professionals

Applications are submitted through an online application form. Eligible applications are reviewed by a panel from Great Learning, and selected candidates receive an offer letter. Applications are rolling and close once the cohort is full.

The program fee is $3,200.

Yes. Participants can contact a Program Advisor from Great Learning for information about flexible payment plans.

The program helps participants evaluate potential AI investments by framing health system problems, assessing evidence and risks, considering operational and ethical factors, and evaluating the value an AI solution could create.

You will develop skills in AI problem framing, AI method selection, health system deployment, workflow integration, scaling, responsible AI governance, safety and equity assessment, post-deployment monitoring, and communicating an AI solution through a clear problem definition.

This framework serves as the analytical anchor of the entire curriculum. Rather than adopting AI simply because the technology is advanced, you will learn to evaluate any AI solution against four core health-system objectives: ○ Effectiveness: Does the AI improve clinical quality and health outcomes?
○ Efficiency: Does it optimize resource management and lower the cost of delivery?
○ Equity: Does it expand access to underserved populations and narrow care gaps?
○ Responsiveness: Does it improve user satisfaction and care coordination?
This systems-first approach helps participants evaluate AI based on measurable operational value rather than simply choosing the most advanced technology.

Choosing the right AI solution starts with the health care problem, not the technology. The program uses Harvard’s High-Value Health System framework to guide this process:
● Define the problem: Identify where your health care operation is underperforming across functions, outputs, or outcomes.
● Measure the value gap: Evaluate the challenge based on effectiveness, efficiency, equity, and responsiveness.
● Assess evidence and risks: Consider the available evidence, implementation feasibility, and risks such as algorithmic bias.
● Match the AI approach: Align the need with the appropriate AI family, predictive AI for forecasting and resource planning, Generative AI for documentation and administrative tasks, Deep Learning for diagnostics and imaging, or Agentic AI for complex, multi-step workflows.
This systems-first approach helps ensure AI is selected based on measurable operational value, rather than simply choosing the most advanced technology.

The program can support professionals working in health care administration, health system leadership, clinical leadership, payer operations, health informatics, analytics, innovation, health care consulting, and public or population health. It is designed to build system-level AI judgment for professionals involved in AI adoption, deployment, scaling, and governance.