HIMSS Assessing and Implementing AI and ML in Healthcare


The HIMSS Assessing and Implementing AI and ML in Healthcare Course is a self-paced, on-demand, e-learning course tailored for healthcare leaders and professionals, focusing on the rigorous evaluation and responsible implementation of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare. The course combines foundational AI and ML knowledge with hands-on practical examples, case studies, and interactive data modules. 

The rapid adoption of AI and ML in healthcare is outpacing the industry's ability to effectively and safely integrate these tools into clinical workflows. Many health systems are grappling with challenges related to external validation, "black box" model uncertainties, and disparities across patient subgroups. There's a critical need for structured training that empowers healthcare leaders to rigorously evaluate and responsibly implement AI/ML models, ensuring optimal patient outcomes and preserving trust. Given the potential risks to patient safety and the demonstrated discrepancies between some models' claims and their real-world performance, addressing this gap is both urgent and essential. This course addresses this pressing need, providing a structured, comprehensive, and hands-on guide to assess AI/ML models responsibly. By equipping you with the right skills and tools, we're not just ensuring optimal patient outcomes but also helping healthcare systems avoid potential pitfalls and liabilities.

This course is hosted on the HIMSS Litmos learning management system. Learners will have six months from the date of purchase to complete all course requirements. The approximate course time is ten hours. Upon successful completion, learners receive a certificate of completion and are eligible to claim ten HIMSS continuing education (CE) hours.

View a sample from this course

Course Learning Outcomes

At the conclusion of this course, learners will be able to

  • Explain the fundamental concepts of artificial intelligence and machine learning in health ecosystems.
  • Identify specific components of the Machine Learning Life Cycle Management Process (MLCM).
  • Understand the ethical implications and evaluation framework for data harmonization and the scoreboard for success.
  • Gain practical skills implementing AI/ML applications from use case development within a healthcare system.

Subject Matter Experts

Hear directly from these subject matter experts (and more) throughout the course.

  • Dr. Brian Anderson, Chief Executive Officer and Co-Founder, Coalition of Health AI
  • Sunil Dadlani, EVP, CIDO & CCSO, Atlantic Health System
  • Dr. Jeffrey Ehrenfeld, President, AMA
  • Dr. John D. Halamka, President, Mayo Clinic Platform
  • Rachini Moosavi Chief Analytics Officer, UNC Health
  • Dr. Aalpen Patel, Chair of Radiology and Medical Director for AI, Geisinger
  • Jeremy Petch, Director, Digital Health Innovation, Hamilton Health Services


  • HIMSS Member* | $425.00 USD
  • Non-Member | $510.00 USD

*excludes Chapter only and Digital only members

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Should you have questions regarding this course, please reach out to us at HIMSSprofessionaldevelopment@himss.org.