- Online
- June 9 – 12, 2025
- $2,600
Implementing Health Care AI into Clinical Practice
Overview
Equipping Stakeholders to Implement Successful AI Solutions in Health Care
Medicine, at its core, is an information processing science. The more patient data we have, the clearer and easier it is to define health outcomes. As AI technology grows, its use in health care settings can accelerate information processing to improve patient care efficiency. However, integrating AI into a provider’s practice or health care system is a large undertaking. Implementing AI in Clinical Practice equips clinicians and stakeholders with the necessary and timely skills to develop these processes in-house.
Take the Next Step in Your Career Evolution with the
Business Applications for AI in Health Care Certificate of Specialization
Implementing Health Care AI Into Clinical Practice contributes to the Business Applications for AI in Health Care Certificate of Specialization, designed for all health care stakeholders, from the doctor’s office to the business suite. To obtain your Certificate, choose two of three online programs created and taught by Harvard Faculty and pioneers of AI in health care to prepare you for the future of an ever-changing health care industry.
For more information, visit our Business Applications for AI in Health Care Certificate of Specialization page.
Through case studies, small group discussions, and interactive sessions led by Harvard faculty, you will gain hands-on experience in each step of the AI implementation process. You will learn how to design an effective implementation strategy by building your skills in four key areas:
- Workflow assessment and system engineering, in which you identify the tasks you need automated and decide the structured sequence of operations to do them
- Accuracy evaluation and model selection, or choosing the best AI or machine learning model for a specific task and test its effectiveness
- Machine Learning Operations, commonly known as MLOps, covers model deployment and long-term maintenance
- Change management for your health care setting, preparing the team and your systems for the move to an AI-led workflow
Through these lenses, participants will develop and tailor AI solutions that will work best for their health care setting and situation. By the end of the program, you will develop skills in multiple disciplines—including data science, user-centered design, and change management—to ensure AI solutions are implemented successfully in clinical practice.
This program builds upon AI for Health care: Concepts and Applications. Both courses can be taken independently; however, to earn the Business Applications for AI in Health Care Certificate of Specialization, you must take two of three programs in our health care AI portfolio. Without a strong background in AI, it is recommended that you take Responsible AI for Health Care: Concepts and Application before either Implementing Health Care AI into Clinical Practice or Innovation with AI in Health Care, as Responsible AI for Health Care: Concepts and Application covers more foundational concepts upon which the other two programs build.
Objectives & Highlights
Learning Objectives
- Analyze clinical workflows with a focus on the clinical decision that can be improved with AI, and design new AI-enhanced workflows
- Understand the nuances of different accuracy metrics to assess the performance of different AI models for specific use cases
- Grasp the concepts of MLOps and machine learning model deployment
- Identify the potential for model drift and how to account for it in a model maintenance plan
- Plan and conduct change management for AI-powered process changes
- Identify what team members—data engineers and scientists, MLOps specialists, and others—you need to fully implement a clinical AI project
Program Highlights
- Hear from experts in the implementation of AI into clinical practice, from health care institutions to health care tech CEOs
- Work through a real-world case study with the guidance of experts in the implementation of AI solutions into clinical practice
- Network with like-minded leaders from across the world through small group discussions in an interactive virtual environment.
Upon completion of this course, participants will be able to:
- Analyze a health care problem that may be solved with AI by using design principles
- Describe the different stages of AI development and implementation, and what skills are needed to successfully advance the project
- Identify necessary professional skills required in a successful health care AI team
- Guide a multidisciplinary team through the process of developing and implementing an AI solution into clinical practice
- Design a plan that includes the different elements of an AI health care project: workflow analysis, data and modeling, deployment and change management
Credits and Logistics
Continuing Education Credit
The Harvard T.H. Chan School of Public Health is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The Harvard T.H. Chan School of Public Health designates this live activity for a maximum of 16 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
The American Medical Association (AMA) has an agreement of mutual recognition of continuing medical education (CME) credit with the European Union of Medical Specialties (UEMS). Additional information regarding this agreement may be found on the American Medical Association (AMA) website.
Harvard T.H. Chan School of Public Health will grant 1.6 Continuing Education Units (CEUs) for this program, equivalent to 16 contact hours of education. Participants can apply these contact hours toward other professional education accrediting organizations.
All credits subject to final agenda.
Successful completion of this CME activity, which includes participation in the evaluation component, enables the participant to earn up to 15 MOC points in the American Board of Internal Medicine’s (ABIM) Maintenance of Certification (MOC) program. It is the CME activity provider’s responsibility to submit participant completion information to ACCME for the purpose of granting ABIM MOC credit.
Faculty
Current faculty, subject to change.
Santiago Romero-Brufau, MD, PhD
Program Director
June 9 – 12, 2025Department of Otolaryngology — Head and Neck Surgery
Mayo Clinic
Adjunct Assistant Professor
Department of Biostatistics
Harvard T.H. Chan School of Public Health
Peter Noseworthy
Faculty
June 9 – 12, 2025Division of Heart Rhythm Services
Mayo Clinic- Rochester
Joshua Wesley Ohde, PhD
Faculty
June 9 – 12, 2025Center for Digital Health, AI Enablement
Mayo Clinic- Rochester
Alexander Jay Ryu, MD
Faculty
June 9 – 12, 2025Department of Medicine and Innovation Committee Chair
Mayo Clinic- Rochester
John Guy Skiffington, BS
Faculty
June 9 – 12, 2025Mayo Clinic Center for Digital Health
Mayo Clinic- Rochester
Agenda
June 9 – 12, 2025
All Times are Eastern Time (ET).
Monday, June 9, 2025 | ||
---|---|---|
9:00–10:15 am | Introduction - Health Care AI as the Science of Decisions | |
10:15–10:30 am | Break | |
10:30–11:45 am | Workflow Mapping | |
11:45 am–12:30 pm | Lunch | |
12:30–2:00 pm | Guided Team Project: Workflow Map | Tuesday, June 10, 2025 |
9:00–10:15 am | Accuracy Metrics | |
10:15–10:30 am | Break | |
10:30–11:45 am | Model Selection | |
11:45 am–12:30 pm | Lunch | |
12:30–2:00 pm | Guided Team Project: Accuracy Metrics and Model Discussion | Wednesday, June 11, 2025 |
9:00–10:15 am | ML-Ops and Architecture Design | |
10:15–10:30 am | Break | |
10:30–11:45 am | Case study: Heart Rhythm AI | |
11:45 am–12:30 pm | Lunch | |
12:30–2:00 pm | Guided Team Project: ML-Ops and Model Maintenance | Thursday, June 12, 2025 |
9:00–10:15 am | Study Design and Change Management | |
10:15–10:30 am | Break | |
10:30–11:45 am | Guided Team Project: Change Management Plan and Study Design | |
11:45 am–12:30 pm | Lunch | |
12:30–2:00 pm | Course Summary and Q & A |
This agenda is subject to change.
back to topWho Should Participate
This online program is designed for clinicians, administrators, and executives who are responsible for implementing AI solutions in their organization.
Participants will come from a range of organizations including health care delivery (hospitals, clinics, primary care systems, and other health care delivery companies or institutions), health care technology, payers, and governments. Some titles represented in the program will include:
- Clinician
- Physician Leader
- Chief Executive Officer
- Chief Information Officer
- Chief Innovation Officer
- Chief Medical Informatics Officer
- Chief Medical Officer
- Data Scientist
- Director
- Innovation Specialist
- Implementation Specialist
- Product Manager
This program is designed for those who have either a background in data science or a general understanding of how artificial intelligence works; as well as a general understanding of the health care system. It is recommended that those with no artificial intelligence background or knowledge take the first program in the AI Certificate of Specialization series, AI for Health Care: Concepts and Applications, before enrolling in this program.