June 12, 2026 - Data Foundations in Healthcare
Goal: Understand the data that powers AI—and its limitations
- Types of healthcare data
- EHR data (structured vs unstructured)
- Imaging, waveform, genomics, claims, SDOH
- Data quality issues (missingness, bias, noise)
- Training data vs real-world data
- Annotation and labeling in clinical datasets
- Why “more data” is not always better
Key takeaway: Data quality determines AI performance
Target Audience
This activity is intended for physicians, nurses, quality & safety professionals, healthcare administrators & pharmacists.
Learning Objectives
Upon completion of this activity, participants will be able to:
- Develop a shared AI vocabulary and conceptual foundation
- Describe the data that powers AI systems and identify their limitations
- Relate core AI concepts to real‑world clinical and operational workflows
- Describe the value of AI within and beyond direct clinical care
- Critically assess AI tools for appropriateness, performance, and risk
- Apply principles that ensure safe and equitable use of AI
- Integrate appropriate trust in, and adoption of, AI-enabled tools
- Identify rapidly emerging AI tools that clinicians are already using
- Measure the impact and value of AI tools in healthcare settings
- Integrate AI use with standards of clinical professionalism
Additional Information
Center for AI and Biomedical Informatics in a Learning Health System (CAIBILS) & Mass General Brigham

Course Co-Directors
David W. Bates, MD, MSc
Director, Center for Artificial Intelligence and Bioinformatics in the Learning Healthcare System (CAIBILS), Mass General Brigham
Professor of Medicine, Harvard Medical School
Hossein Estiri, PhD
Investigator, Massachusetts General Hospital
Associate Professor of Medicine, Harvard Medical School
Li Zhou, MD, PhD
Director, MTERMS Lab, Mass General Brigham
Lead Investigator, Brigham and Women’s Hospital
Professor of Medicine, Harvard Medical School
Speakers
Shawn Murphy, MD, PhD
Chief Research Information Officer, School of Medicine, University of Washington
Director, Institute of Translational Health Sciences, University of Washington
Professor of Biomedical Informatics and Medical Education, Neurology, University of Washington
Liqin Wang, PhD
Assistant Professor, Brigham and Women's Hospital & Harvard Medical School
In support of improving patient care, Mass General Brigham is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.
Credit Designation Statements
AMA PRA Category 1 CreditsTM
Mass General Brigham designates this live activity for a maximum of 26.00 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Nursing
Mass General Brigham designates this activity for 26.00 ANCC contact hours. Nurses should only claim credit commensurate with the extent of their participation in the activity.
Pharmacy
This activity provides 26.00 contact hours (26.00 CEUs) of continuing education credit. ACPE Universal Activity Number (UAN): JA0007437-0000-26-008-L99-P

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