Using Artificial Intelligence to Empower Collaborative Team Workflows
Instead of treating AI as the answer, see how it empowers cross‑disciplinary teams and clinical support services to deliver patient‑centered care. Using cases in advance care planning and early deterioration detection, you will identify key principles, integrate tools with workflows, build effective AI‑enabled teams, analyze barriers and enablers, and apply lessons to design scalable, sustainable improvements.
Availability
On-Demand
Expires on Apr 30, 2027
Cost
Member: $0.00
Non-Member: $55.00
Credit Offered
1 CME Credit
1 ABIM-MOC Point
1 Participation Credit
  • Overview
  • Faculty
  • Accreditation
  • Recommended
Learning Objectives
After completing this activity, learners should be able to:
  1. Understand the Principles and Framework for AI Integration in Health Care: Participants will learn to identify and articulate the framework for designing and integrating AI into complex health care systems, understanding the shared principles applied across different use cases in care delivery. This includes recognizing the importance of viewing AI as an enabler within a larger ecosystem of digital applications, workflows, and human teams.
  2. Develop Skills for Building and Executing AI-Enabled Teams and Workflows: Participants will acquire the knowledge to identify the necessary components and steps to build cross-functional teams for AI implementation in health care. This involves gaining insights into the roles and expertise required-from ML and data science to clinical operations-and understanding how to execute AI integration in a sustainable and scalable manner, with a focus on user-centered design and team-based workflows for patient care.
  3. Analyze and Apply Lessons from Real-World AI Implementations in Health Care Settings: Participants will be able to analyze real-world case studies where AI was integrated into care delivery for advance care planning and appropriate care escalation. They will learn to assess the barriers, facilitators, and emergent characteristics of AI implementations, such as how AI enables new collaborative workflows, and apply these insights to facilitate AI-driven improvement in patient-centered care within their own health care enterprises.
Faculty
  • Lisa Shieh
Faculty Disclosures (PDF)

Faculty Disclosures
The individuals in control of content for this activity have no relevant relationships with ACCME-defined ineligible companies to disclose unless listed here. Any relevant relationships were mitigated prior to the start of this activity.

 
Accreditation Statement
The Society of Hospital Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

CME Credit Statement
The Society of Hospital Medicine designates this enduring material for a maximum of 1.00 AMA PRA Category 1 Credit(s)TM. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

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