Overview

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This course helps nurses integrate AI outputs with patient assessment, clinical context, patient goals, and professional judgment. Through the AI-SBAR framework and interactive clinical cases, learners will practice responding when AI outputs align with or conflict with bedside findings, reviewing generative AI content, communicating concerns, documenting clinical reasoning, and escalating safety issues appropriately. In support of improving patient care, Duke University Health System Clinical Education and Professional Development is accredited by the American Nurses Credentialing Center (ANCC), the Accreditation Council for Pharmacy Education (ACPE), and the Accreditation Council for Continuing Medical Education (ACCME), to provide continuing education for the health care team. The designation was based upon the quality of the educational activity and its compliance with the standards and policies of the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC). Duke University Health System Department of Clinical Education and Professional Development designates this activity for up to 9 credit hours for nurses. Nurses should claim only credit commensurate with the extent of their participation in this activity. Upon completion of all three courses (Seeing AI in Nursing Practice, Evaluating AI in Nursing Practice, and Applying AI in Nursing Practice) in the AI in Nursing Practice: Foundations for Quality and Safety Specialization, you will be eligible to apply for continuing education credits. Please note that continuing education credit rosters will be collected quarterly.

Syllabus

Applying AI-SBAR
- AI-SBAR is the framework used throughout this course. In this module, you’ll become familiar with the framework, connect it to the clinical reasoning you already use, and prepare to apply it in the case studies ahead.
When the AI Output and your Clinical Assessment Align
- Not every AI output will conflict with what you observe with your patient. Sometimes the tool and your clinical assessment both provide accurate information, but each highlights a different part of the patient’s situation. This module explores how to respond when an AI output complements and adds context to your clinical picture.
When the AI Output and the Clinical Picture Diverge
- These two cases share a common challenge: the AI tool suggests one thing, while your clinical observations suggest another. This module will help you consider why that gap may exist and practice responding with confidence when you encounter it.
When AI Surfaces Something Unexpected
- This case is different from the previous two. Your assessment is reassuring and the alert is the only thing raising a concern. This module practices taking an unexpected AI output seriously without overreacting, and using it as a prompt for focused reassessment.
From Assessment to Action
- This module shifts from internal reasoning to external action. Through a generative AI scenario in an outpatient telehealth setting, you'll practice what happens when your clinical judgment has to become visible in documentation, in communication with your care team, and in what you say to a patient.
Putting It All Together
- In this module, you'll close the course with a synthesis of what applied AI literacy means for nursing going forward.