AI is already shaping what information reaches nurses in practice through risk scores, deterioration alerts, reordered patient lists, and documentation tools that often appear without explanation.
This course helps nurses see the structure behind every AI tool in their workflow, the inputs it draws from, the model doing the analysis, the tool that displays the result, and the output it produces. Through the four-bucket framework, flagged, summarized, prioritized, triggered, and guided Dialogue activities, learners will practice naming the AI tools already present in their own practice, telling rule-based systems apart from machine learning, distinguishing predictive AI from generative AI, and asking the governance questions that determine who's accountable when something doesn't seem right. 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). 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. 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. Please note the rosters are collected quarterly.
