Tracks

Jenna Marquard, PhD, FACMI, Professor, University of Minnesota School of Nursing

Ryannon K. Frederick, M.S., R.N., CENP, FAAN, Chief Nursing Officer for the Department of Nursing at Mayo Clinic

This workshop is grounded in the globally recognized standard, "Empowering Learners for the Age of AI: An AI Literacy Framework for Primary and Secondary Education (AILit Framework)," which is a joint initiative of the European Commission and the Organization for Economic Cooperation and Development (OECD), developed with support from Code.org and leading international experts.  Our objective is to translate this international standard into "Empowering Nurses for the Age of AI: An AI Literacy Framework for Nursing Education and Practice."

To ensure a shared understanding, we ground our work in the following definitions:

• General Context: Long and Magerko (2020) define AI literacy as “a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace.”
• The AILit Framework Approach: AI literacy represents the technical knowledge, durable skills, and future-ready attitudes required to thrive in a world influenced by AI. It enables learners to engage, create with, manage, and design AI, while critically evaluating its benefits, risks, and ethical implications.
• Nursing Context: Hoelscher and Pugh (2025) state, “AI literacy in health care encompasses the foundational knowledge, technical competencies, and ethical awareness required to understand, critically evaluate, and effectively apply AI technologies in clinical and educational contexts.”

We will work through the AILit Framework in small groups to adapt its Four Domains to the nursing profession:

1. Engage: Adapting "societal and ethical impacts" to patient advocacy and clinical ethics.
2. Create: Translating "co-creating with AI" to clinical documentation and care planning.
3. Manage: Defining "delegating tasks" within the scope of clinical judgment and safety.
4. Design: Applying "conceptualizing solutions" to workflow integration and tool selection.

While our primary template is the AILit Framework, we will cross-reference our work with parallel nursing-specific initiatives, specifically N.U.R.S.E.S. embracing artificial intelligence: A guide to artificial intelligence literacy for the nursing profession (2025), to ensure our adaptations align with current clinical standards.

Cathy Ivory, PhD, NI-BC, NEA-BC, FAAN, FAMIA, NKBDS Steering Committee and eRepository Liaison; Associate Nurse Executive, Vanderbilt University Medical Center, Nashville, TN

This session will explain the history, structure, and current key priorities of the Nursing Knowledge Big Data Science (NKBDS) initiative. Designed especially for first‑time attendees, the session will help orient participants to the conference community, culture, and shared body of work. Attendees will learn how to get involved, who to connect with, and where to find NKBDS resources, including a live demonstration of the eRepository where work products and outputs are housed. Time will also be devoted to helping new participants feel welcomed, ask questions, and begin building relationships that can support ongoing engagement beyond the conference.

Kelly Landsman MN, BME, BS, RN, PHN, Founder and Principal Nurse Engineer, Landsman Engineering LLC

Nurses are already driving innovation every day — and now, with data, technology, and ecosystem resources at our fingertips, we have what we need to meet the moment. In this 2.5-hour hands-on interactive session, participants will come together to break down silos and learn how to turn methods and strategies written on paper into real-world action in care delivery. After this session, audience may expect to learn: 1) Practical strategies to connect across the healthcare ecosystem and drive meaningful systems transformation; 2) How to access to valuable resources to support your innovation efforts; 3) Opportunities to meet and collaborate with like-minded partners; 4) Techniques to amplify your voice and achieve system-level impact; and 5) New connections and relationships that enhance your ability to meet the moment in today’s evolving healthcare landscape.

Susan Matney, PhD, RNC-OB, FAAN, FACMI, FHIMSS, FHL7, AL2

Laura Heermann Langford, PhD, RN, FAMIA, FHL7, Associate CNIO, Veteran’s Health Administration

As healthcare delivery becomes increasingly digital, the ability to exchange, interpret, and reuse nursing data across systems is essential to safe, coordinated, person-centered care. HL7® Fast Healthcare Interoperability Resources (FHIR®) has emerged as a foundational standard for interoperable health information exchange with growing relevance to nursing documentation, decision support, and care planning. This presentation offers a practical, resource-level walk-through of how FHIR supports the nursing process using U.S. Core FHIR profiles, standardized terminologies, and live demonstrations.

A brief orientation introduces FHIR architecture, resource structure, profiles, and RESTful exchange patterns, emphasizing why nurses and nurse informaticists benefit from understanding terminology bindings and data models. Each phase of the nursing process is then mapped to key FHIR resources and U.S. Core profiles, including Observation (simple and screening assessment) and Questionnaire/QuestionnaireResponse for assessments and instruments; Condition for nursing diagnoses; Procedure for interventions; Goal and Observation for outcomes and evaluation; and CarePlan for longitudinal, patient-centered coordination of care. Integration of LOINC®, SNOMED CT®, and ICNP-to-SNOMED reference sets illustrates how nursing content can be encoded as computable, standards-based data.

Real-world scenarios—such as pain assessment and medication-related allergy events—demonstrate end-to-end representation of nursing data as interoperable FHIR resources, including grouped observations, questionnaire rendering, and example JSON outputs using publicly available tools. Implementation considerations are addressed, including variability in system adoption, limited native support for dynamic questionnaires, absence of national value sets for goals and outcomes, semantic complexity in Goal modeling, privacy and security concerns, and the need for greater nursing participation in standards development. Attendees leave with actionable strategies for selecting appropriate resources, applying terminology bindings, testing use cases, and engaging with standards communities to strengthen data quality and cross-setting care coordination.

Learning Objectives

1. Define FHIR® and explain its relevance to nursing practice.

2. Identify key FHIR resources and U.S. Core profiles aligned to the nursing process.

3. Illustrate integration of LOINC®, SNOMED CT®, and ICNP within FHIR resources.

4. Analyze real-world nursing use cases represented in interoperable FHIR formats.

5. Describe practical steps for nurses to participate in interoperability and standards efforts.

Keywords: Nursing Informatics; FHIR; Interoperability; Nursing Process; Care Planning; LOINC; SNOMED CT; US Core Profiles