Nursing Conferences

“My work began with a frustration familiar to many nurses: documenting care in the EHR, clicking through boxes, and feeling that nursing thinking disappeared into a “black hole” rather than becoming visible knowledge. A defining question has shaped my career: why do health systems collect so much nursing data but rarely use it to understand nursing care, improve outcomes, or represent nursing’s contribution in big data research? This question continues to drive my work to transform nursing documentation from a burden into a source of clinical insight, innovation, and professional visibility.”

“This talk addresses one of the most urgent challenges in nursing informatics: nursing care is extensively documented, but much of it remains invisible, fragmented, and underused in EHRs, research, quality measurement, and AI systems. While healthcare data science often relies on diagnoses, lab values, and medications, the rich record of nursing thinking (nursing diagnoses, interventions, goals, outcomes, priorities, and clinical judgment) is rarely used at scale. This topic is especially important now because AI and large language models are rapidly being integrated into healthcare. If nursing knowledge is not standardized, computable, and represented in these systems, nurses risk being further marginalized in the next generation of digital health innovation. This talk is relevant to nurses, informaticians, educators, researchers, terminology developers, health system leaders, and anyone interested in ensuring that AI reflects the full scope and value of nursing care.”

“Attendees will leave with a clearer understanding of why standardized nursing language is not just a documentation requirement, but a foundational infrastructure for AI, interoperability, and global nursing visibility. They can expect three major takeaways:
1. A new way to think about standardized nursing language: as the knowledge layer that allows nursing care to become computable, comparable, and usable in AI-driven healthcare.
2. A practical example of AI-assisted terminology mapping: how large language models and machine learning can help bridge local EHR terms with standardized nursing terminologies while still requiring nursing expertise, validation, and governance.
3. A future-oriented vision for nurse-centered AI: how tools like Moxie can reduce documentation burden, support care coordination, and translate nursing thinking into structured, usable data at the point of care.

A surprising insight from the talk is that the biggest opportunity for AI in nursing may not be replacing nursing judgment, but finally making nursing judgment visible. The audience will be challenged to move beyond asking, “How can nurses use AI?” and instead ask, “How can nursing knowledge shape the future of AI?””