By Tamara Macieira, PhD, RN
Artificial intelligence (AI) and digital technologies are entering health care faster than many health systems can adjust clinical workflows or prepare the people expected to use them. For nursing, this transformation raises questions that are both technical and deeply human: Is nursing knowledge adequately represented in the data powering AI? Do digital tools support clinical work or add another layer of burden? And how can innovation move forward without weakening judgment or patient-centered care?
These questions connected recent work by University of Florida College of Nursing faculty presented at the Medical Informatics Europe (MIE) 2026 Conference in Genoa, Italy and the 2026 Nursing Knowledge: Big Data Science Conference in Minneapolis. Although the projects addressed different clinical and technical problems, together they revealed three priorities shaping the future of nursing and AI: making nursing knowledge visible in data, designing technology around clinical work and keeping human judgment at the center of digital transformation.
Making Nursing Data Visible in Data
One of the most persistent barriers to using nursing data is the lack of standardization across health systems. Nurses document detailed information about patients’ problems, goals, responses to treatment and the nursing care provided, yet much of that knowledge remains difficult to compare, exchange or reuse.
One study examined if AI could help address this challenge. The authors evaluated a Retrieval-Augmented Generation system designed to map locally documented nursing problems, goals and interventions to standardized terminologies. The system was not accurate enough to replace expert review – but that is not the purpose. Its value was in narrowing the range of possible standardized terms, allowing experts to verify likely matches rather than manually searching entire terminology systems. This illustrates an important direction for clinical AI, where we use automation to reduce cognitive burden while preserving human oversight.
A related international opinion paper examined what is lost when nursing data remain underused. When nursing data are absent from interoperability frameworks, research databases and predictive models, health systems are working with an incomplete picture of patient care. Nursing’s contributions to care, staffing, quality and cost also remain difficult to measure.
Before health care can fully realize the promise of AI, it must first ensure that nursing knowledge is represented in the underlying data. An algorithm cannot learn from information that was never captured, standardized or made available. This raises an important question: What aspects of nursing care remain invisible in the data used to guide research, policy and clinical decisions?
Designing Technology Around Clinical Work
A second theme across the conferences was the gap between adopting technology and successfully integrating it into clinical practice.
Findings from a national survey of 752 registered nurses examining their experiences with digital health technologies during the COVID-19 pandemic showed that electronic health records (EHR) have the lowest usability ratings – a finding likely to resonate with many clinicians.
These findings remind us that a technology can be technically functional and still fail to support the people expected to use it. Usability is not simply about whether clinicians can click through a system. It includes whether the technology fits the sequence of clinical tasks, supports communication and decision-making and avoids creating unnecessary duplication or documentation burden.
The ESCAPE Framework was also presented. It is designed to evaluate health technologies both before and after integration with an EHR. This distinction matters because a tool that performs well as a stand-alone application may become frustrating, inefficient or even unsafe once incorporated into a complex clinical environment. By assessing usability across both stages, developers and health systems can identify problems before they become embedded in everyday practice.
The broader lesson is that nurses should not enter the process only after a system has already been purchased or deployed. They must participate in the design, procurement, implementation and evaluation.
Keeping Digital Transformation Human and Ethical
Digital transformation also raises questions that cannot be resolved through technical performance alone.
A multinational analysis of nurses’ perceptions about digital transformation during public health crises revealed recurring concerns about weakened patient-centered care, privacy and cybersecurity, fragmented infrastructure, documentation burden, inequitable access and inadequate organizational preparation. Participants recognized that technology could improve communication and continuity of care. At the same time, they cautioned that poorly implemented systems may reduce human interaction, limit professional autonomy and introduce new risks for patients and clinicians.
An AI-supported data architecture for organ-recipient matching brought these ethical questions into an especially high-stakes context. The proposed framework integrates clinical, behavioral, socioeconomic, lifestyle and logistical information to support transplantation decisions and longitudinal care. However, more data and more sophisticated algorithms do not eliminate uncertainty. They create new responsibilities related to fairness, accountability, explainability and the ethical use of deeply personal information.
When an AI recommendation influences care, who understands how it was produced? Who recognizes when it may be wrong? And who remains accountable for the decision?
Moving From Discussion to Responsible Action
No institution or profession can address these questions alone. The international workshop at MIE 2026, “Academic-Practice Partnerships to Advance AI in Healthcare: Global Perspectives,” brought together participants from multiple countries to examine how universities, health systems and clinicians can collaborate on responsible innovation.
The work shared across these conferences suggests that the next stage of digital transformation will not be defined only by more sophisticated algorithms. It will depend on whether health systems can build trustworthy and inclusive data, design technologies around real clinical workflows, prepare the workforce and preserve human judgment as automation expands.
For nurses, clinicians, and researchers, this creates both an opportunity and a responsibility. We must ask not only whether a technology works, but whose knowledge it represents, how it changes care and who has been included in developing it.
These conversations will continue at the 2027 Rita Kobb Nursing and Health Informatics Symposium, taking place Jan. 29 at the University of Florida. The symposium will explore how nurses can shape and lead responsible innovation, guide implementation, strengthen organizational readiness and address barriers within health systems for meaningful technology adoption.
Celebrating Informatics Leadership
We also congratulate Drs. Tamara Macieira and Hwayoung Cho on their induction as Fellows of the American Medical Informatics Association (FAMIA) at the 2026 Amplify Informatics Conference. The FAMIA designation recognizes professionals whose accomplishments and leadership are advancing the field of applied clinical informatics.