Where’s the Disconnect Between AI in Nursing Practice and AI in Nursing Classrooms? An Innovation Expert Weighs in
An incongruence exists between healthcare education and healthcare practice when it comes to the use of artificial intelligence and other newer technologies. In the case of nursing, a leading healthcare innovation expert says academic programs are not incorporating digital technology in ways that reflect its actual use in clinical settings. As a result, nursing students can enter practice facing a steep learning curve.
Dan Weberg, PhD, MHI, BSN, RN, executive director of nursing workforce development and innovation at Kaiser Permanente, contends that underutilization of technology in nursing education is a disservice to the nursing profession — and ultimately to patient care.
"Technology in general is absent from our healthcare education, and specifically nursing education," he said. “In the clinical space, we’re really good at helping students make decisions based on the physical exam. I'm not saying the physical exam needs to go away — it's a core element in our profession — but it's only one modality that nurses are actually practicing in once they've graduated.”
Watch the video linked below to see highlights from our interview with Weberg. Continue reading for additional detail on his thoughts about the need for greater integration of AI and digital tools in nursing education.
Why Nursing Programs Must Integrate AI Into Curricula
The traditional physical exam, while foundational, is no longer sufficient in isolation, Weberg said. He proposes a more holistic approach, in which nursing students learn to perform assessments using a variety of tools and platforms: video visits, secure messaging, and even social media. Such adjustments would help education reflect the diverse ways nurses interact with patients today.
“As faculty, we can augment our clinical physical exam skills with technology and AI to teach our students to incorporate different modalities to assess a patient. We can also incorporate decision support tools to help students make decisions based on that assessment.”
Weberg said AI is undervalued as a teaching tool within the classroom. He encourages educators to view artificial intelligence as an additional source of evidence and to challenge students to critically analyze AI-generated recommendations.
"We have to teach students to assess what AI comes up with," he said. “Assign students to evaluate AI suggestions against their own knowledge and clinical experience, to arrive at better decisions — similar to how they perform evidence-based practice assessments.”
Weberg feels so strongly about digital health technology and its role in advancing nursing practice that he and his coauthor dedicated a chapter in the 3rd edition of Leadership for Evidence-Based Innovation in Nursing and Health Professions to this topic. This Jones and Bartlett text became available in September 2024.
Shifting the View on Technology From Adjunct To Need
In education, AI and other technologies are not mere adjuncts, but rather core components that help develop and enhance clinical decision-making skills, Weberg said.
“Most of the AI medical tools aren’t free, but you can have students use these tools for free,” he said. “You can create scenarios and as you're creating them, type the answers into Chat GPT to see what comes up. Now you can compare and have that dialogue with your students.”
For academic programs that don’t have financial resources to invest in more advanced AI technology, Weberg said simulating AI is both practical and beneficial. If direct access to AI tools isn’t available, educators can create scenarios that mimic AI input. This method trains students to critically evaluate information and to view AI as one of many factors influencing their clinical decisions. For example, in lieu of AI, Weberg suggested that the faculty member can act as the clinical decision support tool.
“You can have the students do their assessment and then mock up delivering that information to them, either through voice or something in writing,” he said. “Sometimes you need to make it inaccurate so that the students have to actually critically think: ‘Is this a piece of information that I'm going to use or not?’ Or, make it really accurate so that it changes the way they think about the solution. Then debrief on that.
“Ask them, ‘How did you determine whether this was good or bad information? What experience? What evidence do you have? Does it seem reasonable?’” he continued. “You can actually use that evidence-based practice process to assess real-time suggestions from a simulated machine insight. And that alone gets students to stop thinking about AI as the answer and more about another data point to make a decision on.”
The Imperative for Technology and AI in Nursing Education
Weberg acknowledged that some faculty and programs are hesitant to incorporate digital technology into nursing education. He cautioned that avoiding today’s rapidly evolving tools could render nurses less prepared for the technological realities of healthcare practice.
"It's fine if you don't want to use AI, that is your choice,” he said. “But that's not going to stop it from being used in the workplaces where new nurses will begin their careers.”
Weberg urges educators to provide students with exposure to AI and other technologies and to explore the evidence-based benefits of AI in improving care and nursing practice.
Using AI as a Decision Support Partner in Nursing Practice
Beyond the classroom, AI's potential as a decision-support tool in clinical settings is immense, Weberg said. Today’s AI tools can assist nurses in interpreting assessment findings and guide them through clinical decision-making processes. It’s another compelling reason to bring AI into the classroom.
"Using information from AI and some of the decision support pieces,” he said, “we can go into ChatGPT and say, ‘I have this assessment finding … what should I be looking for?’" This provides an interactive learning experience that fosters critical thinking and situational awareness in nursing students, he said.
An example of an AI decision support partner on the market today is Kate AI, which is designed for use in emergency department triage. Weberg described it as a second set of eyes that analyzes patient data and prompts nurses to consider potential issues that may not be immediately apparent.
"It's using AI to read the electronic medical record, which is impossible at triage," Weberg said, noting that it illustrates how AI has the potential to support nurses in making more accurate triage decisions.
Rethinking the Evidence Hierarchy with AI
The advent of AI calls for a reevaluation of the traditional evidence hierarchy in nursing, Weberg said. A model that embraces real-time data, allowing nurses to make informed decisions based on the most current information available, is needed.
"With AI, we can actually ask real-time questions about what's happening in the moment," he said. This shift will empower nurses to supplement historical evidence with insights drawn from AI, he said, potentially enhancing the quality and timeliness of patient care.