Jing Wang, dean of Florida State University’s College of Nursing, sat down with MobiHealthNews to discuss the role nurses play in implementing AI and the mistrust and lack of AI education that exists among nurses that hinders adoption
MHN: What is the benefit of AI for nurses? How has the technology changed how nurses practice?
Wang: Nurses are the largest healthcare professional group. When you talk about implementing AI in healthcare, I think nurses are the largest implementers of AI solutions in healthcare, and right now, we have a significant nursing shortage across the nation.
However, nurses, on average, spend a lot of time in front of the computer. The administrative burden, documentation burden, workflow processes, all of these areas happen to be the areas AI can be the biggest solution.
So, where I see what is missing currently in the conversation is that people do not recognize nurses are actually the main implementers of AI solutions in healthcare. How people view AI as something that will fill more slots for nurses or will replace nurses – so you have seen nurse strikes about or anti-use of AI – well it is because there is a lack of education and training in knowing how AI can help or how responsible AI will help.
That’s kind of the paradigm shift, where people always think it is replacing nurses. That’s why we are trying to spend more effort in partnering with CHAI, the Coalition for Health AI. We are the nursing education provider on microcredentialing programs because we feel like a lot of nurses need to learn about AI.
It is a different way of being a nurse because what if I don’t agree with the AI algorithms, with predicting the risk versus how I was trained as a nurse?
So, every nurse needs to understand AI governance model in a way to say, in this situation, as a nurse from my clinical judgment, with these AI tools, how should I function? Every hospital system may be different. I think there are just so many opportunities.
We launched this nursing and AI Innovation Consortium with the branding of “nursifying AI” because nurses have been rated by the American public, over 24 years now, as the most trusted profession. And when we talk about AI, there is just a lot of mistrust, including nurses’ mistrust of AI.
So, how can we leverage patients being so close with nurses and trusting nurses, and how can we empower nurses to use AI and co-develop, co-design and know how AIs are developed, so [they] know how to safely use it in the clinical settings.
MHN: You seem like a big advocate for AI. Are there any parts of the technology that make you nervous?
Wang: I would not say I am a diehard AI advocate in this spectrum. One of the things I always emphasize in this initiative…is I talk about high-tech, high-touch. What matters for nurses is the caring and the human perspective, and so I actually value the caring on the non-tech piece more in this context, but I do want to advocate AI because I do see the potential of AI and for nurses to understand the risks, the biases, the hallucination that exists in generative AI.
If I don’t know about it, then there is no way that I can trust AI to provide that care. I actually want to spend 10% of my time in front of the computer, but if I don’t trust a product, because I don’t know about it, I’m, of course, fearful about it.
I think the risks of AI is the general risk that we all need to pay attention to, that is the lack of understanding what goes into it on the predictive AI side, what data was used to generate this tool?
If I don’t have the knowledge on how biased the dataset is in developing this AI prediction tool, then I won’t be able to know how to use it. So that is a huge risk for predictive AI.
Generative AI, I think, is sort of a better tool that is addressing the hallucination part, and I think is more in terms of the administrative burden and all of those tools, I think that could be more effective in helping nurses.
MHN: What would you say to a nurse who is hesitant about using AI?
Wang: I would say open your mind to learn about what AI is, beyond the two words, artificial intelligence, and to understand what CHAI is working on.
There are so many different kinds of AI products and AI solutions, from predictive to consumer, direct-to-consumer, to generative AI. Learn more about exactly what it is and get more education or collaborate to ask the right questions.
If you want me to use this, what are the legal risks? What is the governance risk? What is the prediction risk? What is the bias that I need to pay attention to?
I think that is sort of a minimum requirement for all nurses moving forward; I would recommend to all nurses to have a basic understanding of the technology.
Jing Wang, dean of Florida State University’s College of Nursing, sat down with MobiHealthNews to discuss the role nurses play in implementing AI and the mistrust and lack of AI education that exists among nurses that hinders adoption MHN: What is the benefit of AI for nurses? How has the technology changed how nurses practice? Wang: Nurses are the largest healthcare professional group. When you talk about implementing AI in healthcare, I think nurses are the largest implementers of AI solutions in healthcare, and right now, we have a significant nursing shortage across the nation. However, nurses, on average, spend a lot of time in front of the computer. The administrative burden, documentation burden, workflow processes, all of these areas happen to be the areas AI can be the biggest solution. So, where I see what is missing currently in the conversation is that people do not recognize nurses are actually the main implementers of AI solutions in healthcare. How people view AI as something that will fill more slots for nurses or will replace nurses – so you have seen nurse strikes about or anti-use of AI – well it is because there is a lack of education and training in knowing how AI can help or how responsible AI will help. That’s kind of the paradigm shift, where people always think it is replacing nurses. That’s why we are trying to spend more effort in partnering with CHAI, the Coalition for Health AI. We are the nursing education provider on microcredentialing programs because we feel like a lot of nurses need to learn about AI. It is a different way of being a nurse because what if I don’t agree with the AI algorithms, with predicting the risk versus how I was trained as a nurse? So, every nurse needs to understand AI governance model in a way to say, in this situation, as a nurse from my clinical judgment, with these AI tools, how should I function? Every hospital system may be different. I think there are just so many opportunities. We launched this nursing and AI Innovation Consortium with the branding of “nursifying AI” because nurses have been rated by the American public, over 24 years now, as the most trusted profession. And when we talk about AI, there is just a lot of mistrust, including nurses’ mistrust of AI. So, how can we leverage patients being so close with nurses and trusting nurses, and how can we empower nurses to use AI and co-develop, co-design and know how AIs are developed, so [they] know how to safely use it in the clinical settings. MHN: You seem like a big advocate for AI. Are there any parts of the technology that make you nervous? Wang: I would not say I am a diehard AI advocate in this spectrum. One of the things I always emphasize in this initiative…is I talk about high-tech, high-touch. What matters for nurses is the caring and the human perspective, and so I actually value the caring on the non-tech piece more in this context, but I do want to advocate AI because I do see the potential of AI and for nurses to understand the risks, the biases, the hallucination that exists in generative AI. If I don’t know about it, then there is no way that I can trust AI to provide that care. I actually want to spend 10% of my time in front of the computer, but if I don’t trust a product, because I don’t know about it, I’m, of course, fearful about it. I think the risks of AI is the general risk that we all need to pay attention to, that is the lack of understanding what goes into it on the predictive AI side, what data was used to generate this tool? If I don’t have the knowledge on how biased the dataset is in developing this AI prediction tool, then I won’t be able to know how to use it. So that is a huge risk for predictive AI. Generative AI, I think, is sort of a better tool that is addressing the hallucination part, and I think is more in terms of the administrative burden and all of those tools, I think that could be more effective in helping nurses. MHN: What would you say to a nurse who is hesitant about using AI? Wang: I would say open your mind to learn about what AI is, beyond the two words, artificial intelligence, and to understand what CHAI is working on. There are so many different kinds of AI products and AI solutions, from predictive to consumer, direct-to-consumer, to generative AI. Learn more about exactly what it is and get more education or collaborate to ask the right questions. If you want me to use this, what are the legal risks? What is the governance risk? What is the prediction risk? What is the bias that I need to pay attention to? I think that is sort of a minimum requirement for all nurses moving forward; I would recommend to all nurses to have a basic understanding of the technology.
