AI Regulation in ER and Clinical Judgment: Why AI Tools Must Be Designed for 3 AM, Not 3 PM

Dr. Natasha Dole: A me is buzzing at 3 a.m. That's when I need you to come shadow me as the person making the tool to see what my bottlenecks are, what my challenges are, and understand my environment. Because stuff normally goes wrong at ungodly hours, and that's what I need you to see. And that's where the problems arise. That's where all the issues arise. So if we're not tweeting for that, then you having a great tool that works at two o'clock in the afternoon is not necessarily going to be a great tool at 2 a.m. And for me, that's the tool I need.

Chris Hutchins: Today's guest is Dr. Natasha Dole, a physician, a healthcare executive, technology leader working at the intersection of clinical care, digital innovation, and artificial intelligence. Dr. Dole has spent her career focused on how technology can meaningfully improve the practice of medicine and the experience of both clinicians and patients. Her work spans clinical operations, health system leadership, and the responsible integration of emerging technologies into real-world healthcare environments. What makes her perspective particularly valuable is that she approaches innovation from both sides of the equation. She understands the realities of practicing medicine, and she also understands the design deployment and governance challenges that come with bringing advanced technologies like AI into the clinical settings. In a moment where healthcare is rapidly experimenting with automation, decision support, and new forms of digital infrastructure, Dr. Dole brings a grounded perspective about what actually works in practice, what risks are often overlooked, and what responsible adoption really requires. In this conversation, we explore how clinicians experience AI at the point of care, what health systems often misunderstand about implementation, and why thoughtful governance and human-centered design will determine whether these tools ultimately strengthen care or complicated. Dr. Dole, welcome to the Signal Room.

Dr. Natasha Dole: Thank you. That was quite an introduction. Thank you so much. I'm very honored and humbled. Thank you for the invite.

Chris Hutchins: Well, it it's a pleasure. I've been looking forward to this. I've been really enjoying your your content that I've been seeing. Thanks. You've got unbelievable uh knack for translating images and a wide range of ways that you frame things that people can really relate to that would be more difficult for them to understand otherwise. So I think one of my favorites what was the the recent one. I I've trying to remember is is the Barbie and Ken thing. That's what it was. But it's it's just one example. But you you've got a really great way of translating things and telling a story so people really can understand.

Dr. Natasha Dole: Thank you so much.

Chris Hutchins: So I want to jump into some some good good stuff here because there's a there's a lot that people don't understand about practicing medicine. And that's clear to me in how even reading the threads that that are online when you're posting content about what you're experiencing, I I just see that there's just a wide variety of levels of understanding. So really excited to get into things with you this morning. It'll be great for our audience. So in emergency medicine, how is credibility established in the room before anyone speaks?

Dr. Natasha Dole: So I think I would need to break that up into pre-AI and AI, right? So if we're going to the world pre-AI in the emergency room, credibility is normally established by sort of assumptions based on situational awareness and the senior most leader, who's often the senior most decision maker. And that person then automatically kind of acts as team leader for the rest of the team. And that role is assumed, it is then explained, further roles are allocated, responsibilities are clarified, and you know, hence you go forward. And often when it's a situation like that, we've got strict SOP, so standard operating process procedures and policies in place, you know, specific algorithms that are followed with the scribe, and like I said, clear a team leader and then who allocates other roles. With AI, the AI that's helped in that situation is the team leader, is still the human in the loop, 24-7, come what may, but AI is assisting as a scribe, which is great because it picks up things that are happening in real time, especially in a place like AI, which is a very high-stake environment. And obviously it significantly decreases cognitive load, but you have to double-check everything. You cannot over-rely on it. And you need to make sure it's recording all the correct facts. And often in that situation, if it's a high-stakes environment, the patient may not be in a position to give consent. So that's where the governance issues come in. And often again, you don't have next of kin where you can't get consent. So everything you then do is on balance and obviously on the risks of, I mean, you you weigh out the benefit versus the risk and decide if you're going to proceed. But using an AI scribe in those situations seriously, seriously decreases mental fatigue and cognitive load.

Chris Hutchins: Well, I I think that's an certainly an important objective. Um I've I've seen over the course of my career, the things that we continue to layer on. Somebody who just wants to take care of patients. It's excruciatingly painful for every everyone I've ever talked to about this. I've seen eye rolls every time I have invited a physician to come see a demonstration of technology.

Dr. Natasha Dole: And that is so, so important. And you know what? If I could add to that, the reason physicians eye roll at that is because we're not included in the decision making when this tool is being made. And the tool is for us and needs to be used by us. So if we can't give you input as to what we want, it's not going to be a success. So excluding us and the patients who it's for and who it's aimed at and who we are trying to prevent further deterioration or decrease their reattendance rate and improve our efficacy, that's when you need to get a multidisciplinary team and have all your stakeholders. And that's what needs to be a top-down and a bottom-up approach. So if you've just got your very intelligent tech guys making a tool and you've got no clinician helping them, that tool is already doomed for failure. And if it's gonna add more layers and more checks, I'm not gonna want to use it. I already have enough checks to do pre-AI.

Chris Hutchins: Right. Yeah, that makes total sense. And it's interesting, you you know, you mentioned the the patient in this perspective as well. Uh I'm excited, I'm gonna be doing another episode soon with someone who's really looking at things from the caregiver standpoint, having really been trying to guide a family member through some really challenging things in the healthcare environment. So this should this is gonna be an interesting period of time that I think. And I guess that one of the things I wanted to kind of dig into a little bit with you is you you've written a lot about assumptions that patients and and colleagues make about clinicians. We we just don't think the way that you do because we've never been trained to do that, first of all. But how did these assumptions show up in high pressure moments? I mean, we're gonna see medicine is not like any other aspect of medicine, generally speaking.

Dr. Natasha Dole: So from my experience and obviously very recently lived experience, it shows up with a patient convinced that the paid version of the AI they're using is correct. So they have put in their symptoms or their concerns into AI, and then the generation the the output that's generated automatically equates go to AE or other advice, which really worries me. And again, there's two sides to this. I'm actually more worried about the ones that don't come to AME because I wonder are those people the ones that actually need to be there versus the ones that are told to come to AME that didn't necessarily need to be there, and they've been given false reassurances, false advice, incorrect advice. And the AI has completely ignored the inaccuracies, the harms, the bias, the population. And also there are alternative care pathways available. I fully appreciate currently where I'm working in the UK, GP appointments are really difficult to get. So I understand that patients are turning to alternative forms of healthcare resources, but the way things stand, an AI paid version is not an alternative healthcare resource. If anything, it's supposed to be I would actually almost want to say I want to ban it from patients using it for that reason. You shouldn't be asking AI for medical advice. That's where my my pet peeve lies. However, when patients come in to say, you know, the paid version of this AI has told me to come in, I see that as an education moment and it's a moment for me to explain to them why that uh the output is wrong. And if it's right, which so far it has not happened once, then I explain to them and then that's you know a moment of education, and then you I'm hoping that will get carried forward. But that said, in high stake situations, when I've got an AI fracture detection tool, it's great. It'll pick up a fracture that I may potentially have missed, but I still need to use my brain and my clinical judgment and decide if I'm gonna trust the AI or I'm going to override it, vice versa, if I think there's a fracture and there isn't one. And it needs to clinically make sense. So if the pieces add together, it equals a fracture, then yes, the AI was correct, but there are times where it's not. But that's rapidly evolving and that's the future. And I would love something like that to be incorporated, you know, on a daily basis. All of those things are currently being trialed, just like with lung nodules on a chest x-ray or detecting strokes with AI. The anatomy needs to make sense. You see a patient, you've gone to medical school, you've got two, three, four degrees, you know what equals a stroke. AI is to be your second brain. It's your co-pilot. It's not exactly an automated answer, which is what I'm seeing. So I worry about the med students of today because they have jumped to AI and I worry that they're not using their brain as their first brain, and they're using AI as their first brain.

Chris Hutchins: Yeah, you you bring us something that's really important. The whole idea of putting your own information as a patient into that kind of a platform. Without understanding context or how this thing actually works, that's really the biggest concern. But reality is I don't necessarily remember what medication I was on to well, forget about two years ago. I don't even remember the names of the ones that I might be taking today. So without that context, AI, just like a doctor, if he doesn't, if a doctor does not have access to certain information about your medical history, it changes the approach that they might take. You might have a symptom that by itself is really not that big a deal. But if your AI doesn't realize that you have other conditions where that is that becomes a different kind of a risk, then this is where it gets dangerous. And I I really I really want to make sure that we're battling in on how you think about the whole workflow of navigating this when patients are showing up that and they've they've trusted that capability because they really need to understand how you evaluate it, I think, in order to understand why they need to be very, very cautious about what they're looking at in terms of what it what a piece of technology can give them. Because the realities are it's still the patient-physician relationship that is a front and center. And that's where the trust really has to be. I I've never seen any of that.

Dr. Natasha Dole: And also the accountability. And as you said, the data, where is all that information getting stored? People are putting in patient identifiable details into the World Wide Web. Where is it being stored? Who is accountable for it? And I I know the humans are accountable for it, but I'm saying in terms of the AI that's being used, that data could be used for experiments and further research without your consent, without your knowledge. And you don't want that data shared in a space where you don't have access to it or have rights to say this is what you can and cannot do with my data.

Chris Hutchins: So I I mentioned, you know, the the process that you go through to determine whether you want to trust a tool or even use it. When you're looking at an AI recommendation, how do you decide whether to trust the tool or your own clinical judgment?

Dr. Natasha Dole: So for me to trust the tool, it needs to have gone through clinical governance and been approved. I need to see that and I need to see how it's been approved. I want to know who the stakeholders were, right? Um, who was involved in the making. I want to know that I'm still the human in the loop and it's still my judgment that will make the final call. And like I said, it's my co-pilot, not an autopilot. And I want to see the statistics. I want literature to say it has worked and in what data set and what population group it has worked in. And I want to know about the biases, I want to know about the harms, the inaccuracies. So I want to know about the scientific facts linked with it. And I think if I, like I said earlier, if it adds more layers to my brain or more layers or more tasks to my already multitasked day, I'm not interested in using it. It needs to make my life easier and it needs to be an adjunct. And it's a classic we keep hearing this garbage in, garbage out. So it's that I when I whenever I teach about it, I say it's the three Ps. So it's P being the patients, because obviously healthcare everything's about patients. The second P is people, which is being us, the clinicians, the healthcare providers. And the third P is profit. And I don't necessarily mean profit from the AI app. I just mean in terms of how much it is, it's costing or what the funding is needed to continue using it if it's really good, and where we're going to get funding from. And this is purely from a UK government perspective that I'm speaking from.

Chris Hutchins: Yeah, I I think though that when you when you talk about the profit side of it, like I don't think it really matters too much where you where you are. I think the the difficulties that come from the bias that people are thinking about, I think is where you know for me it's uh a little dicey. So what does AI not know? Well, the it doesn't know anything unless you tell it, first of all, but it also doesn't necessarily understand the context. It doesn't understand history versus like ri real world today.

Dr. Natasha Dole: So you as a patient, um, and I don't mean you per se, but a patient using it doesn't necessarily know what pertinent positives and negatives to put into the AI. And even so, I wouldn't trust it because it's just based on a whole lot of information that I'm not sure I trust the medical literature associated with it or if there's enough in medical literature associated. Certain ages, certain races, certain population groups are excluded from it. And I don't believe that a patient is gonna say, you know, I am of Indian origin, my family history is this. These are exactly my medications. This is the symptom I'm having. And even if this is the symptom I'm having, there are at least five or six other differential diagnoses that could be causing that symptom. You know, you could go from mild, moderate to severe. And AI isn't gonna pick up that it's mild, moderate, or severe.

Chris Hutchins: Yeah, that that that's a whole nother conversation as well. I think the the the difficulties that come from missing information and things that you just don't even know. I just don't recall that much from my from my my past in terms of you know what what actually might even be relevant to have a conversation about. So I think this is an important area to to really make sure that we understand it. And in particular, what are the what are the characteristics of a system that lead you to a point where you you know that you've got enough information to make a decision? What are some of those things that you have to be able to see in order to feel confident in relying it on the capabilities?

Dr. Natasha Dole: No, again, I think for me it's it's the process. If it's decreasing my workload and it's making me more productive, making me more efficient, and I can see that translating into evidence-based care that's improving patient flow and avoiding overcrowding and avoiding exit block, because those are the two main pressures that we're seeing and we're trying to mitigate, then it's a yes. But I need to know the background to it. Just because you're selling me a great AI tool, which is great in the presentation, it doesn't equal great in real life. And just because it's smarter, it also doesn't equal competent. And fast doesn't need doesn't necessarily equal competent either. I need it to be safe. And I think that's the part that a lot of the tech world are missing. By all means, I mean, if it saves me from sitting doing documentation two hours in a day, which I wouldn't be doing otherwise, absolutely, I'm all for using it. If it's picking up things that I wouldn't pick up when I'm tired, absolutely. But again, I still need to double check it. And I think that's the most important word is need to double check. And we cannot be over-reliant.

Chris Hutchins: Yeah, and I I think the the problems that I've seen repeat themselves are when that judgment aspect of it we're not accounted for. Not that we don't think people are going to have be able to apply a judgment, but are we putting the the positives in the right places that actually cause you to step back and actually think about those things? Because there's just so much going on at any given time. Things are moving so quickly. One one room to the next, you've got very, very different scenarios that you're faced with with a patient. Um, and quite honestly, people don't come in bunches like bananas or anything like that. Everyone's unique. You and I might actually have the same condition, take the same medications, have what appears to be the same kind of environmental scenarios. But it's very, very rare that two individuals are going to actually have the exact same outcome.

Dr. Natasha Dole: And you hit the nail on the head there. So that's exactly it. That's the crux of the matter right there.

Chris Hutchins: So I want to kind of dig into a little bit around where these recommendations kind of lead to and what challenges arise w when the recommendation doesn't align with your critical judgment.

Dr. Natasha Dole: So I think there's there's two sets. There's one is mindset adoption, um, and then the other one is organizational readiness, whether the organization is actually ready for this digital change that we're all experiencing. And as you said, it's rapidly evolving and changing literally on a minute-to-minute basis. And then you have the people that are pro-AI and you have the people that are anti-AI. And it's finding that balance and, you know, getting them on board. So for me, I'm an AI enthusiast, but I'm also aware that I will still trust my clinical judgment more than an AI tool. So if the AI tool differs to what I have come up with, with what my clinical features are suggesting, I will correlate the two and decide: is it actually making sense as one plus one equal two, or is this telling me one plus one equals three? And if that's the case, then you know that's when I go to a second human brain and I'll say, I've already used AI as a second brain, but now I need another human to agree with me. And I have no qualms reaching out to another colleague to say, you know what, can we just have a moment of shared decision making? Because this is a tricky situation. This is the clinical picture, and this is what AI is saying. And now I'm confused. It's also two o'clock in the morning, so another head and another opinion would be great. And then you make a joint decision. I have never a hundred percent solely relied on AI. And currently, as things stand, I can't see that happening, at least for me, for my own individual clinical practice.

Chris Hutchins: No, then that that's that's such an important thing to realize. I think our systems historically have been built like healthcare is static somehow. And you know, we always say it's not. I mean, they the whole idea of practicing medicine, I think, has been kind of lost over a number of years. And there people either trust completely or they they they're not trusting uh nearly enough. Um and that's a that's a place where we could really do some damage and and hurt ourselves if we're not keenly aware of it. Because when we approve something from a governance standpoint today, and then we don't revisit it for five years, that's problematic with even without AI. Now that AI is up to now in the picture, the evolution is going to be much, much faster. And I think I'd love to hear some some of your thoughts around how do we really address that from a governance standpoint. What are some of the things that we can put in place to to ensure that we are monitoring and we're we're not assuming six weeks from now that what we decided and signed off on last month is going to be the way it always is. We we really have to make sure somehow. Uh I'd love to hear your your thoughts around what we can do there.

Dr. Natasha Dole: So I think for me, that is uh a three-aspect answer. So one is AI literacy, which is different to digital literacy. And the third one is continuous audits and a cycle that gets reiterated. It needs to have sole ownership where someone's saying, I'm gonna be the one that does the audits, I'm gonna gather the good, bad, and the ugly and feed this back to the consumer so we can change it. And again, that needs to be an MDT team. So you need to have not just clinicians, it needs to be everybody. And again, that's why I say top-down, bottom-up approach. And then the other thing is currently the biggest uh stigma around it is you need to be tech savvy to be used AI. And I keep hearing I don't have enough time for AI. If anything, the people that are not tech savvy and don't have time for AI are the ones that need it the most. So it's kind of breaking that mindset and changing that barrier. And I mean, it's nobody's amenable to change. Change is never easy. But within the UK, the NHS 10-year plan is to go digital, and AI is a big part of it. So it's getting people on board. And the way I do that is I often adapt two quality improvement tools into getting people on board. One is what's in it for me. So I explain to them what AI does for you, why you should be using it, and how it'll help you, what it enables you to do, which you couldn't do before. The most important thing, besides decreasing cognitive load, is um having more meaningful connection with your patient, which we've never ever had. And the second one is the five whys. Why should I use AI? You answer the question, and then you answer why again, and then why again. And by the time you get to the fifth why and the fifth answer, there is enough unpacking and dissecting that's happened. And that's how you get people on board. So it's kind of like a service improvement project, except you're using it to market a tool which you believe in, especially if it's doing good and it's been approved. And you have to stress, and I keep stressing this, that you always need a human in the loop 24-7, 365 days. I mean, that's not negotiable.

Chris Hutchins: Right. I I think that's it, that's an important distinction. I we we hear human in the loop kind of thrown around like a number, like it's the latest buzzword. And I I can see where uh a physician that hasn't had a lot of exposure to AI may feel like that's an imposition and asking even more of them.

Dr. Natasha Dole: Um the reason they haven't had a a lot of exposure is because we sadly don't have. enough allocated protected time to teach it. If you tell me that post my night shift, there's a day training for how to use AI and how it can make your life better, that's the day of my rest. That's the day I'm sleeping, that's the day I've got house care, I've got parenting or whatever it is that you may have going on. You're not gonna then want to attend teaching on something that you're already not sure may or may not work. So it's identifying the opportune moments to get the people on board and you need to get again, it's that the impact engagement scale like who do you need to help you get people on board and how important is this person to have on board? Because if you have person A on board who's really important, then you know BCD will follow. Whereas if you get person, for example, person F who's not as important or as big a dog in the executive industry or the team, the rest of the team may not want to follow. So it's about having, as we say, having the big guns on your side and you know working collectively and you need to have shared outcomes because you've got mutually agreed goals to reach.

Chris Hutchins: Right. When we think about this, I think comes down to a couple of different things that are important. You've you've already touched on the the you know not replacing the the human judgment aspect of it, but but then there's in a broader sense is this is also about what what's responsible AI and healthcare. So it's not only it's not just AI and healthcare, what's responsible healthcare and you know what are the things we're introducing that that either reinforce the and protect that exchange between a doctor and their patient or or if it doesn't, when we talk about that, what are some of the responsibilities that you think uh remain uniquely human? And I think that kind of speaks to the youth apprehension that you might run into from clinicians that are already feeling overwhelmed.

Dr. Natasha Dole: So I think three things uh accountability consent and disclosure. And you need to realize that if the AI makes a mistake and you've gone with it, it's not the AI's fault. It's still you that treat the patient. And consent, like I said, especially in Amy, it's not always possible because the patient is unconscious, but if you can explain it and do it on the balance of probabilities because the benefits outweigh the risks then by all means. But you need to disclose it in your notes. So currently all the AI that I do use auto-populates a line saying this note was generated by this, you know, and then it names the AI. Or even with the factor detection tool it says this is a virtual factor detection tool. And patients are now starting to give consent and patients are seeing the effect it's having on overcrowding and then time to be seen, the weights to be seen, especially when we're drowning. I mean we're supposed to be seeing the end of winter in the UK, which we're clearly not because the pressures are just horrendous and they're getting worse and worse. Our wait times are just increasing. And if AI can fix that and help assist with alternative pathways, whether it's through a scribe or stroke detection or fracture detection, that's really the way forward. And again, only if you've got a human in the loop and it has gone to strict robust governance. And like I said consent and disclosure are of utmost importance to me. So that's what responsible AI for me would be.

Chris Hutchins: You've touched on consent a couple of different times and I and I I wanted to kind of dig into this a little bit because I don't think I've heard it talked about a whole lot. We're gonna see medicine is a different thing altogether because oftentimes the patient when they do arrive, they they're not in a position to actually make a decision about consent. So where do you see some of the the the more critical uh needs and opportunities for us to be focusing on um as we're trying to design technologies that actually support the realities of what you're dealing with in the real world.

Dr. Natasha Dole: What what are some of the things that you you see that are gaps that we can actually address uh just by thinking differently about it, maybe building in stops or whatever those things might look like I think if we get patients and clinicians involved from the very beginning, not at the end, that's absolutely essential because the clinician will tell you what he or she wants in that model, what works, what the bottlenecks are, what the challenges are, what we see in lived experience. And the patient will tell you what they want from the output, what they want from the model. And I think consent needs to be inbuilt into the model it needs to auto-populate and that disclaimer is absolutely essential. And again if I then need to write another sentence to say you know consent was obtained the disclosure I then need to put in that's adding more work to me. I appreciate it's two more lines rather than the entire clinical note. But it's still it's important and you're right patients don't know what it is. And right now, because it's such a big buzzword AI is changing everything, not just in healthcare, people are becoming more aware of it. But people aren't aware of the harms people are just seeing especially patients are just seeing the benefits of it. Right. Um or are like I said over relying without realizing the inaccuracies associated with it not to mention the bias.

Chris Hutchins: Just the the whole notion that any kind of technology can can actually replicate years of experience. I think I I see this in a lot of different ways. It's not even necessarily about AI and technology. There are just certain things that that don't make any sense when you look at it from a a real world lens. There's just no way on earth I could wake up one day and know enough to make a decision based on all your years of experience in in clinical judgment. It just doesn't work that way.

Dr. Natasha Dole: So exactly and then you know it comes down to the patient then because then the patient's responsible for trusting the AI and not coming to the Amy if they've gone with that advice but I'd like to think that the AI models are changing given all the feedback they've received. So now the the outputs are not as concerning as they were previously but I still don't trust them. I mean AI should not be used to be turned or should people patient shouldn't be turning to AI to use it for medical advice at all.

Chris Hutchins: Yeah this kind of leads into something else I want to kind of talk with you about in terms of how design is actually approached. So what are some things that you know some designers, vendors or health system leaders consistently underestimate about clinical environments?

Dr. Natasha Dole: So I think consumers and tech creators model this for outside of A and E for an eight to five healthcare job. A and e is buzzing at 3 a.m that's when I need you to come shadow me as the person making the tool to see what my bottlenecks are, what my challenges are, what my challenges are and understand my environment because stuff normally goes wrong at ungodly hours. And that's what I need you to see and that's where the problems arise. That's where all the the issues arise. So if we're not tweeting for that then you having a great tool that works at two o'clock in the afternoon is not necessarily going to be a great tool at 2 a.m and for me that's the tool I need at 2 a.m when I'm overtired, I've got decision fatigue, my situational awareness is decreasing, my cognitive load is increasing, I'm running on caffeine, I'm running on adrenaline, that's when I want something I can rely on, but again as an adjunct and as a co-creator and a thought partner, a thinking partner. So I need people to see that A and E is not just A and E is not the same as a surgery that's taking place at three o'clock for a planned appendix removal. And you know minutes change in the A and E. You could be completely calm the one minute with you know a very quiet department and the next minute everything explodes and you've got one sick patient after another. And that's where AI can help but that's what the tech team aren't seeing. They're not covering for those times.

Chris Hutchins: Clearly we've kind of missed the boat on a number of occasions in in terms of the assumptions that we make kind of going into design. I I can't tell you how many times I've been frustrated over the years just being on the administrative side of healthcare where we have vendor solutions put in front of us pretty frequently and one of the first things that happens that they will come in and actually whether it's shadowing or whatever, they have these conversations, but then it's quiet for a very long time and then one day they show up and they've got something that they've built and they're convinced you should just use it. What are some of the assumptions that clinicians are being made about AI by designers or c or clinicians where we've just kind of got things misaligned and that they're just always going to go wrong when we make these assumptions.

Dr. Natasha Dole: I think the biggest assumption which is false especially among the anti-AI group is that AI is going to replace us. And that and this line has been used repeatedly people who use AI are the ones that are going to replace you not the tool itself. And like I said it actually it makes me more efficient, more productive and it's given me my time back and I've said this repeatedly as well it it frees up space in my brain and it allows me to think better. It allows me to have multiple tabs in my brain open which are normally open anyway but now I can actually give each one of them appropriate nourishment and engagement because I've got something else taking down notes for me or something else assisting me to guide me and something that's functioning at a very high level but again it's still me making the end decision. But even if it takes off that 1% of technical burden or clinical workload or cognitive load, then it makes a difference. And again I can't harper enough about this, provided it's past governance and as a human in the loop.

Chris Hutchins: Right. And that's a distinction for me. You know, we we've exchanged messages on a number of different topics but I don't know that that we've talked about this specifically but in my own experiences have been kind of geared towards supporting the the patient provider relationship because I saw something up close and personal um several occasions when I was younger. But the the critical thing and I I've heard it in in what you're saying uh even here in the tone of your voice, there's a real passion for what the mission is all about and it's really taking care of people. And that I don't know that we really can quite grasp uh especially if we're not trained as a physician because the only reason anyone wants to even go through all the training and the years and the cost to actually be in a position to provide that kind of help to to people. I don't think we we really can understand it if if we haven't actually done for it, to be honest with you. But I think it's just really important and I I feel like it's an amazing honor for me to be involved in any way, shape or form in anything that I can do to help support what that mission is all about. And technology is not always the answer. It it just isn't I mean realities are people are coming to the ED for any reasons that they want to be there for.

Dr. Natasha Dole: Okay and you know what the majority of people that come in for whatever problem they come in for, they're scared and they need some love and some care and some attention and there's no replacement for the human touch. The worst part about COVID was how we had to break bad news you couldn't hug a patient you couldn't touch a patient you had layers of PPE on and it was awful. I don't ever want to practice medicine in that scenario again. And with AI I've been able to hug patients again because I have more tying with my patients because the scribe has captured the stuff and I've been able to look a patient in the eye and smiled or you know had a chance to interact with their little kid that's come along who's a very cute toddler and stuff like that has not happened in years. Even if I go back to med school which is now a very very long time ago that's something I I was never even trained with because I mean I didn't ever grow up with AI AI was not a thing. And I mean likewise if you turn that around and you ask a med student of today or even the Gen Z of today if you go back to DOS or you know pre-DVDs they don't know what any of that is or when you have to dial up the internet. So it just shows how rapidly the world is changing and evolving and as does medicine. Medicine evolves as rapidly and if you don't keep up you're gonna be really behind and that's the future and I don't think just at one stage the ultrasound has started replacing the stethoscope. So very soon AI is going to start replacing certain pathways and again not the clinician the pathway and if anything it's gonna make the pathway easier and more accessible. But it needs to be equitable that for me is the other key because it needs to cater to every single population.

Chris Hutchins: Yeah you you you said a couple things here there that I I I think we need to be really really cognizant of. If you can just talk about this for a second. So you're talking about populations and the you know we already kind of discussed a little bit that individuals are very very different. There's not two people on the planet who are who look the same from a profile standpoint. I think that's a an area where we really we really have to lean in on this stuff. We we can't be lazy about it and design the way we've always done it. We have also have to understand from a development standpoint and design, we are already starting at a point where we have we've taken time away from that clinical encounter the reasons that you went and you got your training wasn't for our technologies. That was never the point. And it's still not so the fact that we designed electronic health records honestly to support accurate billing it wasn't about clinical workflows. We have to just be aware of that. And this is not an efficiency play. I mean obviously when you spend money on technologies like this, there's this underlying push and motivation to try to be more efficient, but I just think that's the wrong visual to have in front of people is it really is how are we going to give back what we have been stealing from our clinicians since the introduction of all these technologies that are intended to make things better. I can't stress that enough we we have to do better. As we look ahead, I think this year there's going to be a lot more conversations around governance. Sadly I think on a global scale there's going to be some uh lessons that we'll have to learn the hard way because we've gone a little bit too fast.

Dr. Natasha Dole: That's it. It's evolving faster than what we're ready for. You've nailed it you've nailed it again. It's exactly that spot on the technology is miles ahead of us and again if we go back to just emergency medicine we are miles behind. Oncology, radiology cardiology are miles, miles ahead of us and emergency medicine's just not catching up. We're trying desperately but there are many, many barriers, many obstacles, many bottlenecks and it's not just funding. Like I said it's the the organization that's not necessarily ready where's funding issues there's that mindset adoption and it's breaking barriers to change and implementing change. And again you need repeated audit cycles and you need a continuous process of review, monitoring, progress. And like I said it needs to be the good, bad and ugly so you get the full spectrum so you get the entire narrative to help you change it for the better.

Chris Hutchins: This is a real a really important conversation that we're having. I I think we we've got to figure out a way for it to be amplified even more than it is to be honest. I think we're we're we're putting out these buzzwords or we're putting out all kinds of you know technologies and tools. Patients don't come through the front door because of our technology. They don't come because we've got these really flashy looking dashboards or whatever. I mean that's I've that's been my space for a long time is data visualization in the healthcare sector. I think I'll put it this way so the technologies that we're deploying in the in the designs I think about it in a similar way if you go to a venue to see your favorite band, for example, if you know who the sound guy is and where he's where he's located that's probably not a good thing. It's uh it's probably bad if you if you're noticing it, they're not very good at it. So I think from a technology standpoint kind of have to have the same mindset uh and really making sure that we're undergirding the profession and we're supporting it. That's what we're about and it should never be front and center. That's that's never a good thing when the technology is what we're talking about.

Dr. Natasha Dole: But in agreeable.

Chris Hutchins: So as we're kind of wrapping up and I can't believe we've gone so quickly through this time I would love to be able to talk to you for hours, but I know there's more important things that you need to be doing. But as as you think about the the the the remainder of this year, what are some of the the things that that you would encourage people to make sure that they're thinking about not only relative to emergency medicine but in general how how we're thinking about using AI and where we might be able to you know hopefully avoid some missteps?

Dr. Natasha Dole: I think it's a really good question and one that's not necessarily a straightforward answer. I think personally it's about becoming AI literate. Professionally it's becoming digital literate. And if you don't empower yourself and embrace the challenge it's not necessarily going to happen. It's as we've said a rapidly evolving landscape at the moment and AI is being used left right and center in the personal world and in people's professional worlds. There are tons of sweet resources out there that you can go and get your hands on. You can listen to podcasts you can attend webinars and they're all free and you know you need to start somewhere to get a grasp and and you start at the foundation and once that foundation is learned then you build your own framework to decide what you're happy to accept and what you want to question. And it's again it's embracing that change and embracing the challenge associated with that because not all technology is good. And like I said before just because it's good it doesn't mean it's safe and faster doesn't normally mean or doesn't automatically mean it's safe. And I think that's my big tagline is that's something we need to be very cognizant of. And and don't be afraid to ask for help when the AI is opinion differs to your opinion. There's no shame in asking for help pre-AI we'd always ask a friend you'd always phone a friend you'd ask the senior colleague still do that. AI is helping you and it's supposed to be supporting your decision. And again just like the human is wrong AI can be wrong too. So don't rely on it 100% and you need to proofread you need to proof check and you really need to with AI specifically and especially in healthcare you need to dot your I's and cross your T's because at the end of the day it's the clinician that's responsible and accountable. If something goes wrong no one is going to say oh it was the AI. It was you and in medic in a medico legal stance you've got no protection whatsoever and that's not something you want to be fighting with your medico legal lawyers. So we need to be very careful how we tread and how you use it. And like I said with explicit consent, disclaimers and full disclosure.

Chris Hutchins: It's such a wild time because I I know you think back in the 90s we we we couldn't have imagined where we'd be when the internet started to become such a big thing and it was essentially our whole lives are kind of surrounded by things that you know not that long ago didn't even exist. So getting this stuff right as we're talking about patient care, I think is really critical. As we wrap, could you maybe think about for just a minute, if we can do anything for you with the way we're approaching things with AI by design or or whatever, what would be the top uh priorities for you as a clinician?

Dr. Natasha Dole: What would be the things that would make the best difference for you in in the world involve me and come and watch me work and see yourself the bottlenecks that I face at 3 a.m and give me an AI that works or that makes me want to use it at 3 a.m not at 3 p.m

Chris Hutchins: that is amazing as usual you're really crystal clear thinking about these things and I I really appreciate it. And uh I just want to thank you for your your continual contributions. I learn a lot every time I I look at an article or or uh any activity that you're posting.

Dr. Natasha Dole: And I really appreciate all your support and engagement. So thank you it means a lot.

Chris Hutchins: It's my pleasure I think the interesting dynamics for me a year ago I did not anticipate being in an in an or in a situation where I was running my own business and trying to support the clinical mission from a different side of things. I've always worked inside of a healthcare system. I'm more inspired now than I've ever been and I I really truly do appreciate and respect the the work that you're doing. I think it's unbelievably challenging already to to do all balance the things you balance and now throw in this AI concept and you know not only are you figuring out for yourself, you're you're advocating for the the responsible use of it. You're advocating on behalf of clinicians and challenging the status quo and it it's it's really important. It's why one of the reasons a year ago I just started to work with some some firms that are helping me to uh really push some narratives that are opening gateways for clinicians to step up and have a voice because too often you've got politicians, insurance companies or pharmaceutical companies whatever they they're designing things with the right intention but to your point they have to be done with you not to you

Dr. Natasha Dole: absolutely that should be a tagline with you not to you

Chris Hutchins: right Dr. Dole it's been a pleasure to have this conversation with you and I I I can't thank you enough for for taking the time though you're incredibly busy yeah it means a lot to me that you you're taking this time and I I am excited for the audience to get a get a little bit of a chance to hear you know from you and and what's really important and where we can avoid complicating things further. It's really got to be about supporting what you're doing supporting that interaction between you and your patient.

Dr. Natasha Dole: It is a balance and it's finding that balance. Thank you, Chris really really humble and honored and very grateful for the opportunity.

Chris Hutchins: No thank you so much and I'll I'll be in touch with you because I I know there's more for me to learn and I I can't wait to see what you're gonna do next. And I I hope I'll have you back again really soon. We'll have some new exciting things to talk about. We'll also have some learnings that I'm sure you'll have uh a lot more successes uh in the future just because of the way that you think and you approach your your your role as a clinician but also as an advocate and and uh again thank you so very much for for for being on the show today.

Dr. Natasha Dole: Thank you very much for your very kind words. We'll be in touch soon.

Chris Hutchins: That's it for this episode of the Signal Room. If today's conversation sparks something in you An idea, a challenge, or a perspective worth amplifying, I'd love to hear from you. Message me on LinkedIn or visit SignalRoomPodcast.com to explore being a guest on an upcoming episode. Until next time, stay tuned, stay curious, and stay human.