AI in Action – Transforming Clinical Care

This transcript was generated by AI and may contain minor errors.

Dr. Tania Tajirian: We don’t have a pilot problem. We have a follow through problem.

Katie Bryski: Hello and welcome to Digital Health in Canada, the Digital Health Canada podcast. I’m Katie Bryski.

Shelagh Maloney: And I’m Shelagh Maloney.

Katie Bryski:  As we celebrate Digital Health Canada’s 50th anniversary, we have 50 reasons for you to listen to the podcast. Reason number 32: we’re in conversation with leaders who bring inspiration and impact.

Under the hype, what’s really happening with AI adoption in Canadian health care? The AI in Action transforming clinical care across Canada Working Group recently completed a first of its. Kind, environmental scan of artificial intelligence driven clinical initiatives and Canadian health care delivery. Two leaders involved in that work share what they found, why it matters, and what it means for leaders today and tomorrow.

We are very pleased to welcome to the show, Dr. Tania Tajirian, Chief Health Information Officer and Chief of Hospital Medicine at the Centre for Addiction and Mental Health. And Dr. Angel Arnaout, Chief Medical Informatics Officer at the Provincial Health Services Authority. Welcome to you both and thank you so much for joining us.

Shelagh Maloney: And so let’s start this episode the same way we start all of our episodes. We just, as you know, this is about what we’re doing, but also about who you are and some leadership lessons. So Tania, we’ll start with you and just let us know what your career journey is and how you got into this particular path.

Dr. Tania Tajirian: First of all, thanks Shelagh and Katie. It’s a pleasure to be here. I’m gonna admit I’ve been following this series for a while now, and I love that Digital Health Canada is creating space for these real conversations. I’m also very excited to share what we’ve learned through the AI and Action Working Group, and more importantly, how these lessons translate into our everyday leadership and care.

So a little bit about me. I carry many hats. I’m an academic primary care hospitalist by training, and I’m gonna say CHIO, Chief Health Information Officer, by evolution. And after almost 18 years, I’m gonna say at CAMH, I can say I’ve seen both sides of the digital coin. Where digital technology empowers and where it overwhelms.

And I love what Shelagh always says, that tension between hype and reality has shaped most of my career. Often, I’m gonna say in inaugural roles, first at CAMH, first, CMIO. I’m gonna say I’m an accidental, as many CMIOs. Fixing the EHR pain points post-implementation, reducing clinician burden, and now it’s exciting.

It’s the next phase as a CHIO, which is basically expansion into data analytics, AI and population health. Currently, I’m leading integration work in prevention going upstream by bringing metabolic colon, cervical and breast cancer screening into mental health settings. My passion. I’m just gonna be honest, and I say this to be reflective.

I’ve been very fortunate to have leaders who’ve trusted me with opportunities that taught me. Guess what? Leadership is a privilege, not a position. And one of these roles is my current one, which is the Chief of Hospital Medicine division, where I lead a group of small but mighty frontline primary care hospitalist.

We’ve gone through COVID, which reinforced to me. Culture is always gonna come first. And that culture of safety learning community is the foundation before infrastructure of innovation can happen. And I love how Digital Health Canada always says that that foundation of people first then processes, workflows, and then we talk about the tech.

And finally, I do wear an academic hat because I’m an associate professor at the University of Toronto, department of Family and Community Medicine, where not only I publish my work, but I also attend fantastic international conferences. I just came back from MedInfo in August in Taipei. I was blown away with the advancement of AI in Asia, and I also get to meet and collaborate with some incredible digital health leaders from all over the world.

Just like three of you, I’m gonna say, and I’m gonna give a little shout out. Angel is in my cool CMIO chicks group too.

Shelagh Maloney: Well, there there’s a little bit of an introduction angel. Apart from being in that CMIO chicks group. Tell us about your career path.

Dr. Angel Arnaout: Thanks so much and wow. I am, am following in, uh, big shoes there, Tania.

I started my journey in health care  20 years ago as a surgical oncologist, and for 20 years I practiced the best I could deliver in terms of high quality care. One patient at a time, one family at a time, one operation at a time, one decision at a time. And for 20 years I was delivering care, but also a, a deep academic trying to advance the science of how we practice surgical oncology.

I started to notice a pattern that even the best clinical care can be limited by systems and by information around us. There were bottlenecks at the system level, and they were mostly informational bottlenecks. They were about our inability to collect and use the data that we have available to us, our inability to learn from the data that we have around us to make things better.

And this really was very obvious during the pandemic for me, and so that’s how it started, is I got pulled into the digital and AI space during the pandemic. Not because I wanted to chase any kind of technology, but because I saw it as a lever, a very powerful lever to make our system much better in terms of the way it delivers care, more efficient, more compassionate, and also smarter.

At that time, I went and did a fellowship at Kaiser Permanente and learned about digital transformation. Then I went and did a formal degree, uh, and training at Stanford where it really got to me the academic side of technology implementation. The engineering behind it was very fascinating to me, and it’s true what they say.

Math can solve almost anything. And so I, I learned all that and it just seemed like after learning all of that, that I couldn’t just go back to being a clinician only. And I, and so I took on a job at the Provincial Health Services Authority, in BC, and now I focus on deploying AI safely and at scale across provincial health systems.

Multiple health authorities, multiple institutions, aligning clinicians’ data and leadership together so that we actually create some kind of impact at a system level. So this is why I think the conversation today is very timely. Canada is very interesting in the sense that I think we’ve been at an inflection point for a few years.

Rich an idea, but we need to double down in terms of making the impact, and I think now’s the time where we need to create the muscle and the mindset to do that.

Katie Bryski: That’s a great bridge, I think into the main topic of our conversation today. So you were both involved in the AI in Action Transforming Clinical Care Across Canada Working Group. Tania, you were its champion; Angel, you’ve been a CHIEF executive forum working group member. Uh, so I’m curious if we can dive into the impetus for creating this group and why you both wanted to get involved in it.

Dr. Angel Arnaout: Yeah, so I, um, was fortunate to be given the opportunity to participate. I got involved because I was interested in actually knowing what is the landscape of AI in Canada.

I, I was really, I think, similar to many people in the working group. Everybody was talking about AI in health care, like everywhere, like everything you read was about ai. But nobody really knew exactly what was happening on the ground in terms of the successes, the failures, and some of the learnings we weren’t sharing with each other, so there was no common understanding of.

Where many of the projects were, how they were proceeding, whether they were successful, whether they were stalling, and whether there were any real barriers. And so really it was for me, a really good way to get, as I mentioned before, I, you know, very much academically bent and that’s how it drew me into the specialty.

Very much working in this working group was about moving from speculation to evidence. I really needed to understand. Real world evidence, not the hype. Everybody brags and talks about what they’re doing, but in reality, did it actually make a difference to outcomes? Did it actually make a difference to the way we deliver care in a way that’s beneficial for patients?

I think unlike other technology alone leaders, as a physician, I feel that we have the advantage of only doing things that will be impactful. You know, I, my mom always said to me, are, are you serious? Are, are asking me when I got into it, uh, CMIO position, are you serious? Are you literally telling you that you’re gonna give up your surgical job of 20 years to go and work as an IT specialist.

So first of all, I had to explain that I wasn’t being an IT specialist working at a help desk. But at the same time though, it really helped me understand I am giving up so much. I was at the peak of my career giving up so much to, to be able to make a difference.

And so it’s not about the technology, it’s about whether they actually make a difference to the, the real world clinical impact. And so that was the reason I wanted to join s see where everybody was across the country and whether or not we could learn from each other and, and the learning from the data that’s out there from each other so that we don’t repeat the same mistakes.

That’s what I was interested in.

Dr. Tania Tajirian: Angel. I love it. I love it. You really, really described everything about the whole working group was all about that. So I’m gonna say, first of all, I’m lucky because I’m the co-chair of the CHIEF advisory group and I was really interested to build on the winning conditions for AI and health.

And what I appreciated with that, it really explained what sets successful implementation apart and the trust, transparency, all of the things that Angel is mentioning, the importance of governance as prereqs for scaling the culture there. There was a lot of talk about how important the culture of change of over risk fixation, equity, both that moral and operational core and the courage to fail fast and learn faster.

So what we did with the AI in Action scan basically built upon that working group. What we were hoping is putting evidence, as Angel said, behind these, these principles. So because I’m the co-chair of the CHIEF advisory group, and just it felt like Angel was saying it was right to create this group everywhere.

I went, everyone was asking the same question, was getting boring the same thing? Are we actually improving care with ai or just making, I’m gonna say better slides. I was seeing a lot of more interesting slides coming. So we decided to find out, and I’m gonna shout out for Marissa Binstock, our working group members from across Canada, and what really excited me is the emerging professionals.

We had multiple Masters of Health Informatics, MHI, student volunteers. And what we did, we were able to conduct what might have been, I’m gonna say the first national scan of clinical AI initiatives in Canadian health care. Basically it’s a living database verified through media releases, health system reports, vendor publications, and funding records, which we cross-reference and categorized by province, care setting, AI type, and stage of deployment.

We confirmed what many of us suspected in this space, pilots dominate in Canada. Scaling is rare and outcomes are thin.

Shelagh Maloney: Tania, I wanna pick up on that last comment that you made about the pilots. So 152 initiatives were included in the, in the environmental scan. 59% of them were pilots. Do you think that’s this pilot Titis that we’re so guilty of across the sector, or do you think that’s reflection of where we are? AI is relatively new?

Dr. Tania Tajirian: I love this question. So we’re saying about 60% of the initiatives of our scan is still in the pilot phase. That might sound like you’re saying like a failure to scale, but it actually reflects, like you’re saying, where Canada is sitting on that maturity curve. We don’t have a pilot problem, we have a follow through problem.

Canadian health care, we are inherently cautious and that’s reasonable for so many different reasons, but honestly, at some point I kind of feel we have to land the plane. Also scaling, I truly feel, is not about writing better code, it’s about building trust, and I think Angel will say the same thing from our experiences.

It’s so important to focus on that, setting clear metrics and maintaining transparency. So Shelagh, off the 89 pilot projects, we tracked fewer than half published their evaluation results. This lack of visibility is preventing replication, in my opinion, and reinforces the siloed innovation I feel to move forward.

We really need to think about funding learning, not just launching. Pilots automatically should include built-in evaluation frameworks, shared repositories for lessons learned. And I think Digital Health Canada can be a venue for that and pathways for procurement. And I really feel innovation is not gonna be about being first.

It’s gonna be about being repeatable and reliable. And our scan reinforces that when clinicians us, I’m gonna say co-design, the solutions deployment becomes more effortless. And when they don’t, even the best algorithms that you’re gonna be developing feels like spam. I’m gonna be honest about this. And what I loved about the scan is like that.

It’s like if you don’t involve us. The stakeholders, the people who are actually using these tools, the people part before process and technology. And that’s what I’ve learned over the, the time in, uh, in this world of tech. We continue to fail because we don’t learn the lesson.

Shelagh Maloney: Angel, any surprises from the findings, from your perspective?

Were you surprised at all, first of all, and anything that you want to comment on from that perspective?

Dr. Angel Arnaout: You know, I don’t know if I was surprised. You know, we hear about in Canada, not just in in technology. I’ve heard it already. And so I think it’s a general theme across multiple areas in health care and digital and AI is, is no exception.

And I think the reason behind that is because, to be honest, somebody need to stand up at the 30,000 foot level to really define two things. For me, number one, a target operating model for scaling. We don’t have that, and what I mean is, is some of the things that Pam mentioned is the governance, the evaluation framework, the data infrastructure, but also the business model for scaling.

We don’t have a good way to calculate ROI. We don’t know how to move from grant or innovation funding to operational funding. I think those two things need to happen, and I also think that it’s about training leaders to be able to do sort of large system scaling strategies. So, you know, I think as a leader currently, I have the privilege of seeing multiple health authorities and overseeing multiple health care sites.

You know, you have to treat this AI business as sort of like a diversified business. And you have to treat it like a portfolio where there’s different tools for different health care specialties, and it has to be a, a portfolio mindset that somebody has to have where you are strategically deciding which sites, which specialties, which organizations, which tools match to create value. And then look for patterns across large systems to see where those areas could take advantage of the learnings of another and actually plan out how to scale.

And I think we need to train our leaders to be able to recognize patterns of success, be able to have the view of the entire landscape of where else that success can be duplicated, and where innovation can be new and where we don’t need to do any more innovation. I don’t know if we’re looking at it holistically like that.

Dr. Tania Tajirian: You know what, again, the findings and what you’re saying, angel wholly built directly on the winning conditions that.

Was definitely reviewed in podcast number 15, that call that you’re calling for, to shift from risk to readiness, to build governance that enables rather than hinders. To nurture a culture of experimentation where failing is part of learning. And I think that is part of, do you remember Angel, where we did the panel at the chief symposium that came out as one of the word polls about trust and strong AI leadership is gonna mean.

Creating the right environment, one is gonna be grounded in safety. We have to have shared purpose. And that measurable progress is so critical and it means we’re gonna have to start small, but scaling smart. And we have to be also from a leadership perspective, be willing to pause, learn, redirect when needed, and I think that’s gonna be an important skill to move forward with leadership in the future.

Katie Bryski: Tania, I’m curious, you mentioned earlier in the show you’ve gone to international conferences, you’ve seen AI at different stages of maturity in other countries. Are there things that we can learn from what other health systems are doing?

Dr. Tania Tajirian: Listen, I’m gonna be honest. When I went to, I’m gonna be honest, I went to MedInfo.

I was so proud of myself. I have two papers I’m going to present. And it was so interesting. I felt proud as a Canadian and I presented what we are doing and the silence in the room was deafening. And I didn’t know, like, I was like, oh my God, what’s going on? And one of the Taiwanese colleagues stood up and said they had fixed everything I was talking about 10 years ago.

They have digital twins. So what I really appreciated by these conferences, it makes you step back. Reflect of what others are doing. And I think that the beauty of digital health is the collaboration like this AI in action. Like we need to collaborate with other countries like, like European informatics, like there’s so many of these organizations.

But I always kind of say, getting out of our fishbowl, right? And learning what others are doing is amazing. But for me, I looked at their digital twin app. I was just like, I had envy. Everything is centralized. It’s not decentralized, right? Even for your health records, you tap out. You don’t tap in for sharing.

Like for me, this is the thing. What I really appreciate is exposure to other ideas, other solutions, and just the importance of collaborating with our national and international experts.

Katie Bryski: Angel, it makes me think of your comments earlier about the clinical perspective, the system’s perspective. I’m wondering about your reflections on the role of leaders in bridging some of these different perspectives and settings.

Dr. Angel Arnaout: First of all, I echo the sentiment here that, you know, we need to get out and look outward to learn in terms of leaders, I think. Yeah, it’s the translation of not just the language, the culture, but the style of doing things and being at the intersection of all these different specialties is what? I think a good leader is going to take advantage of somebody who can balance all of the what’s in it for me from all the different specialties and the unique strengths of every specialty, and bring it together in a very unique way such that we take advantage of the strengths of every stakeholder and foster trust between all those involved.

That’s what I think is going to make a difference between those leaders who succeed versus those who are just kind of coasting. It’s a skill to learn. It means we as leaders have to really invest the time to understand the lens from others’ point of view. This is the the time and age where we need to be broader.

And so to the people that are thinking of getting to this space, get out, learn about what other specialties do, be in their shoes. Go do their training, go be in their workspace, understand how they could bring value to what you wanna achieve because you can’t do it alone. And unless you understand that, I don’t know, we’re gonna be lost forever.

So I think leaders like Tania understand that and, and are succeeding because of it. But yeah, for emerging leaders, this is, this has to be intentional.

Shelagh Maloney: I really like that comment about learning from others and being in other shoes. As I’m listening, I’m, I’m realizing that part of this podcast is to share the learnings from the AI environmental scan, and we didn’t really talk about them, but we found that the three most popular AI initiatives projects going on right now across Canada are around machine learning, computer vision, and natural language processing.

The majority, many of those projects are in the hospital, an acute setting. Those are important findings that we should share and should maybe just say right now, if you are interested in learning more Digital Health Canada website, the AI in action transforming clinical care across Canada, that information is freely available on our website.

And so two things. One, you can go and learn about it, but two, as Tania said earlier, it’s a living document so you can add to it.

Dr. Angel Arnaout: I do think your first point, Shelagh, about the concentration of AI in acute care setting really does illustrate what we were mentioning earlier. The fact that you do need to have the infrastructure for AI to succeed.

And the infrastructure here we’re talking about is that acute care settings have very rich, dense structured data environments, and they’re often places where the data’s already centralized and there’s often academic partnerships with data science capacity. So, so that is an enabler of why these areas have succeeded and have dominated the AI deployment across Canada.

You are also seeing that not many, uh, AI is deployed in primary community care and public health, and that’s because. There isn’t this kind of infrastructure, right? You don’t have this data infrastructure or data science capacity in, in these areas, uh, as much. And so that’s why you’re, you’re not seeing it there.

So, so I think what we’re seeing in terms of what’s happening from a real world evidence. If you look back from an implementation science point of view and understand what are the enabler for them to have happening, even at the pilot level, there is clear factors that contributed to it that we must learn from.

So, so the scan is not just about what are we doing, but what is the pattern that we’re seeing that we can learn from, that we can use to scale to other places that don’t have it.

Dr. Tania Tajirian: Exactly what you’re saying, Angel. One of the harsh truths I’m gonna say from the scan was exactly that. What we were suspecting that most AI activity in Canada is concentrated in urban and resource rich environments.

And out of the 152 initiatives, fewer than 10% occur in community, rural, northern, or public health. I think public health, we only have three. And that imbalance risks deepening those existing inequities. Again, I think equity, we all know it’s not a side project. It’s gonna be all about smart governance. At good science, every data set is gonna be used to train.

AI must reflect the diversity of the people it’s meant to serve. And I think our national scan reminded us that most transformative innovations are those that bring care closer to the people who historically have been left behind. This is the work I’m passionate about because I work with patients with severe mental health issues.

And I’m just gonna say it’s interesting. As an example, I just created an AI video avatar. I went down the dark web and I was really impressed with what I was able to create freely, right? And I showed her to leadership and I was so proud. Guess my 12-year-old daughter. What she told me when I wanted to show her, I’m the cool chick.

She turned to me and said, how many trees did you kill to impress people? And it really made me reflect, first of all, oh my God, I’m just learning about what AI is doing to the environment. Like the conferences, where is a 12-year-old learning this? Guess where?

Katie Bryski: TikTok?

Dr. Tania Tajirian: TikTok, yes. So you reflect about information sharing what we are doing and, ugh.

The good and bad that’s coming.

Katie Bryski: I mean, we laugh that it’s TikTok, but really it’s that same idea of people sharing ideas across each other and it’s, it’s everyone contributing and collaborating.

Dr. Tania Tajirian: Yes. And the younger generation, that’s where their information is. So like there’s so many different layers. I think we can spend 16 hours or 18 hours talking about this.

Katie Bryski: We’ve talked throughout this conversation about some of the skills that are going to be increasingly important for leaders as AI adoption and implementation continues. I’m wondering, based on the trends that you saw in the scan, what you’ve observed in your own work. If you had a piece of advice for emerging professionals, what would you suggest to them in terms of experience they should try to get, or a skill that they should try to build to meet the challenge of the future?

Dr. Tania Tajirian: I loved working with the emerging leaders in this project, and I think, do you remember Shelagh, in the Fall symposium? Everyone stood up. It’s like the, the, our bright future. So my advice is gonna be simple. Start where the system hurts most. Don’t chase the buzzwords and fix problems that matter.

I’m gonna really continue emphasizing problems first. Even a small evidence-based improvement beats a beautiful slide deck. I’m kind of like, this is, I’m conferenced out. I’m slides, I’m gonna say, and you need to learn just enough technology to ask sharper questions. I went through, uh, completing my master’s in health informatics from University of Waterloo.

And guess what? A lot of that was just to understand the right language. And the other thing that they have to do, they have to stay grounded in empathy. Like I can’t emphasize and enough how important that piece is and understanding that innovation is a team sport. So build your village. I always kind of say as you, you move through your career, build your village of.

Privacy experts, they’re data scientists, your clinicians, and most importantly, I’m gonna say your mentors. And that’s something I’m so passionate about. Angel knows, like we have the cool CMIO chicks group, but I also provide half an hour coffee chats from all of these fantastic people. And usually it’s women, which I love for half an hour.

Coffee chats, virtual and just, uh, I’m gonna say for senior leaders, I don’t want people just to mentor. Please, please sponsor, give those emerging professionals protected time, visibility, and credit. And again, like just in our AI action group, emerging leaders were not just the note takers, they were the co-authors, and that’s how we are gonna build the future.

Dr. Angel Arnaout: Totally agree. In terms of starting with the problem, and maybe this is, this is what I would say to emerging professionals. Don’t wait for permission to get involved. Just the fact that if you understand some aspect of patient care, if you understand some aspect of workflow design or, or even the, the culture quality improvement, then you already have much of the perspective that a lot of AI projects desperately need.

And I think really getting involved in conversations, joining working groups if you can, attending conferences, reading the literature and ai. That’s how I started. And yeah, you know what, in medicine, you, you always feel like you need a little bit of formal training before you embark on a new direction in your career.

And so I needed to do that for my personal confidence because that’s what we do in medicine. You, you go into a new specialty, you get a new fellowship, you, you pass another board exam or you, you get another degree. And so I needed to do that. And so if you are gonna do that, maybe AI literacy is important, but really not about coding.

It’s almost like computational mindset. What are the right questions to ask and how to interpret the outcome? That’s what I call a computational mindset, asking the right questions and interpreting the outcomes accordingly. And so it’s really not about coding, it’s, it’s to question the right way. And ultimately, I think as Tania said, also, you know, don’t be intimidated by the jargon really.

It’s overwhelming in the beginning, but at its core it ultimately, AI is about using data to drive better decisions. And that is the only reason that I got involved is because I saw that there was disconnect between information and decisions and data and decisions. And you, you don’t need a PhD. I thought I needed a PhD, but you don’t need a PhD to get involved in this space.

Shelagh Maloney: I love that it sort of comes full circle and some of the messages we’ve heard from other leaders in this space around don’t wait for permission to get involved. And that’s a nice way to sort of end it. And so we started with a question that we always ask. We’ll end with the question that we always, always ask, and certainly we’ve been asking for our 50th anniversary, uh, episodes.

So in one word, what does digital health mean to you? Tania?

Dr. Tania Tajirian: Okay, so one word. I’m gonna say enablement. Enablement because fundamentally I believe this digital transformation that we keep talking about is about unlocking problems, not just deploying tools. So my favorite word about this is enablement.

Katie Bryski: Angel, how about you?

Dr. Angel Arnaout: Uh, so liberation for me, and when I talk about liberation, I’m talking about liberation from low value work. I really think that this is, um. A hope that I have right now, we’re not doing it that great right now. Technology is a burden, but if we do it right, technology and AI can actually liberate us, allowing us to do what we actually wanna do, allowing us to have more control, more agency, allowing our patients to have more agency, allowing our systems to adapt and learn and make smarter decisions.

So I think it’s about releasing or liberating us from the parts of the jobs that we don’t. We aren’t actually needed as humans or we aren’t actually valued, and so that we can focus on the parts where we actually shine.

Katie Bryski: And I wanna thank you for your part in building that culture and building up our conversation on the show today.

I think we’ve really seen how collaborating with other perspectives, building on the work that came before as you built on the winning conditions per AI, sets up a really strong foundation for success. And I’m excited to see what’s built on the work that you’ve done. Thanks again.

Shelagh Maloney: Thanks so much everyone.

That was a great conversation.

Katie Bryski: Yeah, I appreciated the opportunity to be able to build on the conversation we had previously about winning conditions for AI. That was a nice natural progression from what do you need to have in place to what is actually happening across the country.

Shelagh Maloney: And you know, it struck me when Tania talked about, this is potentially the first national view of what is going on in AI in a clinical setting. And she mentioned this, but it’s probably worth repeating. I know this is publicly available data, so there might be other things that are going on, but. The team that was looking at finding these resources wanted to make sure that it was, people could track them and find them.

And so I’m, I’m curious about what the denominator is. And even since that report has been published, and it’s only been a, a couple of months, more people have come and contacted us and saying, oh, we wanna add this one, we wanna add this one. So it is very much a, a living document. And so having that resource for us to share and those contacts to say, oh, you know, here’s a.

AI implementation in a community care setting, that might be helpful for me to make that contact and find out what worked and what didn’t.

Katie Bryski: It would also be interesting just thinking, like looking at trends over time. Like, I don’t know if there’s an ability to filter the data this way, but if you could see, maybe there’s a turning point when you do start getting more AI in community care or maybe a different type of AI takes off.

Shelagh Maloney: Yeah, that’s a very good point. And, and you know, we always say you can’t measure it if yet, or you can’t manage it if you can’t measure it. And, and just, but yeah, doing a state of the nation and, and you know, hence my question around that, those pilots. Is the fact that we’re still in pilots just because most of these projects are new, or three years from now, are we still gonna have 60% of our projects in pilot?

It’s a good point, right? Where are we in the evolution of ai? Angel made a great point around the infrastructure has to exist. Most of the projects are acute in acute care sector, and you have to have the data, and you have to have all, all that infrastructure that supports that. It, it’s an interesting point in time.

Katie Bryski: It aligns really well with what was found in the winning conditions for AI report.

Shelagh Maloney: The other thing that I thought was interesting and I, there’s a couple of pattern recognition and it was kind of interesting that this is what this is and how do you look at this? And I think as somebody like Angel who, you know, was practicing medicine and she was one.

Patient at a time changing their lives for sure, but then recognizing that there’s something that I can do and make a system impact. And so it, it was really, that struck me. And, and you know, Tania’s comment, and we’ve heard this lots of times, right? People process technology in that order and, and that end user, whether it’s the patient or the, or the clinician, involve them and bring them in.

That intersection of specialties, that balance from everybody and getting input from everybody’s perspectives and, and building on each other’s strengths. That’s so about what we do. Even health informatics, we talk about that three-legged stool, right? Information technology and information management and, and clinical information.

It’s all, it’s all together.

Katie Bryski: I’m hearing the letter from Steve Huesing that you read on the first episode of the season Echoing. It’s, it’s the clinicians and it’s the data folks, and it’s the health administrators, and it’s the policy people, and it’s the patients. There we go. I know it could go on for a lot longer, but let’s end by reminding listeners that everything we talked about in the show today, the AI in Action Environmental Scan and the winning conditions for AI powered health care  report are available on the Digital Health Canada website and are linked in the show notes.

So you have plenty to read and reflect upon before our next episode next month. Until then, thank you for joining us on Digital Health in Canada, the Digital Health Canada podcast. Thank you for listening to today’s episode. Digital Health Canada members can continue the conversation online in the community hub.

Visit digital health canada.com to learn more. Be sure to subscribe to the podcast to get new episodes as soon as they’re available and tell a friend if you like the show. We’ll see you next month. Stay connected, get inspired, and be powered.