Getting Down to Business with AI Implementation

This transcript was AI-generated and human-corrected. It may contain minor errors.

 

Julien Billot: AI at the end can do two things. One is prescription, and the other one is prediction.

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

Shelagh Maloney: I’m Shelagh Maloney.

Katie Bryski: AI has incredible potential to transform care, but what does that look like in practice, and what is the potential value for health systems and organizations? Today, we invite two leaders to explore the rapidly changing AI landscape, and how leaders can support effective implementation.

And we’re thrilled to welcome to the podcast today, Julien Billot, Chief Executive Officer at Scale AI, and Matt Gallagher, Director of Business Development, Health Division, at Airudi. Welcome to you both. Thank you for joining us today.

Shelagh Maloney: So maybe I’ll start, and if you’ve, uh, listened to the podcast at all, you know the very first question we ask, and one of the things that is most popular question that we ask with our listeners, we ask each of you to describe your career pathway.

People are always interested to know how leaders got to where they are. So Julien, maybe we’ll start with you, and maybe just let us know where you started and how you ended up being the CEO of SCALE AI.

Julien Billot: Well, you know I have a lot of gray hair. You cannot see that, so I have a long career already. Uh, so no, basically I began in the telecommunication industry.

I graduated as an engineer, then I decided to join, at that time ca- called France Telecom, but then Orange. So I joined the mobile industry very early. I was chief revenue officer for Orange for some years. Then I left the telco industry that I did for the first 13 years of my life to go to the media industry in two parts.

One for Lagardère Group, so traditional media, you know, the Elle brand, and, uh, for the Yellow Pages industry. And that’s why I came to Canada back in 2014 to be president and CEO of the Canadian Yellow Pages, so Yellow Pages Group, which I did since mid-2017. And then mid-2017, I decided to go to a very different space, meaning I was fed up to being disrupted.

I decided to be on the disruptor side, and so I decided to join the AI industry, uh, in 2018. Uh, first of all to lead an acceleration program at HEC Montreal called the Creative Destruction Lab in Montreal, and nextAI, an incubation program for startups in AI. And then beginning 2019, joined Scale AI to be the first employee, basically, and to be a CEO of, uh, Scale AI, the Canadian AI supercluster.

Katie Bryski: Seeing as early to, to be scanning ahead and seeing the influence that AI would have, was there something in particular that drew you to it at that point?

Julien Billot: You know, it’s funny because a lot of my friends who’ve been in the, the mobile industry or internet industry back in the ’90s went to AI very fast.

Perhaps we can recognize the impact of new technology. Do you know, in fairness, in mobile, nobody believes in mobile. When I was at France Telecom, everybody were on a fixed business, and only young people like me who had nothing to lose went to the mobile industry. So that’s why we had the unique opportunity to build our career.

And I think AI is a bit like that, you know. A lot of people are still thinking about digitalization, not AI yet. So perhaps we were more, I don’t know, used to embrace disruption and go to what’s, uh, what will be the next disruptor in the market.

Katie Bryski: Matt, how about you?

Matt Gallagher: Mine’s not as super exciting. I’m a, I’m a Xeroid.

So I started, uh, my corporate sales career at Xerox in downtown Toronto in one of the Bay Street territories, and had a absolute blast for three years. Learned what I liked and what I didn’t like about sales, and ultimately, eventually what I wanted to get into, which was more on the technology side. So I actually joined, uh, telecommunications as well with Telus, and I got to actually do emerging technology.

So we were looking at M2M. We were coining cloud. We were doing all the really fun stuff. It was 2014, and, and there was a lot of exciting technology at Telus. So I got a really good indication on the data side that data was the next big oil, and you know, we’re looking for refineries. And as I got into healthcare in 2017, I started to realize the massive impact this data may hold one day.

And ultimately, trying to build rules-based systems in healthcare for five years, it was incredibly complex, and I think we needed something that could get us over that complexity. Along came AI. Well, I got in with a small startup, and I’ve been working for s- startups ever since, and when I saw the opportunity at Airudi to be able to be on the forefront of AI technology within healthcare, I had to jump all over that.

And ultimately, that’s been a great year so far.

Shelagh Maloney: It is kind of interesting, Matt, ’cause you said AI addresses complexities, and so many would sort of suggest that AI causes complexities. So it, it’s sort of an interesting conundrum. And, and Julien, like, my question for you is, can you describe Scale AI? What exactly does it do?

Julien Billot: Basically, we were born in the end of 2018, uh, from an initiative from the federal government of Canada, created the Supercluster program. Five superclusters were created with a task in view, which was to ma- go innovation, not only in research, but really in commercialization. So we are industry-born. Uh, we decided at Scale AI to create, to pitch, uh, actually about introducing AI in business processes.

And by doing that, and the end is very important, creating an AI industry focused in, uh, delivering solutions to introduce AI into business processes. So we exist in 2019. We are a funding agency, that’s… We are not an investor in the sense that we give grants to people, but we have already funded more than 200 projects, some of them with Aurudy, obviously.

We are very happy to have Matt here because we fund a lot of products for Aerody. Uh, we have granted, uh, close to $300 million already in AI projects. Global value of projects being around $1 billion, so every dollar we grant, industries is investing $2 around us. The condition for us to invest is really that there’s an IP creation, a intellectual property creation in Canada, and that IP stays in the one delivering the service.

So let’s say if you have an adopter, the hospital, typically, uh, because what about health, uh, we want the IP not to stay in the hospital, but to be in the one building the solution for the hospital. So the IP can be used across the ecosystem. Obviously, we’ll be part of the new AI strategy to be announced soon by Minister Solomon, and our mandate is really to help adoption on one side, and on the other side, build a, a full Canadian AI ecosystem.

Shelagh Maloney: And so what percentage of your grants are in the health space?

Julien Billot: That’s a great question because as I said, our focus is really business processes. So health has business processes to solve, as many other industries. We have invested across nine different sectors, health being one of them. So I would say around 10% of our grants were in the health industry, but I want to be very precise about the type of thing we fund as project in health.

So we will not fund typically, uh, drug discovery or this type of thing. That’s not the type of project we fund. We really focus on improving business processes across the health industry. Management of resources can be, um, management of products, can be procurement, uh, management of assets, like, uh, surgery rooms.

So that’s the type of thing we fund, so really aiming to improve productivity for the health tech sector.

Katie Bryski: And Matt, I’m wondering if you can tell us a bit about Airudi and where you fit in this AI ecosystem.

Matt Gallagher: We look at ourselves as a pioneer, ultimately, in the artificial intelligence space. We rank among the top 10 AI companies in Canada, and ultimately, the expertise that we bring to the table I think is unparalleled.

What we do specifically, we’re in the workspace and operations optimizations, so we want to make improvements to processes that are more or less complex, maybe very manual, adding a lot of inefficiency to healthcare, hospitals. But, you know, our technology’s already used by 5,000 companies in North America, and ultimately we’re growing.

Um, Europe has been a very big play for us as well, so we’re interested in that. But ultimately, our expertise is in artificial intelligence and developing real solutions. So it’s not only rooted in this area. We find ourselves more on the outlook of the whole organization because we, we don’t want to be looked at as just a simple technology or a Band-Aid that can fix a, you know, one-time problem.

Our expertise within the, the processes, the very complex manual… In he- healthcare, I want to just say very manual challenges that these organizations face daily. This is where we choose to partner with, because ultimately we look at ourselves as more of a part of that organization, very much in the strategy side.

Katie Bryski: It’s interesting ’cause we’ve had a number of podcast conversations about AI, right? And largely very kind of future-focused, looking at the potential about what it could be in the future. But it sounds like both of you are really focused kind of the nuts and bolts, what’s happening now, how do we turn that from vision to reality.

I’m curious about some of the challenges you may be facing as you do that. Like, it’s, it’s a dynamic space. What sorts of things do you come up against and, and how do you sort of navigate around them?

Julien Billot: Tricky thing we have is to explain to politician actually there are different type of AI. You know, everybody’s talking about LLMs, the frontier model, large language model, because of course they are very critical for sovereignty, and that’s very nice to talk about them.

That’s also the one who threatened the most democracy and whatever. But there’s multiple type of AIs, and we are really focused on industrial AI, applied AI, which is much more modest than LLMs, not the same type of computing power, but is bringing real productivity improvements and very short-term improvements.

So one of the challenges to explain to everybody that when we’re talking AI, we’re not only talking about ChatGPT, OpenAI, or Anthropic and Mythos and others, we are really talking about different types of AI, different technologies that are much more frugal, but very critical to improve business processes.

And that’s part of the challenge we have because the type of AI we work on is not raising at all the same type of regulation issues. Even if you think about in the, in the health space, meaning improving surgery or improving, uh, if a project we funded for would be all the, um, ambulance drivers with Urgence-Sante, that’s not threatening democracy or, or privacy or whatever.

It’s really about improving business processes. So that’s part of the cha- the challenge we face is explaining that.

Matt Gallagher: That’s exactly the challenge that we have as well, dealing with all of the other AI solutions that are out there, getting lost in that crowd, and ultimately trying to bring the conversation back to reality of where is the value today and how is this best applied to the organization or the hospital?

I think a lot of the time when they hear AI, yes, it goes to a traditional chatbot. It’ll be in a some sort of that type of a AI, ChatGPT, uh, even with enterprise systems that are out there today, I don’t wanna say any of the names, obviously, but this level of AI isn’t to the complexity of what we think that we can get into with our platform.

Katie Bryski: Yes, that’s an interesting point. How do you speak about something that is quite technical and complex to people that may not have that grounding in it, right? Like, how do you speak AI and all of its various nuances to just everyday people?

Julien Billot: Actually, you should not speak AI. You should speak business issues, and I think that’s all the thing, meaning AI is a tool.

You talk about hammer, you talk about how you can use a hammer. So you think about business processes. What do you want to deal? What, what do you want to change as your business process?

Matt Gallagher: That’s how we, we approach it as well, more as consultants. We very much wanna hear from the customer. You know, where is it that is the most, you know, important to make improvements on these processes today?

We want to have those scoping sessions. We wanna understand, and we wanna make that investment, whereas I feel there’s a lot of- Hesitancy because it’s unheard of. A company as, you know, a, a software company would wanna do that and make that type of investment.

Shelagh Maloney: You know, it’s interesting. I was on th- just this afternoon, and it was about sort of the health workforce and, and digital health in particular.

But we talked about AI and, and Julien, I take your point very well, like AI is not AI is not AI. And one of the things that came out was it’s being perceived now a little bit, especially in healthcare, as the shiny object. And the comment that was made today was, we’re still in health not getting the foundational pieces quite right, and so we can’t be chasing these shiny objects.

And, you know, we published our winning conditions for AI in healthcare, and clearly the folks who are doing it well and doing it right in this space are using AI to improve strategic issues. You know, your comments about process improvement, that’s the foundation of what we wanna do. It sounds like that’s one of your biggest challenges, is to convince people that AI has more potential than just being a chatbot.

What other challenges do you see when you’re talking to folks in this space and trying to get them to understand the potential of AI?

Matt Gallagher: I mean, there’s several challenges. The larger ones would be you have a lot of it’s not broke, let’s not fix it. You have a lot of companies, well, hospitals, that have been jaded trying to chase shiny objects before.

There’s all of that, and then ultimately the change management piece because the complexity of any software, any technology to be adapted by an organization, it has to be accepted. And ultimately to be accepted, there has to be an education piece because it, there’s a lot of fear that goes in AI, even from the executive level.

Because very much to Julien’s point earlier, it’s not there to replace, it’s much there to do the inane, the mundane, the boring, the, the things that humans weren’t really meant to waste their time on. I feel it as it’s, it’s there as a tool to take away the boring, as it were, and ultimately freeing time for the competency of that profession, doctors to spend more time on patients in surgery rooms.

So the challenges unfortunately today are, you know, within processes. Everyone, especially in a hospital, has no real understanding of what it is, how do we approach it, what’s the process? So it’s a lot of, yeah, a lot of education needs to be done. And I think with healthcare, the good thing is usually when an organization picks up something that’s very successful, it’s talked about, and ultimately it’s adopted.

So there is a positive light to that. We need those game changers, the, the disruptors, the ones that wanna make a difference, the ones that wanna do something. Ultimately, those are the partners that we look for

Julien Billot: Well, something very, very important that Matt said is, first of all, health is not very different than any other industry, meaning health has asset constraint, has management constraint, has resources, human resources constraints.

So, uh, every, uh, meaning every industry is facing the same challenge. So it’s also accepting that solution that were built not for health can absolutely be applied for health. Airodi is a good example, meaning the first project we funded for Airudi was for the Port of Montreal about optimizing the use, the, the human resources for the Port of Montreal.

And so obviously, then optimizing human resources for the health system, nurses, ambulance drivers, is a bit in the same category. And demand forecasting is typically one of the key issue the health system would have that other industries have, which is, in that case, demand forecasting, okay, how many people are going to show up in emergency services today and tomorrow?

Because you need to have the right staff at the right time. That’s something very important to make people understand. We’re not talking here about destroying jobs, and definitely not in Canada. We’re talking about better optimizing assets and existing resources because there’s a need to deliver more service to customers.

There’s a need to decrease delays in surgery, decrease delay in oncology, decrease delays in radiation, have more people in emergency services, and that’s exactly where AI can help, by better optimizing processes, better allocating resources linked to demand, so better forecasting, basically, the type of demand you would have.

That’s not different, and something we try to say is, you know, of course, every sector is different, every company is different, every organization is different, but at the end, you all have the same processes. You all have to optimize your assets. You all have to optimize your, your resources. You all have to understand your demand.

You all have to organize your production. You all have to manage your human resources. So that’s exactly what we try to explain is at Scale AI that’s the type of projects we try to fund. And typically because we fund all these type of projects across sectors, then you can have companies like Airodi that are able to build solution across verticals to deliver on these issues.

Katie Bryski: I have a question, though, just going back to some of the change management, and the constraints, and resources and time. Like hospitals in particular, they’re stretched, and I would imagine the prospect of coming in and changing processes quite significantly with an AI tool might seem a bit daunting, right?

It’s like there’s this upfront cost that ultimately would lead to savings. I’m, I’m just curious about how you kinda get people over that hump, the cost of change versus the cost of the status quo.

Julien Billot: When we select projects, typically we try to have an answer to that. So we always ask organization, “How are you going to manage the change?”

So that’s really something we embed very deeply into our way of selecting projects and looking at project, which is, how do you organize your change management? Because we know if there’s no change management, the project will not be happy. So perhaps, Matt, you want to explain how you of- do the change management in the hospital?

Matt Gallagher: Well, I was hoping to say there’s a very crazy stat with AI projects, right? Like, how many fail before they actually end up in the end up zone where people are using them And I think it’s something about 75% of those projects fail. How do we get over that hump is change management is, is deeply rooted with inside of our process, but from both ends, from the Aruti side as well as from the hospital.

Because, again, adoption needs to be there. We’re doing this throughout the projects. Ultimately, the projects we select are very similar to h- kind of how Julien would look like with Scale AI. We want… There has to be immediate value. We need deliverables, KPIs, so that we can ask the hospital, “Is this right?”

And ultimately, so that when we get to the end, everybody is happy. It’s not just one area of the hospital. And how do you do that best? Well, the top may think the project goes one way, but ultimately when you get it on the floor and the users, the project completely pivots and it goes another. So again, all of that is kind of brought into our process.

We invest again in heavily in healthcare to this foundational problem because we want to bring it right down to the foundation. And the end goal is a hospital or an organization that is that level four of AI readiness, that level five even of AI, AI readiness. And ultimately, we’re going to build that scope and vision with as best we can with that partner.

But yeah, we’re not just looking to be a band-aid. We want to deep dive and figure something very complex out that we can, uh, be of long-lasting value.

Shelagh Maloney: You know, it’s interesting. One of the things, like change management, we know, and, and people need to come along, they need to understand, and they need to know what their role they play in.

And one of the, the studies that we recently completed around, in particular, this was around, again, the digital health workforce, and one of the key findings of this report was that executives, often leadership, they’re not comfortable enough with technology, AI, and they are often a bottleneck just because they don’t understand, and they…

And so I’m curious, are you finding that? And then how do you overcome that?

Julien Billot: At the end of the day, the question is not about technology, it’s about business changes. And that’s why, meaning, I’m s- I was, uh, said initially, it’s not about the tool itself. As an executive, you need to think about your business and how to change your business.

AI is a tool that can help you change incrementally your business or radically your business, and that’s the way to think. Usually, you have to think the reverse. So, okay, I have business processes. Some can be improved, but if I had a magic wand, I would totally transform them. AI can be this magic wand, so the thing is, you have to think as a business leader about the impact and the possibility AI is opening.

But AI in itself is just a tool. That’s… AI is not the question. The question is, if you had something enabling you to better predict, typically your sales or your demand, what that would change for your business? And that’s exactly the type of question. So executive need to think not about the tool, but think about, okay, what is impeding my business right now?

If I could change something on my business, what that could be, and how that would drastically or incrementally change my business. And then you can ask, okay, now let’s look at the tool, but tool is only a, a, a second level question. The, the first question is really about business change. So as a leader, your thinking should be, okay, how can I improve my business?

Some things can be improved by AI and some others not, but first of all, what can I improve in our business as a leader?

Matt Gallagher: From our side, everything’s Julien said and more. I mean, we need leaders that think that way, and if they don’t think that way, it’s a harder challenge. It’s a longer, tougher hill to climb, but it’s not impossible.

The rapport needs to be built whichever way we do it best, whether it’s comfortability, showing solutions that we’ve done, uh, with other healthcare organizations and how they can impact. And something that’s simpler as opposed to something that is maybe a new type of a solution or a use case for that hospital.

That may be a way to go. But I mean, I just had a really fantastic meeting with a, a larger healthcare organization, and it started right at the top with the CEO. And, you know, she sees that there’s something positive that can be done, um, by the, the examples we’ve done, and we sat in a room with all of the other leaders, and we tried to think of something complex and unique for them that’s really, really challenging today.

And it’s as simple as that. And, and ultimately from that meeting, we’re now, we’ve come up with a very challenging problem, and ultimately we’re all about trying to solve it with them, and I think that’s the partnership we need.

Katie Bryski: So just questioning, you don’t have to necessarily get into specifics or details, but just going back to this idea of AI as a tool and, like, looking at business problems, what do you do when you find maybe there’s a problem an organization has come to you with, but AI is actually not the answer to that particular problem?

Are there, I guess, themes that turn up, like types of problems that… ‘Cause I think people tend to look at AI as a bit of a silver bullet right now, right?

Matt Gallagher: So from our end, it’s data readiness. That’s a big one. That’s a massive one. Data readiness is, you know, we’d love to do this, but we have no data, or we have no infrastructure.

There is the problem in itself, and ultimately, how can we help with that? Well, it’s never a walk away, but I think a lot of work needs to be done before we come to the table. And ultimately, we’ll be very upfront about that. You know, we’re not there to do things outside of our core competency. And I think, you know, Julien will probably attest to this, there’s companies that want to solve problems and use AI, but they’re, they’re just not ready.

Julien Billot: You know, AI at the end can do two things. One is prescription, and the other one is prediction. That’s what AI can do. So anything around prediction or prescription is something AI can do But something which is not prescription or prediction, AI cannot do. So if you think about LLM, LLM is just predicting what could be the answer.

That’s what they do. So if it’s something which is not prediction or not prescription, AI will not be able to solve that. So not every business process has prediction or prescription issues. Uh, if you want to improve, uh, the atmosphere in your team, that’s likely not being prescription or prediction based.

Obviously, everything which is better understanding what could happen or, um, everything around what could be the right action to take, that’s usually something AI can help. But then obviously, depending on data, depending on a lot of different things. That’s why a lot of companies, including the health sector, begin by demand forecasting, because demand forecasting is really something which is not heavily dependent on internal data.

Or if you need internal data, that’s mainly sales, which usually you have as a company or an organization, but it’s really dependent on a lot of external factors. Meaning we know, I think there, there was a study for the, in the, in the, in the Quebec media recently, when c- when the Habs are, Canadian are playing, there’s less people in the emergency rooms.

So, you know, that’s a demand forecasting tool. If you have c- Habs game, you will have less people in the emergency rooms. Uh, or if the weather is good, you have less people. If the weather is bad, have more people, blah, blah, blah. So you can really, um, demand forecasting is usually the first thing you can improve because that’s really, you have usually data available.

But not everything is based on demand forecasting. Not everything is made about predictive maintenance.

Matt Gallagher: He says demand forecasting like it’s very simple. I think it can be very complex.

Julien Billot: Yeah, because it’s multifactorial and, and very dependent on multiple factors and all, all different per organization.

Shelagh Maloney: So maybe, uh, one of the last questions, you’re in this space, lots of opportunity, and I love…

Can’t remember which one of you said that healthcare is not a unique sector. Um, I think healthcare, we like to think that we are, but I really, like, you’re absolutely right, especially when you bring it down to foundational and business processes. It’s not unique. So I’m curious, what advice would you give somebody sort of either as a leader trying to tackle one of these big problems or maybe as a startup organization trying to get into this space?

Julien Billot: You know, one of the opportunity or danger is, you know, AI as any tool, it’s hard to apply on an existing process. So when you want to apply AI efficiently to solve a business issue, you have to rethink your business issue. You know, usually if you, if I take an example, when you try to implement a software on an existing process, that fails because you need to adjust to the software.

You need to change your process to actually adjust to the software. That’s exactly the same for AI. Meaning, if you try just to say, “My process is great, I just want to improve it incrementally by plugging it,” that will fail. So you have really to rethink your business issues and think it differently by saying, “Okay, there’s a new tool, new opportunities.

How do I completely rethink my business?” And that’s the hardest part because usually leaders, you do something, you want to improve incrementally by some percent, you’re happy because you did 5, 10, 15%. But perhaps the opportunity is 50% improvement, 60% improvement. But to do that, you have to totally rethink your process around the tool, and that’s exactly what you need to do, and that’s the harder thing as a leader is thinking disruptively.

Matt Gallagher: Yeah. I mean, that’s a great point. That’s kind of, uh, fundamentally what we try to do is that whole idea of finding the problem, and now let’s get to the root of it, and how do we solve it? We’re looking to s- best optimize that problem. And so it’s just thinking that way. I think it- that’s the, the heart of it.

If we could think, if we could all think that way, I think there would be a lot of improvement that could be had, um, especially with the technology where it’s at today. My advice would be pick a real problem. Pick something that’s fundamentally a problem.

Katie Bryski: And as we close out the conversation, we also end with a consistent question, which is if you had to describe what digital health means to you in one word, what word would you pick?

Julien Billot: Better use of our taxes. No, the reason I’m saying that is better use taxes because we know, meaning the health system is going to cost more and more and more because just of the aging population. And so improving productivity is not, uh, is something absolutely imperative because if not, we’ll not be able to treat correctly Canadian people, Canadian citizens, Canadian people.

And so that’s a key thing to at least maintain the level of what we need to provide at a relatively affordable cost. If you are not able to do that, we all know that what that mean because the… can just increase taxes, and that’s not sustainable long term. So it’s really about digital health for me, it’s really…

well, at least this digital health, the one we are talking about, which is improving logistics, human resources. In this one, it’s really do more with the, does the amount of money available in the system.

Matt Gallagher: Yeah. I mean, one word? One word I would say, um, proactive or efficiency. Efficiency. One of those two, but I think that’d be a good way to describe what, you know, the essence of, I feel, digital health should be.

Katie Bryski: I’m here for it. This has been such a great conversation, and I think has really taken us through some of the hype that especially I think in healthcare we’re hearing about AI right now, and just really distilling it back down to those basics, right? What is the problem? What is the business process? And how can this tool be most effectively applied as the solution?

Matt Gallagher: I agree. Now, now it’s just as simple as having these conversations with healthcare leaders.

Katie Bryski: Well, thank you for joining us today for this conversation, and hopefully it sparks more inspiration, more conversation with other healthcare leaders. It was a pleasure to have you. Thank you.

I love that we can do many different episodes about AI, but it’s a slightly different take and perspective each time. I really, I really did appreciate that opportunity to kind of just get back to, again, fundamental, what is the problem? What are potential solutions, and how can AI be applied as a tool?

Shelagh Maloney: I, I was just gonna say the same thing.

I think that was one of the best stick-to-the-fundamentals, get-back-to-basics conversation about AI. And, you know, we had this conversation, you know, we’re updating our core competencies and, and the foundational structures of things are the same. Whether it’s like, you know, strategic thinking or project management, the foundation is the same.

The environment in which you apply it or the tool that you use to apply it might be different. But, you know, I love that it’s all about business processes, and there’s lots of opportunity to improve business processes in healthcare. But, you know, one of the things that Julien said at the end was this whole piece about, and I think this is we’re not especially good at doing this, is you need to change the business processes, or U- AI has great potential to improve business processes, but you have to be willing to change and rethink how you do things now.

And so I think that’s one of the things that, you know, rethink a business issue if you really wanna get transformative change versus incremental change, and that’s a challenge, I think, for us in healthcare generally.

Katie Bryski: Yeah, I mean, like, I think of EMRs, right? And the, the general desire to not just digitize an analog process, and yet in many cases, especially in the early days, that is more or less what happened, right?

It’s, it’s a paper chart, but now it’s on a screen. Versus what we’re seeing now, um, including with AI, is, as it becomes actually just a different workflow altogether.

Shelagh Maloney: One of the other things that I really appreciated about this conversation was the fact that at its foundation, health is not that much different from a number of other sectors.

You know, there’s forecasting issues, there’s workforce issues, there’s, like, there’s a lot of things that are the same, and in fact, we can learn from other sectors. Again, I, I just like that reminder. Not anything new, nothing we haven’t heard before, but what, uh, can be applied in other sectors can be applied in healthcare.

And that, uh, I think is a good reminder for us

Katie Bryski: Question, uh, and you may not be able to answer, and that’s fine, but you, uh, mentioned a few times the study of Digital Health Canada has been doing on the workforce. Is there anything you can share about it now that people can look forward to?

Shelagh Maloney: Yeah, so what we wanted to do is we set out to really find out what does the digital health professional sector look like, who’s in it, what kind of skill sets do they have, what will they need in the future?

And so we did a, a lot of data searches through CIHI, and Health Workforce Canada, and CHIMA, who’ve done some work in this area, but then we also had some key informants, so about 21 national, international key informants that we sort of asked them what their thoughts were. And so we actually, earlier today, had a call with those key informants, and presented the first draft of the report and the findings.

So that will be coming out imminently, and, uh, certainly in time for the eHealth conference. And now we’re sort of exploring what are the next steps, what are the opportunities that come out of it? And one of the findings was we don’t really have a good handle on who the digital health professionals are.

And, and I’ll give you two sort of advanced things. One is that digital health is still sort of a, a specialty area, but it is also a system requirement. Almost anybody, if you want to be, whether you’re a bedside nurse or even a patient for that matter, you have to have some sort of degree of digital literacy to function in this environment.

And so that was one of the key findings. And, and AI is sort of accelerating the urgency to, to recognize that. And the other big finding was that it’s hard to quantify what this space looks like. And so we think there’s between 75 and 100,000 digital health professionals out there, but then when you add on clinicians, and governance people, and policy experts that have to have some degree of literacy, like, the numbers scale to maybe between 1.2 and 1.5 million.

So it’s a, it’s a big number, but we need to get better at quantifying that number. So yeah, it’s a, a really inter- interesting piece, and I think it’s an important piece to move us forward.

Katie Bryski: Well, people can look out for that on the Digital Health Canada website, uh, I’m sure very shortly in the weeks to come.

So until then, thank you very much for tuning in, and we will see you next time right here on Digital Health in Canada, the Digital Health Canada podcast. Thank you for listening to today’s episode. 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.

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