AI adoption in hospitals - Michael Page (Unity health)

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Hi Michael, it's great to have you with us today.

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Yeah, thanks for having me.

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It's wonderful to be here.

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If you could tell us a bit about your childhood and go as in-depth as you'd like to about

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the past to where you are now.

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Yeah.

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Where do you begin with childhood?

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I think maybe, you know, I tell some people this and maybe it's surprising, maybe it's

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not because maybe I like I present a certain way or maybe there's some assumptions of

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who I am.

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Grew up in sort of this like suburban countryside and sort of a lower income working class family.

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So I'd say childhood was really, really great filled with creativity and a lot of time outdoors

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and not a lot of time in technology, which I think some people sort of assume.

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I might be the kid that's like taking apart radios and try to figure out how to put them

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back together the same as like my bike or something else around the garage.

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So probably the most impressionable thing is being the first person in my immediate family

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to go to university.

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And so, you know, education was really important.

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It was hard to navigate that as being, you know, kind of that outsider.

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My parents kind of, you know, steering me as much as they could, but more things being

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unknown than known for them.

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And you know, that took me to an incredible path of, you know, everyone says like follow

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your passion.

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So I started my career in the arts and maybe we could talk a little bit about that.

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But where I am today is I ended up going back and doing a master's.

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I now work in technology and AI of all things.

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And for over a year now, I've been at the Ivy Business School at Western University

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now teaching.

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So my kind of like full circle moment is being this loved and supported working class kid

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to be the first person to go to university to now teach in a university, which I think

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is just, you know, if not through education, then it has to be Canada's the only place

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where you can make that happen is what I believe.

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That's amazing to hear.

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So let's go back to your time in high school.

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And how did you decide to do a bachelor's in arts and talk to me about your experience

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there and then your transition to working for the Toronto Symphony?

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Yeah.

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Great question.

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So I end up becoming sort of this like, you know, kind of now comfortable in crowds and

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can sort of speak my mind freely.

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But that's not where I started in high school.

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I think it was for a good period of time, you know, by sweating through my shirt afraid

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of my own shadow a little bit too much.

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But at some point in high school, I became kind of the theater kid.

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And through, you know, those classes, those opportunities, we had like an improv group

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and doing some plays.

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I kind of found my voice and being able to talk to people and relate to people and maybe

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also being more confident being another character rather than being myself is probably part of

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the story.

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And so when I was choosing universities, what's interesting is I applied in sort of like a

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really eclectic way.

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So on one hand, there was like fine arts and sort of bachelor arts programs.

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And then on the other hand, were business and technology programs.

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And I had actually committed accepted to a different university to do a business and

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technology.

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I think it was a B.com, but I don't remember.

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And on the last day when you can decide or would have you back then, well before email,

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I got the acceptance letter from the University of Toronto to do a humanities degree.

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And on the very last day, my girlfriend at the time was at the university and I decided

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to go to U of T and just sort of pursue the arts and pursue what I love.

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I am only able to say this in hindsight.

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I don't think I appreciate it at the time.

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And there's some brilliant people.

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Like I think this quote is loosely based on something that Hal Jackman has said, where

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humanities degree or the arts teaches you how to think critically.

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It's actually not really about analysis of art in and of itself.

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It's about how are you structuring thought?

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How can you understand what other people believe, think and value?

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And then how do you communicate that?

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So that was kind of my takeaway.

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And I found myself there by circumstance.

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I wish I was probably more intentional at the time, but I did walk away with some skills

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and some purpose and some this growth mindset of what I wanted to do in the world.

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And what prompted the transition to business and then to AI?

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That's a good point.

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So I started off at a few different arts organizations as you've noted.

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So I worked at a theater company in Branson, Ontario that was just opening up called the

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Rose Theater.

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I was there for the opening was like one of my first internships, bounced around at like,

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you know, a children's theater company, an arts service organization for playwrights.

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Found myself at the Toronto Symphony, where the symphony felt like the most professional

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place I had ever worked at that point.

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I'm like very early 20s.

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You know, they've got a board, a CEO, there's hundreds of people, there's lots of revenue

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flowing in.

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And this is the moment where I start to feel and understand and really experience all the

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different, you know, common nodes of business.

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So marketing and sales and HR and in our case, fundraising.

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And how do they function and how does sort of this corporate structure function?

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And, you know, I'll say I was good enough at fundraising and an open an opportunity to

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return to U of T where I would work as an administrator in their advancement office

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and and I'm spending a decade there grew a lot.

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I say it's like, you know, I feel like U of T's played a pivotal role where it's like,

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you know, your first university where you're becoming an adult and then kind of where like

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I actually grew up by by working there for a decade.

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But the transition from all of that into business and technology was very much related to, you

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know, first my like my management mindset at the time.

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And then the second thing, wow, what an incredible opportunity being at U of T talking about AI

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and you know, in the 2010s.

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So the first thing is my management mindset before I went back and did my MBA at Ivy Business

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School was I always kind of kept the mental note of bad managers and the things that they

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would do and we're kind of mentally commit to.

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I don't want to do this when I manage.

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And I kind of got to a point of like, you know, when you're leading fairly large teams

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or you know, really big revenue goals or projects or what have you where I didn't really have

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a great skill set, right?

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Like this skill set based on I should do things, you know, I should not do the things that

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I did not like is not really a strong management philosophy.

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And so I felt like, you know, this is a huge part of my career.

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I'm not done.

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I want to have 10x the impact that I've had thus far.

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And if there's if there's one thing I need to learn, I need to learn how to lead.

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So that's what took me to Ivy Business School and doing my executive MBA there and kind

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of learning and growing.

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The transition to technology happened at the same time and it had a lot to do with, you

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know, the people and the projects and things I was working at U of T.

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I'll just name job for a second.

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None of these individuals will remember who I am, but having the opportunity to get assigned

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to support or be on projects with some, you know, incredible researchers and then now like

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founders like Dr. Raquel Erdison, Dr. Jillian Hadfield and a few others where very early

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days like in the 2010s, I'm learning about autonomous vehicles and learning about that,

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you know, how did they actually learn?

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Why are they learning?

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Where is this going and kind of conceptualizing the future or that these systems could be really,

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really unsafe if they're not managed or we don't think about the ethics and equity is

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really what Dr. Hadfield has contributed to AI and our ecosystem.

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And so this planted the seed in me where it's like, you know, we're seeing tech everywhere.

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I feel like it's the largest growth industry in the world, but being at U of T in that

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moment around, you know, these descendants of Dr. Jeffrey Hinton and where U of T has

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kind of led the world.

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I had in my gut, but I was probably, you know, just surrounded by some incredible people

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where it's like AI is going to be really, really big.

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So the transition to business is, you know, MBA, this in depth kind of understanding of

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seeing research really at the bleeding edge at U of T and then saying, we got to do more

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than just publish papers.

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We don't need more citations collecting dust on a bookshelf somewhere.

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We have a chance as Canada to really own the podium here and let's figure out how we get

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this research out of our really amazing research institutions into companies into everyday life

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so that we can, you know, grow economically.

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We can create more jobs, but that we can contribute to the world in a meaningful way.

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So I joined the Vector Institute and that's been, you know, a wonderful organization that

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really grew and groomed me and taught me so much about machine learning.

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And now I find myself in healthcare where, you know, we're seeing this every single day

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in the way that our clinicians are treating patients.

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So along eclectic circus of a journey, but I do relate this a little bit back to, you

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think to that 17 or 18 year old humanities kid, a lot of this is based there of how can

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I understand what other people value or what they communicate and how can I think critically

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about these things.

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And AI for me is really about that of we have this massive corpus of information.

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How do you take it in?

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How do you think critically?

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How do you exercise judgment or build systems that are effective or safe?

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And so the eye rely on the business skills and, you know, and some of the hustle and

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acumen and stuff like that that I've built up.

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But a lot of this is that arts background of how do we think critically?

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How do we understand what the current context is?

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Does the common startup philosophy that says that you should hire slow and fire fast, do

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you agree with that?

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Oh, that's a great question.

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I like the higher slow.

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So like I think, you know, we're coming off a period where venture back companies got

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incredible valuations.

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They hired hundreds, sometimes thousands of people and really did not have the runway

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to sustainably support that head count.

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The fire fasting, I'd say like, you know, it depends on sort of the context.

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I don't know what the definition would be around that, but I find that most individuals

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are really learning a job.

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You're contributing, but you're learning a job probably for the first year.

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The second year is where, you know, you're moving beyond sort of this binary learning

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like, oh, I was giving a task, I executed the task.

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The second year is when you really understand that context, your internal context, and you

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understand the external context where you might be able to be a top contributor or not.

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Now, all it's that people don't show up, they're late or quality of work, all that, you know,

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aside, but I do take a longer term view, depending if that is a long term view for some people,

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on human capital in particular, because it organizations are weird.

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Everyone is unique and everyone says that they're unique.

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And I don't know that enough leaders reflect on how poor their internal contacts might be

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for individuals to succeed.

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And so I feel like it's around the year mark, sometimes it's shorter, sometimes it's longer,

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but I find that sort of that second year is when people really kind of hit their stride

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and you can really see the value that they bring.

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Have you ever made a bad hire?

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And is there something you could have done in the hiring process that would have stopped

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you from hiring them?

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Yeah, made a bad hire, for sure, multiple times, did not see it coming.

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So I think, you know, interviews are getting to be a little bit more interesting and better,

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but something that I really, really reflect on, this does, you know, follow the murder

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of George Floyd and Candace efforts and truth and reconciliation is, I do think that as leaders,

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managers, people hiring, we need to think about how fraught the hiring process is.

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And this has nothing to do with AI suggesting like who we should hire, what resumes we should

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read, the process has been fraught to hire the person that went to the best school, had

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the best network, had the best reference, it's sort of been coached to do the same.

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And I'll say like, you know, we've taken it in quote, a chance on a few people where the

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interview was clunky, or you know, there was something where we're like, oh, we're not

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too sure, you can't see hustle, you can't see commitment, you can't see drive, and

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you're 10 standardized questions, and the one or two assignments you give someone, or

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the, you know, one to three references that you check.

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So who a person is, you know, you might get that sometimes in an interview process.

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And that's something where you're building a relationship together, and that spark may

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or may not happen.

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But if you find someone that really is aligned with your personal purpose or your corporate

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purpose, and they will literally move mountains for you, that's a great hire.

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And you know, if they're a bit quirky or late or they've got too many cats, who cares?

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As long as they're kind of, you know, really devoted to where both of you can go together.

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Yeah, my kids would say you can never have too many cats.

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I know my son desperately wants cats and dogs.

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We had a dog, and we're now in the like in between phase.

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But yes, if we had 16 cats, he would love it.

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I think people underscore the importance of references.

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And there's a couple of ways to do it, I usually seek references from previous jobs that the

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founders don't provide readily, and I let them know that.

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Or you can ask for 10 references and say you will randomly call three of them.

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Now that's a big ask.

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And the job offer or the investment has to reflect that.

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But you want references where the founder has had conflict as well.

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I think so, and you know, and understanding how people navigate conflict, like sometimes

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it really shuts someone down.

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And other times, someone can take it on the chin and say, you know, thank you.

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I disagree.

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But I'm going to think about this or I'm going to learn from this, this opportunity.

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So I like how you how you navigate that, I think is, is, is quite important.

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I think like, you know, I've seen things where like some companies are doing like mixers or

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actually the hiring team is kind of not involved for a certain, you know, round or phase.

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It's someone on the team is like messages as a person, hey, are you free for coffee?

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That's what I'm interested in.

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You know, if this person for an hour or two hours or as coach, the references can put

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something together for me, that's fine.

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But you know, Karen and Kayla, who you're going to have to work with every single day.

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Can you get along?

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Like is there some sort of animosity or friction or is there actually, you know, some, some

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real bond here for, for whatever reason?

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Is there sort of that that spark of opportunity?

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What is hard, and I do feel like there's some people talking about this, but what is really,

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really hard in AI and in tech is we just have many, many engineers and computer scientists

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that are introverted.

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And so if you look at the traditional scorecard of, you know, character or leadership, education

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experience, whatever, like how we're going to score someone, that individual just might

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be really, really nervous to talk to you or to share in that way.

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So part of like, you know, do we need to rethink this as some of this just like, would you

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be open like we just text over the next like five days of like, you know, I'd love to hear

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your thoughts on this big announcement from, from the video, like what do you think the

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impact would be on, on our work or health Canada just issued updated regulation on AI's medical

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device like what do you thought we can ask that and you know, maybe I've been coached

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and trained and have more experience where off the cuff, I'm going to give you something

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that's that's eloquent, but maybe for that interview to introverted engineer or computer

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scientists, let's just do that over email or do that over Slack or do that over text

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messages or something else.

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And maybe that's the way that we're going to build trust in that relationship.

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Maybe that's the way that I should be evaluating you.

250
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So that's a long way of me sort of saying that like, the world has changed, you know,

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like we're working remotely, very, very often, have our hiring practices changed as as radically

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probably not.

253
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And we probably need to rethink it.

254
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What are your thoughts in remote work?

255
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And you have a similar culture remotely, because a lot of us, a lot of companies are

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mandating back in office.

257
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Where do you fall on that spectrum?

258
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So it's like a working parent hybrid is an incredible benefit.

259
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And I can't say that it's that it's not you mentioned you have kids.

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They're sick.

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All these crazy things that happen with with kids schedules.

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I know a lot of us also have like elder care responsibilities.

263
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So it makes it challenging to be in office and present as much as maybe you would like

264
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to be.

265
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There's the there's the old adage, right?

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Culture eats strategy for breakfast.

267
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So are we really fixated on our corporate strategy?

268
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Or are we really fixated on our corporate culture?

269
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And I think you can build this remotely.

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It is hard.

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It might actually be more expensive than what people think.

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Like I hope that founders are not choosing hybrid or remote because it's the cheap thing

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to do, but that it actually does resonate with the team or individuals or what it is

274
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that that you need to build or why you're building.

275
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You get a lot more accomplished in person, so I do see sort of, you know, community building

276
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this kind of like this culture building happens just a little bit more seamlessly and in person

277
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than it does virtually, but it doesn't mean that it's impossible.

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On my experience and kind of our team right now is you have to put effort on both sides.

279
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So we are hybrid, but we have a lot of people that are committed to like, when are we all

280
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going skating in a few weeks together as a team over lunch?

281
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This isn't dictated by the leadership team to say like, you know, 28th of the month is

282
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you know, skating day and we expect you like, no, like it's a real group of friends and

283
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community within the team saying there's literally a skating rink across the street.

284
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Who wants to go on this day?

285
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Yes.

286
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Do this as a team activity.

287
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So there's real effort on that side to build relationships, build trust, build friendships,

288
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get to know one another beyond the code or the deliverable or the tactic that you're

289
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working on.

290
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In the same time, you know, we've also grown remotely.

291
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We have people that are not working within 30 minutes or an hour of downtown Toronto.

292
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How do we maintain that same friendship bond culture virtually?

293
00:23:20,120 --> 00:23:23,520
So you know, there's there's stuff that randomly pops up.

294
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You got to be creative with how you're using Slack.

295
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And I'd say the team does a fairly good job of it, but you need to put effort in into

296
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both sides for sure.

297
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It's something I will actually just share about our team.

298
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We I don't actually know where the name came from, but for whatever reason, on a monthly

299
00:23:46,800 --> 00:23:53,480
basis, they created just a really quick internal application called Timbits.

300
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I don't want any kind of issues with the Tim Horns Corporation.

301
00:23:57,280 --> 00:23:58,280
It's spelled differently.

302
00:23:58,280 --> 00:24:00,480
It just sounds the same.

303
00:24:00,480 --> 00:24:05,880
But on a monthly basis, we just get an email of like, Hey, Mike, you're going to have coffee,

304
00:24:05,880 --> 00:24:11,440
your virtual coffee with Rashad, and you're not allowed to talk about work.

305
00:24:11,440 --> 00:24:14,160
There's some suggestions of things that you could talk about.

306
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And it randomly kind of assigns people on every month.

307
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So this isn't leadership being dictated.

308
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This isn't, you know, the standing meeting.

309
00:24:23,120 --> 00:24:29,400
This isn't, but it does fork force us to break the mold to say, you know what, I haven't

310
00:24:29,400 --> 00:24:33,640
talked to this ETL developer in six months.

311
00:24:33,640 --> 00:24:35,280
We never cross paths.

312
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They're not on, you know, my immediate team.

313
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This is great.

314
00:24:40,640 --> 00:24:41,640
What are you doing as a person?

315
00:24:41,640 --> 00:24:44,920
Did you enjoy the holidays or, you know, how are things going?

316
00:24:44,920 --> 00:24:51,880
So we're using tech and investing sort of both sides of like in person and virtual to

317
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really maintain our team culture.

318
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If you could wave a magic wand and change one thing about Canadian healthcare, what

319
00:24:59,600 --> 00:25:04,760
would you change?

320
00:25:04,760 --> 00:25:13,000
Working in healthcare at times, it feels like, it feels sometimes a little bit like everyone's

321
00:25:13,000 --> 00:25:14,000
at odds.

322
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So, you know, we read a lot of the media and it feels like it's sort of accusatory of

323
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physicians or providers are so slow or something like wait times are egregious because they're

324
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so slow.

325
00:25:26,000 --> 00:25:34,040
If I could wave a magic wand, the one thing I would change is I hope that we come to understanding

326
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that providers and patients actually want the same thing.

327
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We want to take care of our patients and as a patient, you know, I want to get the best

328
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care possible.

329
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What probably your average person doesn't know is all the constraints that are in the

330
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way to deliver, you know, max amount of coverage or the type of quality of care that all of

331
00:26:02,800 --> 00:26:04,280
us want.

332
00:26:04,280 --> 00:26:11,080
And a lot of this is regulatory, a lot of this is data access.

333
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Some of it is human capital.

334
00:26:13,560 --> 00:26:17,880
So I'd hope that I can't change all those things with this wand wand, but the one thing

335
00:26:17,880 --> 00:26:24,040
I would change is just that current, what I feel mindset of that we're at odds like

336
00:26:24,040 --> 00:26:28,760
providers and patients are sort of antagonistic with one another.

337
00:26:28,760 --> 00:26:32,840
I don't feel like it's real and I'd love to wave a wand and just remove that.

338
00:26:32,840 --> 00:26:37,560
Or we all are like, oh yeah, no, they actually want to take care of me and they chose this

339
00:26:37,560 --> 00:26:43,000
profession or you know, you know what, it's fair that this person's really frustrated.

340
00:26:43,000 --> 00:26:47,720
They couldn't get into my family practice for three weeks or four weeks because I'm so

341
00:26:47,720 --> 00:26:52,600
overbooked and now they have pneumonia or they just sat in my emergency department for

342
00:26:52,600 --> 00:26:55,760
12 or 16 hours before they saw me.

343
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And of course, they're really, really frustrated.

344
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So I'd love to sort of remove the animosity so that we could really build back a system

345
00:27:04,640 --> 00:27:07,720
that is reflective of who we are.

346
00:27:07,720 --> 00:27:15,800
If Sequoia or A16Z or General Catalyst sent you an email tomorrow said, Michael, here's

347
00:27:15,800 --> 00:27:17,480
$50 million.

348
00:27:17,480 --> 00:27:19,360
We want you to build something in healthcare.

349
00:27:19,360 --> 00:27:20,360
What would you build?

350
00:27:20,360 --> 00:27:21,360
All right, great.

351
00:27:21,360 --> 00:27:26,640
Well, 50 is not enough money if you're A16Z listening to this.

352
00:27:26,640 --> 00:27:29,720
$500 million, whatever the number is.

353
00:27:29,720 --> 00:27:35,040
So I think, you know, we are starting to see this.

354
00:27:35,040 --> 00:27:39,880
We see some of the potential and the type of quality of data that the Nordic countries

355
00:27:39,880 --> 00:27:41,440
have captured.

356
00:27:41,440 --> 00:27:49,840
And where we're really excited at Unity Health and St. Mike's is yes, AI is everywhere and

357
00:27:49,840 --> 00:27:51,240
this is another AI talk.

358
00:27:51,240 --> 00:27:52,240
I get it.

359
00:27:52,240 --> 00:27:53,920
We're done with that.

360
00:27:53,920 --> 00:27:55,520
We are just at the beginning.

361
00:27:55,520 --> 00:27:59,960
If we're just seeing the tip of the iceberg, we don't see what's below the water.

362
00:27:59,960 --> 00:28:10,160
And the thing that I would build is a real robust multimodal AI company.

363
00:28:10,160 --> 00:28:15,240
Our leader, my boss, Dr. Muhammad Nani, has been talking about this for years.

364
00:28:15,240 --> 00:28:22,120
But imagine a system that can not only read, which is what we have now, right?

365
00:28:22,120 --> 00:28:28,760
A lot of the systems are based on reading text data out of your electronic medical record.

366
00:28:28,760 --> 00:28:36,160
Imagine a future AI that can not only read, but can see because we've built in video feeds.

367
00:28:36,160 --> 00:28:40,200
Imagine a future AI system that can read, see, and hear because we're using auditory

368
00:28:40,200 --> 00:28:46,240
inputs from our emergency department or our surgical bays.

369
00:28:46,240 --> 00:28:51,080
Imagine that it's a system that can feel because it's getting sensor feedback or instrument

370
00:28:51,080 --> 00:28:55,720
feedback from any of the diagnostic machines we're using or any of the surgical devices

371
00:28:55,720 --> 00:28:58,240
that we're using.

372
00:28:58,240 --> 00:29:04,840
That's the type of AI that really feels science fiction and far-fetched, but where I'm getting

373
00:29:04,840 --> 00:29:12,760
excited because we are seeing things like Alpha Fold and the team getting a Nobel Prize

374
00:29:12,760 --> 00:29:15,200
just a few weeks ago.

375
00:29:15,200 --> 00:29:22,680
And that is really, yes, a big, massive data set and a huge feat, but we've really not

376
00:29:22,680 --> 00:29:27,040
tapped into all the data possible.

377
00:29:27,040 --> 00:29:34,800
And if we can get a team like the next DeepMind access to that type of information, and I

378
00:29:34,800 --> 00:29:39,520
think you would be aligned with this, imagine that we can start helping people before they

379
00:29:39,520 --> 00:29:46,800
arrive in clinic that you knew, based on your aura ring and your feedback, I got to actually

380
00:29:46,800 --> 00:29:52,760
get on medication or I need to go to St. Mike's because this is actually pretty severe.

381
00:29:52,760 --> 00:29:57,520
And so the thing I would build is something around multimodal AI.

382
00:29:57,520 --> 00:30:03,080
And I think it's bigger than one medical discipline, one clinical area.

383
00:30:03,080 --> 00:30:07,720
I think it's widespread because medicine is interdisciplinary.

384
00:30:07,720 --> 00:30:14,760
It's going to take in data and data types and feeds from across the care spectrum.

385
00:30:14,760 --> 00:30:21,240
You know, lately we've been hearing that AI, in some circles that AI is getting dumber,

386
00:30:21,240 --> 00:30:26,760
and which is attributed to the phenomenon called drift from what I understand AI just

387
00:30:26,760 --> 00:30:28,840
goes wonky.

388
00:30:28,840 --> 00:30:30,280
How do you control for that?

389
00:30:30,280 --> 00:30:32,160
How do you fix that?

390
00:30:32,160 --> 00:30:44,720
Yeah, I think we are seeing that drift is not a guarantee from at least my reading and

391
00:30:44,720 --> 00:30:45,720
understanding.

392
00:30:45,720 --> 00:30:47,520
So it may or may not occur.

393
00:30:47,520 --> 00:30:52,200
And that's sort of the interesting phenomena to me of like, why is this or when is this

394
00:30:52,200 --> 00:30:55,880
not happening?

395
00:30:55,880 --> 00:31:03,120
Some of our models which are not LMS are just the R machine learning and some rules based

396
00:31:03,120 --> 00:31:05,000
systems, they're not getting dumber.

397
00:31:05,000 --> 00:31:07,440
The accuracy is staying the same.

398
00:31:07,440 --> 00:31:14,600
But the part that I think maybe we need to keep in mind is our current context does change.

399
00:31:14,600 --> 00:31:16,240
So I'll give you an example.

400
00:31:16,240 --> 00:31:21,040
We've got a tool that predicts volumes into our emergency department at St. Mike's and

401
00:31:21,040 --> 00:31:22,040
St. Joe's.

402
00:31:22,040 --> 00:31:28,640
It's 48 to 72 hours in advance and it's very, very accurate, over 90% accurate, I think

403
00:31:28,640 --> 00:31:30,760
over 95% accurate.

404
00:31:30,760 --> 00:31:37,840
The part that we've done that's different than some other areas is we're able to predict

405
00:31:37,840 --> 00:31:41,280
severity of cases and some key areas for us.

406
00:31:41,280 --> 00:31:49,400
So we'll know, okay, you know, on Monday morning, we are going to have 30 cases, 28 of them are

407
00:31:49,400 --> 00:31:53,760
going to be pretty easy, two of them are going to be difficult and five of them are going

408
00:31:53,760 --> 00:31:55,840
to be mental health cases.

409
00:31:55,840 --> 00:32:00,880
What is this tool trained on or where is it getting its data from just in general?

410
00:32:00,880 --> 00:32:06,200
Yeah, so this is, it's a time series model.

411
00:32:06,200 --> 00:32:11,680
And so we're doing inputs of sort of our historical data at our emergency departments of the types

412
00:32:11,680 --> 00:32:12,680
of visits.

413
00:32:12,680 --> 00:32:17,400
And there's a lot of literature, I think going back to the 70s of that, emergency department

414
00:32:17,400 --> 00:32:21,720
visits are very predictable and there's seasonality associated with it.

415
00:32:21,720 --> 00:32:27,480
As Canadians, we should understand that there's cold and flu season for, for a reason.

416
00:32:27,480 --> 00:32:30,520
For our hospital too, there's also trauma care season.

417
00:32:30,520 --> 00:32:36,620
So please wear your helmets when you're riding your bike or going skating.

418
00:32:36,620 --> 00:32:40,640
So we see these natural spikes and occurrences and they do repeat themselves on an annual

419
00:32:40,640 --> 00:32:41,900
basis.

420
00:32:41,900 --> 00:32:51,880
The other key input for us too, actually, is we input weather and we input cultural events

421
00:32:51,880 --> 00:32:53,520
in the city.

422
00:32:53,520 --> 00:32:58,200
So really, really high likelihood, but we know, you know, if there's a hockey game and

423
00:32:58,200 --> 00:33:03,320
freezing rain and it's a Saturday night during cold and flu season, it's probably going to

424
00:33:03,320 --> 00:33:05,480
be a bad night in our emergency department.

425
00:33:05,480 --> 00:33:11,080
And when you say you input cultural events, like what is actually happening?

426
00:33:11,080 --> 00:33:17,160
Yeah, so we have basically like a listening type bot that there's a few different sites

427
00:33:17,160 --> 00:33:19,640
that's looking for posted dates.

428
00:33:19,640 --> 00:33:25,680
So hockey games is, it's one of the big ones basketball games.

429
00:33:25,680 --> 00:33:30,640
We you know, if there's parades and stuff like that, there's just more slips, trips,

430
00:33:30,640 --> 00:33:32,000
bumps and falls.

431
00:33:32,000 --> 00:33:34,120
And so therefore, where do they arrive?

432
00:33:34,120 --> 00:33:36,480
They arrive at downtown hospitals.

433
00:33:36,480 --> 00:33:42,280
And it's able to kind of say a hockey game will have one extra patient, whereas a Taylor

434
00:33:42,280 --> 00:33:45,480
Swift concert might have 100 extra patients or.

435
00:33:45,480 --> 00:33:52,360
Yeah, I don't know the volumes and how that, how those two sort of things equate.

436
00:33:52,360 --> 00:33:54,720
I will tell you the thing that we missed.

437
00:33:54,720 --> 00:34:00,160
I don't know if the listening tool didn't have this, but the thing where we had many

438
00:34:00,160 --> 00:34:05,360
more people in the emergency department than what we were expecting was when our world

439
00:34:05,360 --> 00:34:10,160
championship Toronto Raptors had their parade.

440
00:34:10,160 --> 00:34:16,520
And we, you know, I don't think the city was ready for that celebration.

441
00:34:16,520 --> 00:34:20,720
But the emergency department, it was like we weren't ready, but we saw many more people

442
00:34:20,720 --> 00:34:24,080
than what we were anticipating that day.

443
00:34:24,080 --> 00:34:26,680
So that's one of the, one of the misses.

444
00:34:26,680 --> 00:34:33,240
But the, yeah, I don't know that we take into account the volume of the event, but just

445
00:34:33,240 --> 00:34:38,680
certain events like, you know, marathons and stuff like that, the people do get hurt and

446
00:34:38,680 --> 00:34:42,480
they do end up in our emergency department.

447
00:34:42,480 --> 00:34:48,360
Do you think we should mandate explainability for AI as some of our medical societies have

448
00:34:48,360 --> 00:34:54,160
come out with and said, you know, if you're using an AI, you need to understand at some

449
00:34:54,160 --> 00:35:00,440
level how it works, but AI, you know, is a black box for the most part.

450
00:35:00,440 --> 00:35:01,440
Yeah.

451
00:35:01,440 --> 00:35:07,920
So I think explainability has caught on somewhat unfortunately.

452
00:35:07,920 --> 00:35:11,360
And so there's a few things to be the backup and say about this.

453
00:35:11,360 --> 00:35:15,160
So no, I don't think that we should mandate explainability and feel like also that we've

454
00:35:15,160 --> 00:35:16,680
solved the problem.

455
00:35:16,680 --> 00:35:23,720
I feel like explainability is caught on as a surrogate for trust and safety.

456
00:35:23,720 --> 00:35:29,080
So if we can understand something is kind of the argument, oh, then therefore it is trustworthy

457
00:35:29,080 --> 00:35:31,000
or then therefore it is safe.

458
00:35:31,000 --> 00:35:32,000
And that's actually not true.

459
00:35:32,000 --> 00:35:36,920
You can understand something and it could still not be trustworthy and it could still

460
00:35:36,920 --> 00:35:38,440
not be safe.

461
00:35:38,440 --> 00:35:43,360
The other example, you know, I've heard many people sort of say, you know, there's actually

462
00:35:43,360 --> 00:35:53,120
a lot of really common medications that all of us use regularly that we don't understand

463
00:35:53,120 --> 00:35:55,480
that we can't explain.

464
00:35:55,480 --> 00:35:59,360
You can ask your average physician or nurse, what is the mechanism of action for a CETA

465
00:35:59,360 --> 00:36:02,680
medifin and they're probably going to get it wrong.

466
00:36:02,680 --> 00:36:08,760
But yet it's, you know, one of the most widely used pharmaceuticals in the world.

467
00:36:08,760 --> 00:36:13,280
We all drive cars or get on trains and buses, but we don't necessarily understand, you know,

468
00:36:13,280 --> 00:36:17,880
how does a V8 or V12 engine works anymore?

469
00:36:17,880 --> 00:36:21,240
What is the difference between combustion or electric engines?

470
00:36:21,240 --> 00:36:26,520
So I don't think that explainability takes us to the place that we want to go.

471
00:36:26,520 --> 00:36:30,320
I think it just sort of caught on of like, oh, you know, if I understand more, I'm going

472
00:36:30,320 --> 00:36:32,880
to feel safer.

473
00:36:32,880 --> 00:36:37,760
And the reality with AI is you might not understand something, but still it might be really, really

474
00:36:37,760 --> 00:36:39,280
unsafe.

475
00:36:39,280 --> 00:36:47,600
So the part where I'm interested is what are the monitors and evaluations that are in place.

476
00:36:47,600 --> 00:36:50,320
And I don't know how we necessarily regulate that.

477
00:36:50,320 --> 00:36:52,640
That's not my space to dictate.

478
00:36:52,640 --> 00:36:59,800
But what I would want to see is for critical systems that have an impact in hurting someone,

479
00:36:59,800 --> 00:37:05,560
that they're monitored, and that there's some type of regular evaluation in place.

480
00:37:05,560 --> 00:37:11,520
That's what would make me feel safer and that we're in a better spot.

481
00:37:11,520 --> 00:37:19,120
Throughout history, we have been the smartest being on earth as far as we know.

482
00:37:19,120 --> 00:37:24,560
And now we're creating something which is more creative and smarter than us.

483
00:37:24,560 --> 00:37:26,880
And I think this scares us.

484
00:37:26,880 --> 00:37:34,040
And I think this is where this rise of explainability comes from is, okay, if I can explain it,

485
00:37:34,040 --> 00:37:36,600
I'm smarter than it.

486
00:37:36,600 --> 00:37:43,720
And I think that that's what's driving these rules and regulations.

487
00:37:43,720 --> 00:37:50,160
You sit at an intersection of industry and healthcare.

488
00:37:50,160 --> 00:37:59,800
What advice would you give to hospitals who are looking to be more innovative and incorporate

489
00:37:59,800 --> 00:38:03,840
more AI and startups into their ecosystem?

490
00:38:03,840 --> 00:38:10,160
And what are some things they do wrong?

491
00:38:10,160 --> 00:38:18,480
So I think it really comes down to identifying the problem and picking the right problem.

492
00:38:18,480 --> 00:38:24,360
And there's many different ways that you can go about that.

493
00:38:24,360 --> 00:38:28,560
What I see, whether you're building or you're buying something, like you're going to partner

494
00:38:28,560 --> 00:38:34,560
with a startup or what have you, innovation is this big, sexy thing.

495
00:38:34,560 --> 00:38:39,600
And a lot of people on the leadership team get involved in these meetings and issue the

496
00:38:39,600 --> 00:38:41,800
RFP, what have you.

497
00:38:41,800 --> 00:38:47,680
And I'll just be honest, I don't know many nurses directly.

498
00:38:47,680 --> 00:38:52,160
I'm not funded or supported by any sort of nursing union or federation.

499
00:38:52,160 --> 00:38:58,360
But you go through these processes and typically nurses are not included whatsoever.

500
00:38:58,360 --> 00:39:01,360
And it's such a huge miss for acute care.

501
00:39:01,360 --> 00:39:07,040
I can't speak to clinical practice specifically.

502
00:39:07,040 --> 00:39:12,800
But for acute care, where it's like something 80% or like 70% of bedside care is delivered

503
00:39:12,800 --> 00:39:16,520
by nurses, but they're not part of the innovation RFP process.

504
00:39:16,520 --> 00:39:22,760
Or they don't have a mechanism to share their challenges or pain points.

505
00:39:22,760 --> 00:39:30,600
So that for me is like a big miss of not including everyone within that practice as part of the

506
00:39:30,600 --> 00:39:36,880
journey, identifying the right end users as well.

507
00:39:36,880 --> 00:39:43,400
So we're still seeing very much a top down model.

508
00:39:43,400 --> 00:39:50,160
And I think we need to be honest, our innovation record in healthcare, but across industries

509
00:39:50,160 --> 00:39:54,220
in Canada is one of the worst in the world.

510
00:39:54,220 --> 00:40:02,320
So we have the lowest corporate R&D expenditure on technology in the G8.

511
00:40:02,320 --> 00:40:06,280
So we find ourselves below Italy.

512
00:40:06,280 --> 00:40:10,160
So that's not something that we might kind of like really conceptualize and think about.

513
00:40:10,160 --> 00:40:17,200
This isn't healthcare being less than average at its ability to innovate.

514
00:40:17,200 --> 00:40:23,160
We're seeing this in financial services, in manufacturing, in forestry, in education,

515
00:40:23,160 --> 00:40:28,520
in any industry in Canada, we find ourselves in the last place of the G8.

516
00:40:28,520 --> 00:40:33,920
So there's kind of this thing, like if you do something wrong once, do you repeat that

517
00:40:33,920 --> 00:40:35,960
pattern or do you actually change it?

518
00:40:35,960 --> 00:40:41,640
We find ourselves decades into this journey of we keep repeating the same pattern.

519
00:40:41,640 --> 00:40:44,880
And what I would say is we need to break the model.

520
00:40:44,880 --> 00:40:46,280
We need to try new things.

521
00:40:46,280 --> 00:40:53,600
Probably the same model doesn't work for each organization or each individual, but not including

522
00:40:53,600 --> 00:40:59,800
your people in that process, in that journey is a huge, huge miss.

523
00:40:59,800 --> 00:41:07,520
My second thing that probably we'll get some hate mail for this one, but I've said it before,

524
00:41:07,520 --> 00:41:09,120
so it's okay.

525
00:41:09,120 --> 00:41:19,280
The second fallacy that I think organizations fall into is a very long, protracted, expensive

526
00:41:19,280 --> 00:41:21,280
governance process.

527
00:41:21,280 --> 00:41:27,080
Okay, so we are going to innovate XYZ.

528
00:41:27,080 --> 00:41:33,320
So we need to talk to privacy, we need to talk to legal, and we need to talk to IT security,

529
00:41:33,320 --> 00:41:39,880
we need to talk to ethics, and we probably need to hire a consultant and, you know, met

530
00:41:39,880 --> 00:41:46,600
with one of my colleagues and they're frustrated, but they're into two plus years of working

531
00:41:46,600 --> 00:41:50,800
on an AI governance framework for their organization.

532
00:41:50,800 --> 00:41:53,680
It's the most beautiful framework you will ever see.

533
00:41:53,680 --> 00:41:59,480
I hope they get it framed, but where we stand today after the tens of thousands of hours

534
00:41:59,480 --> 00:42:06,680
that have been invested into that thing is the organization has not deployed any AI solutions.

535
00:42:06,680 --> 00:42:12,600
So we're now in 2025 and they're not using AI after all that money.

536
00:42:12,600 --> 00:42:14,960
So I wouldn't get hung up.

537
00:42:14,960 --> 00:42:19,560
Yes, you need governance, but you need to be lean, smart, and action oriented of how you're

538
00:42:19,560 --> 00:42:21,640
going through that process.

539
00:42:21,640 --> 00:42:23,920
And yes, leadership needs to be engaged.

540
00:42:23,920 --> 00:42:28,920
We would have that as part of like our strategy framework or our diamondy.

541
00:42:28,920 --> 00:42:34,120
Yes, leadership needs to be aligned, but leadership doesn't need to lead and be the only decision

542
00:42:34,120 --> 00:42:36,680
maker in an innovation process.

543
00:42:36,680 --> 00:42:42,480
We need to keep our people and our end users engaged and part of that process as well.

544
00:42:42,480 --> 00:42:50,840
Yeah, I think hospitals need to have an innovation department which has control over a few beds

545
00:42:50,840 --> 00:42:59,280
in a marriage or in a floor and has full and has a budget and has the ability to deploy

546
00:42:59,280 --> 00:43:03,760
that budget without any oversight from leadership.

547
00:43:03,760 --> 00:43:11,600
I think there needs to be a distinct department which is separate from hospital governance,

548
00:43:11,600 --> 00:43:15,960
legal leadership that can, you know, incorporate these projects.

549
00:43:15,960 --> 00:43:16,960
Yeah, I completely agree.

550
00:43:16,960 --> 00:43:24,760
And I mean, like your audience is mostly in healthcare, so they will get this, but we

551
00:43:24,760 --> 00:43:27,040
see this in other areas as well.

552
00:43:27,040 --> 00:43:31,520
So I spent some time in academia as I mentioned.

553
00:43:31,520 --> 00:43:37,320
If we look at our org chart, how come we don't have a chief innovation officer or a chief

554
00:43:37,320 --> 00:43:38,480
technology officer?

555
00:43:38,480 --> 00:43:44,520
Like, isn't that strange that like some of these networks and systems are multi-million,

556
00:43:44,520 --> 00:43:49,320
sometimes billion dollar organizations and we don't have a chief innovation or chief

557
00:43:49,320 --> 00:43:51,000
technology officer?

558
00:43:51,000 --> 00:43:55,240
We also sometimes don't have a chief marketing officer or chief revenue officer either.

559
00:43:55,240 --> 00:43:59,040
And this goes for major academic health science networks.

560
00:43:59,040 --> 00:44:03,120
This goes for many of the universities and colleges in the country.

561
00:44:03,120 --> 00:44:08,920
So, you know, wearing my like business school hat of like, why do we find ourselves in the

562
00:44:08,920 --> 00:44:10,560
position that we are?

563
00:44:10,560 --> 00:44:15,720
Yeah, you know, we can tweak some things and we can blame economic policy or hate on whatever

564
00:44:15,720 --> 00:44:22,320
political party we want or individual, but structurally, we've made some really weird

565
00:44:22,320 --> 00:44:23,320
decisions, right?

566
00:44:23,320 --> 00:44:33,200
Like, if we want to see more innovation commercialized out of our universities and academic hospitals,

567
00:44:33,200 --> 00:44:38,360
how come there's not a chief commercialization officer at every university and hospital?

568
00:44:38,360 --> 00:44:44,600
If we want our hospitals to, you know, be global brands like the MIT, the Harford, the

569
00:44:44,600 --> 00:44:49,000
Stanford, the Oxford, it came from U of T and Ivy that have great brands.

570
00:44:49,000 --> 00:44:53,800
But how come we don't have a chief marketing and revenue officer at every university and

571
00:44:53,800 --> 00:44:54,800
hospital?

572
00:44:54,800 --> 00:45:01,240
So, there's things where structurally, we find ourselves where we are because we've made

573
00:45:01,240 --> 00:45:04,000
the decision, we've made the bed that we're lying in.

574
00:45:04,000 --> 00:45:07,800
And that's things that could change, you know, to your point, can you appoint someone?

575
00:45:07,800 --> 00:45:14,040
Can you give them a mandate money and people to go and change the way that care is delivered?

576
00:45:14,040 --> 00:45:15,040
Absolutely.

577
00:45:15,040 --> 00:45:19,480
There's nothing that's holding us back except our own ambition.

578
00:45:19,480 --> 00:45:21,080
Last question, Michael.

579
00:45:21,080 --> 00:45:26,760
If you could go back and talk to yourself 20 years ago, what advice would you give him?

580
00:45:26,760 --> 00:45:31,040
You have no idea what's coming, but take a breath, it's all going to be okay.

581
00:45:31,040 --> 00:45:32,040
Awesome.

582
00:45:32,040 --> 00:45:33,040
Thank you, Michael.

583
00:45:33,040 --> 00:45:34,560
Yeah, great to speak with you.

584
00:45:34,560 --> 00:45:41,560
Thank you.