Latest / Becker’s Healthcare Digital Health + Health IT / Mouneer Odeh, MA, Chief Data and Artificial Intelligence Officer at Cedars-Sinai

Transcript

Mouneer Odeh, MA, Chief Data and Artificial Intelligence Officer at Cedars-Sinai · Becker’s Healthcare Digital Health + Health IT

• 0:02
  - Every health system executive right now is being
• 0:04
  - asked to do something that has no useful
• 0:06
  - precedent,
• 0:07
  - make durable technology decisions in an environment that
• 0:10
  - looks meaningfully different every six months.
• 0:13
  - The pace isn't slowing,
• 0:14
  - the vendor landscape isn't getting simpler, and the
• 0:17
  - cost of missteps
• 0:18
  - keeps rising.
• 0:19
  - Becker's eleventh annual IT and Revenue Cycle Conference,
• 0:23
  - The Future of AI and Digital Health, exists
• 0:25
  - for exactly this challenge.
• 0:27
  - Here, you'll find more than 2,500 executive level
• 0:30
  - attendees,
• 0:31
  - 600 plus speakers, and four days of sessions
• 0:34
  - built around the questions leaders are actually wrestling
• 0:36
  - with.
• 0:37
  - How to scale AI responsibly,
• 0:39
  - how to protect increasingly complex systems,
• 0:42
  - how to bring more performance out of the
• 0:44
  - revenue cycle, and how to make tech investments
• 0:46
  - that hold up over time.
• 0:48
  - Join us September fourteenth through the seventeenth at
• 0:51
  - the Hilton Chicago.
• 0:53
  - You can register on our events page at
• 0:55
  - beckershospitalreview.com
• 0:57
  - by clicking on the events tab in the
• 0:59
  - upper right.
• 1:02
  - This is Laura Dirda with the Becker's Healthcare
• 1:04
  - podcast. I'm thrilled today to be joined by
• 1:06
  - Munir Odeh, chief data and artificial intelligence officer
• 1:09
  - at Cedars Sinai. Munir, it's a pleasure to
• 1:11
  - have you on the podcast today.
• 1:13
  - It's great to be here, Laura. I'm excited
• 1:15
  - about our conversation.
• 1:17
  - Oh, absolutely. I think this is gonna be
• 1:19
  - a lot of fun, and I know you're
• 1:20
  - very much on the forefront of a lot
• 1:22
  - of what's happening in health care today with
• 1:23
  - technology, digital transformation, and, of course, artificial intelligence
• 1:27
  - will be a huge part of what we're
• 1:28
  - talking about. So looking forward to that. But
• 1:30
  - before we dig in, can you tell us
• 1:32
  - a little bit more about yourself and Cedars
• 1:34
  - Sinai?
• 1:35
  - Sure.
• 1:36
  - You know, I'm the inaugural chief data and
• 1:38
  - AI officer at Cedars Sinai. I'm an economist
• 1:41
  - by training, but I spent, most of my
• 1:43
  - time in different sectors within health care, originally
• 1:46
  - life sciences, then Quest Diagnostics,
• 1:48
  - then switched over,
• 1:50
  - felt like I could make a bigger difference
• 1:52
  - with a nonprofit academic medical center. So I
• 1:54
  - I switched over to Jefferson Health in 2015,
• 1:56
  - spent a number of years at Nova Health,
• 1:58
  - and joined, Cedars Sinai about a year and
• 2:01
  - a half ago.
• 2:02
  - Cedars Sinai is a health system based in,
• 2:05
  - LA,
• 2:06
  - in Southern California area. And,
• 2:09
  - you know, we are
• 2:11
  - known for phenomenal world class health care services,
• 2:15
  - but also a really, really strong research program.
• 2:17
  - One of the distinctions of that research program
• 2:20
  - is a strong emphasis on computational biomedicine
• 2:23
  - and, using real world data to drive discoveries.
• 2:27
  - But also one of the things that's really,
• 2:29
  - really interesting and exciting and one of the
• 2:31
  - things that drew me to Cedars Sinai was
• 2:33
  - that culture of innovation.
• 2:35
  - This is an organization that really does embrace,
• 2:38
  - progress,
• 2:40
  - has a long history of, introducing new technologies
• 2:43
  - and adopting them rapidly,
• 2:45
  - and has kinda taken that same approach to
• 2:48
  - how AI can be a really powerful force
• 2:50
  - for,
• 2:52
  - you know, delivering better care for our patients,
• 2:54
  - accelerating research and discovery,
• 2:56
  - delivering better experiences,
• 2:58
  - and just kinda making things better for for
• 3:01
  - folks throughout the health system.
• 3:04
  - I love that. I I think it's so
• 3:06
  - helpful to look at that context of seeing
• 3:08
  - how AI can be such a powerful tool
• 3:10
  - to,
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  - supercharge the way you're thinking about health care
• 3:14
  - delivery and how the organization runs. Now from
• 3:17
  - your perspective, what are some of the most
• 3:18
  - powerful forces that are shaping digital health and
• 3:21
  - IT right now, and how do you anticipate
• 3:23
  - they're gonna evolve in the next year or
• 3:24
  - so? Yeah. It's an interesting question, and I
• 3:27
  - almost start outside of tech
• 3:30
  - to just sort of, like, set the context.
• 3:32
  - And this isn't really new for folks that
• 3:34
  - have been in health care, but it's, I
• 3:36
  - think, an important
• 3:37
  - force for for change and transformation.
• 3:40
  - And that's just the pressures, the macro pressures
• 3:42
  - that we face in our industry.
• 3:45
  - We all know with an aging population, that
• 3:47
  - creates increased demand.
• 3:49
  - And,
• 3:51
  - you know, in the face of that demand,
• 3:53
  - we have a very limited supply of doctors
• 3:56
  - and nurses,
• 3:57
  - and so it's really difficult to be able
• 3:59
  - to meet that increasing demand. We're also seeing
• 4:02
  - costs
• 4:03
  - growing very, very rapidly.
• 4:06
  - And so that's creating a tremendous amount of
• 4:08
  - pressure on health systems across the country.
• 4:12
  - The the good thing about,
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  - that is the case for change is well
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  - established.
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  - Enter stage, you know, AI and technology.
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  - This is a really exciting time because,
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  - you know, we can leverage data driven intelligence
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  - to drive a lot of transformation,
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  - very much needed transformation
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  - within the health care sector.
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  - I think for the first time,
• 4:37
  - we have the opportunity
• 4:38
  - to not just, like, add more people to
• 4:41
  - provide more services to deal with the increasing
• 4:43
  - demand.
• 4:44
  - I think we have a real opportunity to
• 4:46
  - think about how do we reengineer
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  - the way that we deliver care,
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  - and the services that we provide
• 4:53
  - to be much more efficient so that we
• 4:55
  - can,
• 4:57
  - really,
• 4:58
  - rise to the challenge of taking care of
• 5:01
  - our communities at a time when, when they
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  - need us most.
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  - I think that makes a lot of sense.
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  - And, you know, it's helpful to understand that
• 5:09
  - context because when you're looking at it and
• 5:11
  - thinking through
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  - the best way to approach,
• 5:14
  - care delivery in this transformation, you wanna make
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  - sure the patient and and, community is still
• 5:19
  - at the center of everything that you do.
• 5:22
  - I'm curious. When you think about the last
• 5:23
  - year or so, what is a big project
• 5:26
  - you did or or a big swing that
• 5:27
  - you took in the last year that paid
• 5:28
  - off? What did you do, and what were
• 5:30
  - the results?
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  - Yeah. You know, maybe I'll actually contextualize and
• 5:34
  - bridge first the prior question
• 5:36
  - some other sort of
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  - technology specific trends, and then I'll just talk
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  - about some of the swings that we made,
• 5:42
  - in response to that. So the
• 5:44
  - you know, I think that with with AI
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  - is, you know, it's amazing possibility, but it's
• 5:49
  - kinda two waves of impact on us. The
• 5:52
  - first is gonna be, like, productivity.
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  - It is a tremendous
• 5:57
  - tool for enhancing the productivity
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  - of our,
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  - of our workforce, our clinicians,
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  - And it's,
• 6:05
  - we're starting to see
• 6:07
  - those opportunities
• 6:09
  - spread dramatically.
• 6:10
  - The second wave
• 6:12
  - is really more of transforming
• 6:15
  - workforces
• 6:16
  - and workflow.
• 6:17
  - So it's changing the way we work and
• 6:19
  - as a result, changing the jobs that we
• 6:21
  - have that, you know, we have our job
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  - descriptions.
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  - I think on that first wave,
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  - what we have done
• 6:29
  - is the big swings,
• 6:31
  - say, is really, like, three
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  - broad attempts at this. The first is
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  - we recognize that the you really can't do
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  - anything
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  - with AI if you don't have good quality
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  - data, and you need a robust infrastructure
• 6:45
  - for managing that data. So,
• 6:48
  - we recognize that while we've been building data,
• 6:52
  - assets for over a decade and have really
• 6:55
  - mature capabilities,
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  - we needed to modernize the infrastructures and move
• 6:59
  - to the cloud AI native capabilities
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  - so that we could more easily access that
• 7:04
  - in a more streamlined, easier, simplified way.
• 7:08
  - And so that cloud migration
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  - is kind of a core enabling capability
• 7:14
  - for our data management in the age of
• 7:16
  - AI. The second
• 7:18
  - big swing, and it speaks to that productivity
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  - piece that I mentioned earlier,
• 7:23
  - AI, the technology is so accessible for people
• 7:27
  - that,
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  - we realized pretty rapidly that a big part
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  - of our strategy
• 7:32
  - should be to democratize
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  - access to this technology and promote,
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  - you know, the skills and the awareness and
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  - the adoption
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  - of the technology so that people could solve
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  - their own problems.
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  - They understand them better than anyone else. Things
• 7:46
  - get lost in translation when you try to
• 7:48
  - express that to technical teams.
• 7:51
  - Where possible, we put these technologies in the
• 7:53
  - hands of of our, team members. We started
• 7:56
  - off by building our own versions of, chat
• 7:59
  - GPT
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  - because we needed to have a HIPAA safe
• 8:01
  - environment to do that. We got over 6,000
• 8:04
  - users,
• 8:05
  - and our total health system is about 20,000
• 8:08
  - folks. So this was tremendous adoption
• 8:10
  - in a fairly short period of time.
• 8:13
  - And we started to cultivate that spirit of
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  - innovation
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  - with this new technology
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  - by,
• 8:18
  - hosting prompt a thons and doing some training
• 8:21
  - sessions around prompting,
• 8:23
  - and really just trying to champion and evangelize,
• 8:27
  - how this technology could,
• 8:29
  - improve their their own lives. And what we
• 8:32
  - have seen is,
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  - 97%
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  - of those who are using the tools will
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  - tell us that, it's made an important difference
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  - for them. It's helped them to be more
• 8:40
  - productive and to save time.
• 8:42
  - And, you know, we recently,
• 8:44
  - switched from our homegrown solution to an enterprise
• 8:47
  - solution, which is just giving much richer features.
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  - It still preserves that HIPAA safe compliant,
• 8:55
  - environment.
• 8:56
  - But the ability to introduce features to our
• 8:59
  - frontline has just been phenomenal, and it's been
• 9:02
  - really exciting to see that.
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  - You know, the third big swing,
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  - is
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  - what we realized is,
• 9:12
  - you know, just as
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  - AI is changing the opportunities that we have
• 9:18
  - available to us, we also needed to change
• 9:21
  - how we do our own work.
• 9:23
  - And,
• 9:24
  - through a lot of thinking, we kind of
• 9:26
  - decided that the old functional organizational structure that
• 9:30
  - we had,
• 9:32
  - wasn't really suited for the future. It was
• 9:35
  - a great way of dealing with
• 9:37
  - IT problems in the past. So people would
• 9:39
  - come to us and put in a ticket.
• 9:42
  - We had functional teams that were really good
• 9:44
  - at doing specific tasks.
• 9:46
  - But what often happens is you have to,
• 9:48
  - like, pass the baton onto a different teams,
• 9:52
  - And the coordination
• 9:53
  - of the overall project could get complicated in
• 9:57
  - some cases and could take some time to
• 9:59
  - line up the resources, make sure people had
• 10:01
  - the bandwidth to get things done. And in
• 10:03
  - the age of AI, we really wanted to
• 10:06
  - make things much more efficient,
• 10:08
  - much more, you know, rapid deployment of these
• 10:12
  - capabilities
• 10:13
  - and more iterative
• 10:14
  - interactions with our customers.
• 10:17
  - We've seen that they they are really good
• 10:19
  - at cocreating alongside us. And so we restructured
• 10:22
  - our teams to be a little bit more
• 10:24
  - customer focused.
• 10:26
  - We created multidisciplinary,
• 10:28
  - teams. We call them pods.
• 10:30
  - These pods have data scientists, data engineers,
• 10:34
  - BI developers,
• 10:35
  - AI engineers, all within the same group. And
• 10:37
  - so we emphasize that autonomy
• 10:40
  - because we wanted them to,
• 10:42
  - really,
• 10:45
  - really work at the discretion
• 10:46
  - of our leadership, and that was a way
• 10:49
  - to elevate the strategic priority of the work
• 10:51
  - that we were doing. It's very easy to
• 10:53
  - be busy. We've always got tons of, opportunities
• 10:55
  - and things to do.
• 10:57
  - I think for us, the challenge was make
• 10:59
  - sure we're doing the right work. And so
• 11:00
  - we put the
• 11:03
  - our leaders, our business leaders in the driving
• 11:05
  - seat, and they really direct what the pods
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  - are doing.
• 11:09
  - And, the pods have the autonomy,
• 11:11
  - and they are self sufficient for most of
• 11:14
  - the work that they need to do so
• 11:16
  - that they can get that,
• 11:17
  - work done and delivered. And then the other
• 11:19
  - thing is is sometimes with technology, you get
• 11:22
  - what you get based on who you ask.
• 11:24
  - And in this case, you know,
• 11:27
  - we could figure out what the right solution
• 11:29
  - is,
• 11:31
  - for our customers because we have a multidisciplinary
• 11:34
  - team who can come together and and work
• 11:36
  - through that. So these, forward deployed teams that
• 11:40
  - we've deployed
• 11:41
  - have been,
• 11:42
  - you know, as a change that we're going
• 11:44
  - through right now,
• 11:46
  - what we're seeing is, our customers are telling
• 11:49
  - our internal customers are telling us that, they're
• 11:52
  - excited about this. They're excited about,
• 11:55
  - the ability to
• 11:58
  - really decide what those priorities of the groups
• 12:01
  - are going to be. But I think the
• 12:03
  - long term opportunity from this is not just
• 12:06
  - that we can deliver work, you know, on
• 12:09
  - target and,
• 12:11
  - you know, faster.
• 12:12
  - But I think as we build those relationships
• 12:15
  - between the pods and the operations,
• 12:18
  - they kinda get
• 12:20
  - adopted into operations as part of the team.
• 12:23
  - And when that happens,
• 12:26
  - we're really looking for our folks to be
• 12:28
  - thought partners with our business customers
• 12:31
  - and our operational customers
• 12:33
  - so that they can contribute
• 12:35
  - great ideas.
• 12:36
  - They can understand the world of our customers,
• 12:39
  - And the more they understand that,
• 12:42
  - and the more proactive they feel empowered to
• 12:44
  - be,
• 12:45
  - the more that they're able to help shape
• 12:47
  - what the future,
• 12:49
  - workflows are going to be. And so we
• 12:51
  - we really feel like long term, this is
• 12:53
  - the right strategic direction,
• 12:56
  - to put us in place to drive
• 12:58
  - bigger,
• 13:00
  - more impactful,
• 13:01
  - value
• 13:02
  - from, from technology.
• 13:05
  - And, Ben, is there anything that you learned
• 13:07
  - that worked particularly well or or things you
• 13:09
  - had to adjust as you were going through
• 13:11
  - in creating this new process
• 13:13
  - in in the pods?
• 13:16
  - A lot. There's a lot of learning. So,
• 13:20
  - you know, I'd say,
• 13:23
  - making a big change like this requires,
• 13:26
  - a lot of shifts. So we may have
• 13:29
  - had some folks that were
• 13:31
  - covering a lot of different areas. Now we're
• 13:33
  - asking people to focus in on a specific
• 13:37
  - customer group so that they can build long
• 13:39
  - term relationships with them. And we're also asking
• 13:42
  - them to upscale or top skill their skill
• 13:44
  - their capabilities. So,
• 13:46
  - a big part of what we
• 13:48
  - are working through
• 13:50
  - is how do we leverage technology
• 13:52
  - to streamline our own internal operations, all of
• 13:55
  - that work that we've been doing for many,
• 13:57
  - many years,
• 13:59
  - try to automate as much of that as
• 14:01
  - possible and free up bandwidth to be able
• 14:04
  - to solve more problems for our customers. So
• 14:07
  - kinda less maintenance work and more,
• 14:10
  - delivering value project work.
• 14:13
  - That transition,
• 14:15
  - just requires a tremendous amount of going deep,
• 14:19
  - understanding our current workflows,
• 14:21
  - thinking about where technology can save us time,
• 14:25
  - and like spreading those learnings
• 14:28
  - across the different pods that we have. So
• 14:30
  - if we discover that we can be more
• 14:32
  - efficient in one area, how do we share
• 14:34
  - that knowledge across other areas? You know, I
• 14:36
  - think that's one area,
• 14:38
  - that, you know, we are putting a lot
• 14:40
  - of emphasis around. The other, though, is
• 14:43
  - when you ask people who are covering
• 14:45
  - a broad set of customers to focus in
• 14:48
  - on a smaller set of customers,
• 14:50
  - what you end up doing is you're shuffling
• 14:52
  - the deck a little bit, and you're kinda
• 14:53
  - building new relationships with our customers. That's disruption.
• 14:57
  - And we have to manage through that,
• 15:00
  - in the the transition period is not a
• 15:03
  - smooth transition, and we're working through what those
• 15:05
  - handoffs are. You already started this project. You're
• 15:08
  - not gonna throw the project over to somebody
• 15:10
  - else. So how do you run it through
• 15:11
  - to completion?
• 15:13
  - How do we do the warm handoffs?
• 15:15
  - All of that is,
• 15:17
  - complicated
• 15:18
  - process that we're working through,
• 15:21
  - you know, that with our teams. But what
• 15:24
  - I'll say I've been really excited and,
• 15:29
  - you know, a little bit surprised is just
• 15:31
  - how ready our team is for this transition.
• 15:34
  - They recognize that this is a unique moment
• 15:37
  - when we can do some amazing things for
• 15:39
  - our customers.
• 15:40
  - And And we're kind of embracing this future
• 15:43
  - recognizing that it's a little messy in the
• 15:45
  - transition period, and it requires us to shift
• 15:48
  - our mindsets,
• 15:49
  - and shift our mindsets for being more, like,
• 15:51
  - reactive and, like, okay. I'll fill up my
• 15:54
  - work with different tasks to being a little
• 15:56
  - bit more proactive about how do I anticipate
• 15:59
  - what my customers need and how do I
• 16:01
  - support that. So all of those transitions are,
• 16:04
  - really important,
• 16:06
  - change management aspects.
• 16:09
  - At the same time, we're working with our
• 16:10
  - customers to feel like I think they intuitively
• 16:13
  - recognize that this is a,
• 16:16
  - a great direction,
• 16:17
  - and they're excited about being in the driver's
• 16:20
  - seat.
• 16:21
  - But at the same time, they don't necessarily
• 16:23
  - all know how to drive that car, like,
• 16:25
  - really, really if if it's a sports car
• 16:27
  - and, you know, it's a different way of
• 16:29
  - driving,
• 16:31
  - it takes some time
• 16:33
  - to for them to get comfortable
• 16:35
  - in that seat and,
• 16:37
  - you know, understand the limits of what that
• 16:39
  - car can do, if if you were to
• 16:41
  - extend the analogy. And so so we're working
• 16:43
  - through some of those transitions.
• 16:47
  - Got it. That's fascinating to hear. And I
• 16:49
  - can imagine, you know, it's an ongoing process
• 16:51
  - for sure.
• 16:52
  - And I appreciate your your candid,
• 16:55
  - sharing there, especially I know a lot of
• 16:57
  - our listeners will be interested in doing similar
• 16:59
  - things across their organizations.
• 17:01
  - But now we've talked about, you know, some
• 17:03
  - of the things you've done in the past.
• 17:04
  - I'm curious. What's the most important problem that
• 17:07
  - you're trying to solve with AI and technology
• 17:09
  - right now?
• 17:12
  - You know, I don't know that it's any
• 17:14
  - one thing. I think for me as, you
• 17:17
  - know, as enterprise role of trying to look
• 17:19
  - across the system and recognizing that
• 17:22
  - AI is becoming pervasive throughout all of technology.
• 17:25
  - It's kind of like an overall upgrade of
• 17:28
  - technology
• 17:28
  - in some ways.
• 17:30
  - For me, the challenge is how do we
• 17:33
  - deploy this technology
• 17:35
  - responsibly,
• 17:37
  - rapidly,
• 17:38
  - and at scale?
• 17:40
  - And,
• 17:42
  - I think, you know,
• 17:44
  - doing those three things
• 17:46
  - and then making sure they're aligned to the
• 17:48
  - strategic priorities of the organization
• 17:51
  - is really ultimately
• 17:52
  - what I feel like my responsibility
• 17:54
  - is
• 17:55
  - here at Cedars Sinai. And I'll I'll take
• 17:57
  - them, you know, once one by one. We
• 17:59
  - talked about trying to put these pods in
• 18:01
  - in the, you know,
• 18:03
  - to be prioritized by our leaders.
• 18:05
  - We are developing a little bit more of
• 18:07
  - a a strategic plan that helps us to
• 18:10
  - have more of a multiyear road map, so
• 18:13
  - we understand that. But on the responsible deployment,
• 18:17
  - you know, this technology is extraordinarily
• 18:20
  - powerful.
• 18:21
  - In some ways,
• 18:23
  - we have a a control over a knob
• 18:25
  - that tells us how much autonomy can we
• 18:27
  - put in the hands of this technology and
• 18:29
  - how much of it is in the hands
• 18:30
  - of humans.
• 18:31
  - Right now, we've kept it,
• 18:33
  - almost entirely
• 18:34
  - in human hands. So this is a tool
• 18:36
  - that is being used by humans.
• 18:38
  - It is a predict mostly a productivity tool.
• 18:42
  - At some point in the future, it'll become
• 18:43
  - more of an autonomous agent that's starting to
• 18:45
  - do things on its own with human supervision
• 18:48
  - around the systems of, control that we need
• 18:50
  - to build around that and that reliability infrastructure
• 18:53
  - that we will we are building around,
• 18:56
  - AI.
• 18:57
  - So responsible deployment of AI really does mean
• 19:00
  - that we have to have a complete list
• 19:02
  - of all the tools that are out there,
• 19:04
  - that we have an ability to assess them.
• 19:06
  - Are they safe? Are they equitable?
• 19:09
  - Are they reliable?
• 19:11
  - And as we monitor,
• 19:13
  - as we, you you know, assess them and
• 19:15
  - deploy them, we need to monitor them to
• 19:18
  - make sure that six months later or two
• 19:20
  - years later, they continue to be so. And
• 19:22
  - so that's where we're putting a lot of
• 19:24
  - our emphasis is on the ongoing monitoring of
• 19:27
  - those tools and make sure that we feel
• 19:28
  - comfortable that,
• 19:30
  - that not only
• 19:32
  - did we sign off on them when they
• 19:33
  - went live, but that we continue to have
• 19:36
  - confidence that they're working as intended throughout the
• 19:38
  - lifespan
• 19:39
  - of those tools.
• 19:42
  - On the rapid, you know, I talk a
• 19:44
  - lot about those autonomous
• 19:45
  - pods.
• 19:46
  - Part of the reason why we wanna create
• 19:48
  - that autonomy is so that they could move
• 19:50
  - fast, and we also made them self sufficient
• 19:52
  - so that they didn't have a whole lot
• 19:53
  - of dependencies on other teams.
• 19:56
  - And and so we we'd like to continue
• 19:58
  - to think about how we can make it
• 20:00
  - easier to deploy the technology.
• 20:03
  - How do we invest
• 20:04
  - in
• 20:05
  - skills from nontechnical
• 20:07
  - folks to be able to solve their own
• 20:09
  - problems? I talked about that democratizing,
• 20:12
  - AI. What that's allowing us to do, Laura,
• 20:15
  - is kinda shift our focus more so than
• 20:18
  - ever before.
• 20:19
  - Kind of, like,
• 20:21
  - encourage people to solve their own problems if
• 20:24
  - it's low complexity.
• 20:25
  - Even if it's high impact, but if it's
• 20:27
  - something that they can do themselves, we're giving
• 20:29
  - them the tools and kinda helping them to
• 20:32
  - feel comfortable and confident doing that. And where
• 20:35
  - it makes sense, share it with others
• 20:37
  - and build a quality assurance around that. So
• 20:39
  - that democratization,
• 20:41
  - I think, is an important part of how
• 20:43
  - we can scale,
• 20:44
  - and do this more rapidly.
• 20:46
  - But what that leaves for us is those
• 20:48
  - high impact but high high complex
• 20:51
  - use cases where these multidisciplinary
• 20:53
  - teams are well suited to be able to
• 20:55
  - solve those that work.
• 20:57
  - And so to really trying to be very
• 20:59
  - clear about where we wanna focus our time
• 21:02
  - and where do we wanna invest in,
• 21:06
  - empowering,
• 21:07
  - other team members here at Cedars Sinai to
• 21:10
  - to solve some of their own challenges,
• 21:12
  - directly.
• 21:13
  - And then the third, you know, in terms
• 21:15
  - of the scale,
• 21:17
  - I think,
• 21:18
  - I think there's a few ways of,
• 21:21
  - of thinking about this.
• 21:24
  - You know, it's it's really hard to say
• 21:26
  - there's just one area that will be impacted,
• 21:28
  - and we should put all our bets on
• 21:30
  - on that one area. We do see AI
• 21:32
  - as impacting a whole lot of different areas.
• 21:36
  - You know, we are are trying to think
• 21:38
  - through
• 21:39
  - the way that we organize ourselves, our own
• 21:41
  - operating model, how we support things at scale,
• 21:46
  - building
• 21:47
  - the the infrastructure
• 21:48
  - to not only build these tools more rapidly,
• 21:52
  - but also make sure that they're reliably deployed.
• 21:56
  - But but just as important,
• 21:58
  - I I think, for us, and this is
• 22:00
  - new and, you know, I don't know that
• 22:02
  - we've solved this,
• 22:03
  - is
• 22:04
  - how do we build that
• 22:08
  - continuous improvement? So it's not just working on
• 22:11
  - a project and then moving to the next
• 22:13
  - project,
• 22:14
  - but more like, hey, we're working on a
• 22:16
  - program that's
• 22:18
  - multi year. How do we make it easier
• 22:20
  - for our patients to access care and navigate
• 22:22
  - through the health system. It's not one project
• 22:25
  - here or there. It's a multiyear
• 22:27
  - effort.
• 22:28
  - And when technology is focused on that alongside
• 22:31
  - our business,
• 22:32
  - partners who are thinking through that problem with
• 22:35
  - us, we think that we can make incremental
• 22:37
  - progress sometimes. But after a while, by working
• 22:41
  - through this,
• 22:42
  - how do we have that breakthrough moment where
• 22:44
  - we kind of, like, thought we were going
• 22:46
  - in the right direction, then we realized we
• 22:48
  - could just reengineer this in a completely different
• 22:50
  - way and make it a whole lot simpler.
• 22:52
  - And and I think that's what I'm most
• 22:54
  - excited about, in terms of,
• 22:57
  - scaling the impact
• 22:58
  - and driving more transformative impact. But it's still
• 23:01
  - early days, and I think it requires
• 23:03
  - us to have a little bit of faith
• 23:05
  - in,
• 23:05
  - building
• 23:06
  - that network
• 23:08
  - of,
• 23:11
  - folks from
• 23:13
  - our teams as well as from our clinicians
• 23:15
  - and our operational partners
• 23:17
  - who know how to use the technology and
• 23:19
  - are getting more and more creative with how
• 23:21
  - they can deploy that.
• 23:26
  - That's fascinating to hear. And, you know, I
• 23:28
  - I love the various thought out process and
• 23:30
  - especially when you're looking at how AI, will
• 23:33
  - continue to evolve and grow and making sure
• 23:35
  - that,
• 23:36
  - your teams have an understanding of it, and
• 23:38
  - then we'll be able to leverage it,
• 23:40
  - as much as possible,
• 23:42
  - even for the nonclinical or, excuse me, nontechnical
• 23:45
  - folks.
• 23:46
  - It just really is helpful to have that
• 23:48
  - kind of backbone and then the message of
• 23:50
  - simplification
• 23:52
  - is so important these days. So,
• 23:54
  - that that's amazing to hear. Before we wrap
• 23:57
  - up, I I just have one more question.
• 23:59
  - What are you most curious about for the
• 24:01
  - future?
• 24:02
  - Yeah. It's such an interesting question.
• 24:05
  - I think for me, what I'm most curious
• 24:07
  - about is the relationship between people and technology
• 24:10
  - and how that's gonna evolve.
• 24:12
  - It feels like almost every week we get
• 24:15
  - up and kind of like something new is
• 24:17
  - out there and it's changing.
• 24:18
  - It's changing
• 24:20
  - how people experience technology.
• 24:23
  - So when ChatGPT
• 24:24
  - came out, it rapidly got, you know, a
• 24:27
  - 100,000,000 users within a few months.
• 24:31
  - And the reason, you know, I think we
• 24:32
  - all experienced that our spouses, our children,
• 24:36
  - board members, and frontline staff, like, everybody was
• 24:39
  - all sort of experiencing this at the same
• 24:41
  - time.
• 24:42
  - And what was unique about it was it
• 24:44
  - was just so natural.
• 24:46
  - Right? The technology was so natural, and it's
• 24:49
  - now becoming a new interface in terms of
• 24:52
  - how we interact with the technology.
• 24:55
  - And,
• 24:57
  - I think that that we are starting to
• 24:58
  - see with our clinicians,
• 25:01
  - when we're investing in ambient AI and other
• 25:04
  - documentation
• 25:05
  - improvement solutions, optimization solutions,
• 25:08
  - what we're seeing is
• 25:10
  - they're experiencing
• 25:12
  - a better quality of interactions with our patients.
• 25:15
  - They're experiencing
• 25:17
  - less cognitive
• 25:18
  - overload.
• 25:19
  - And,
• 25:21
  - when we talk to, folks who are using
• 25:24
  - our generative AI solutions and now building,
• 25:27
  - you know, agentic workflows,
• 25:30
  - what they tell us is it's making their
• 25:32
  - lives easier, and they never imagined that they
• 25:35
  - could do some of the things that they're
• 25:37
  - doing.
• 25:38
  - We're seeing some power users
• 25:41
  - outside of the IT function
• 25:43
  - that are doing amazing things that just simply
• 25:46
  - couldn't have been done
• 25:47
  - without a technical background.
• 25:49
  - And because they are
• 25:51
  - wired in a certain way, they're able to
• 25:53
  - take,
• 25:54
  - advantage of these tools and the technology
• 25:57
  - to do, like, amazing things.
• 26:00
  - And so that democratization
• 26:02
  - of the technology
• 26:03
  - is shifting things. It's empowering
• 26:06
  - our,
• 26:06
  - nontechnical folks to do things in in amazing
• 26:09
  - ways. At the same time,
• 26:12
  - we are seeing
• 26:13
  - that,
• 26:15
  - the technology
• 26:16
  - is reshaping society.
• 26:18
  - There are really deep ethical and social
• 26:22
  - considerations, and
• 26:24
  - it is,
• 26:25
  - having an impact among politicians. It's, having an
• 26:29
  - impact in our neighborhoods on electricity prices.
• 26:33
  - And so I I think I'm really, really
• 26:35
  - curious to see not only at the micro
• 26:38
  - level how the technology is being adopted and
• 26:41
  - driving change,
• 26:42
  - but as you accumulate those changes across society,
• 26:45
  - how is it gonna change society? How is
• 26:47
  - it gonna change the economy?
• 26:49
  - And then, you know, ultimately, there's a big
• 26:52
  - debate about, like, how far will this technology
• 26:55
  - go?
• 26:56
  - You know, are we gonna get to the
• 26:58
  - point where it can outperform humans in all
• 27:01
  - tasks, or is it still in in all
• 27:03
  - ways that the human mind works, that generalized,
• 27:08
  - you know, capability?
• 27:09
  - Or is it still very task focused as
• 27:11
  - it is, largely today?
• 27:14
  - I, you know, I think that,
• 27:16
  - that debate is still out there, but I'm
• 27:19
  - I'm very curious to see how rapidly this,
• 27:22
  - technology is innovating
• 27:24
  - and how we need to continue to adapt
• 27:26
  - to it.
• 27:29
  - That's a, you know, fascinating discussion and really
• 27:32
  - truly looking into the future in how,
• 27:34
  - the world will be different even one year
• 27:36
  - from day today, let alone five, ten years
• 27:38
  - from today. It's just kind of incredible to
• 27:40
  - think about. It's, I mean,
• 27:43
  - it's kind of a unique time to be
• 27:45
  - alive.
• 27:46
  - Absolutely.
• 27:47
  - Well, Munir, thank you so much for joining
• 27:49
  - us on the podcast today.
• 27:51
  - This has been so much fun, and, you
• 27:53
  - know, I I'm really excited to have you
• 27:55
  - speaking at our health IT and revenue cycle
• 27:57
  - conference as well. I know we'll continue this
• 27:59
  - discussion and going through many of these themes
• 28:01
  - that we talked about today. So I'm looking
• 28:03
  - forward to that and,
• 28:05
  - continuing the dis discussion.
• 28:07
  - Wonderful. Thank you, Laura. It was great meeting
• 28:09
  - you, and I enjoyed our conversation as well.