Latest / Becker’s Healthcare -- Pediatric Leadership Podcast / Making AI Actionable Across Healthcare with Jawad Khan

Transcript

Making AI Actionable Across Healthcare with Jawad Khan · Becker’s Healthcare -- Pediatric Leadership Podcast

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  - Every health system executive right now is being
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  - asked to do something that has no useful
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  - precedent,
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  - make durable technology decisions in an environment that
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  - looks meaningfully different every six months.
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  - The pace isn't slowing,
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  - the vendor landscape isn't getting simpler, and the
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  - cost of missteps keeps rising.
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  - Becker's eleventh annual IT and Revenue Cycle Conference,
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  - The Future of AI and Digital Health, exists
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  - for exactly this challenge.
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  - Here, you'll find more than 2,500 executive level
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  - attendees,
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  - 600 plus speakers, and four days of sessions
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  - built around the questions leaders are actually wrestling
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  - with.
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  - How to scale AI responsibly,
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  - how to protect increasingly complex systems,
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  - how to wring more performance out of the
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  - revenue cycle, and how to make tech investments
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  - that hold up over time.
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  - Join us September fourteenth through the seventeenth at
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  - the Hilton Chicago.
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  - You can register on our events page at
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  - beckershospitalreview.com
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  - by clicking on the events tab in the
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  - upper right.
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  - This is Laura Dirda with the Becker's Healthcare
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  - podcast.
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  - I'm thrilled today to be joined by Jawad
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  - Khan, vice president of enterprise analytics and applied
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  - AI at Akron Children's. Jawad, it's a pleasure
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  - to have you on the podcast today. I'm
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  - happy to be here. Thank you for having
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  - me. Absolutely. Now I'm excited for our conversation
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  - because I know technology is moving so quickly,
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  - and we're gonna talk about some really cool
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  - things that you're doing at Akron Children's. But
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  - before we dive in, can you tell me
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  - a little bit more about yourself and, the
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  - hospital?
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  - Sure. I'm Jawed Khan, vice president of enterprise
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  - analytics and applied AI at Akron General Hospital.
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  - I lead enterprise data,
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  - analytics, and applied AI.
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  - Our focus,
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  - is turning AI from interesting pilots
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  - into trusted operational capability.
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  - That's that's where we are focused right now.
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  - The the kind basically, the AI that we
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  - want to implement is the kind that reduces
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  - friction for clinicians and staff,
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  - improves decision making,
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  - and creates measurable value for the organization
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  - and the families we serve, really. At the
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  - end of the day, we all want to
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  - do best for our patients and the families
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  - of our patients.
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  - And about the the brief note about Akron
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  - Children's Hospital, we are children's holy hospital based
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  - in Akron, Ohio, which is Northeast part of
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  - Ohio.
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  - We have,
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  - our main facility is in Akron, which is
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  - our flagship hospital. We have two other facilities
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  - and over 40 specialty and PCP clinics, ambulatory
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  - clinics in the area.
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  - That's great to hear and and definitely, you
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  - know, impressive to see the reach as well
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  - as, I know, such an important service to
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  - the community.
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  - Now I'm curious,
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  - what are some of the powerful forces that
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  - are shaping digital health and IT at your
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  - organization today? What does it look like right
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  - now, and how do you anticipate they'll evolve
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  - in the next year or so?
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  - I mean, the biggest shift is that health
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  - care has moved from asking
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  - whether to use AI
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  - to asking actually where it creates real value.
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  - That puts pressure on leaders to
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  - prove ROI, you know, return on investment.
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  - We have to prove certain governance
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  - and embed AI into actual workflows.
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  - So, essentially, the big forces or the big,
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  - push shaping digital health and IT today
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  - is that the winners won't be the organizations
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  - with the most pilots.
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  - They'll be the ones that make AI trusted,
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  - operational,
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  - and measurable across the enterprise.
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  - A good example of that, I can share
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  - perhaps what we are doing.
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  - A good example of that workflow focus is,
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  - for example, physician in basket summarization, a very
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  - well known project that many institutions
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  - may have implemented,
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  - and we have done it.
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  - It's basically you know, it helps physicians,
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  - reduce the cognitive burden,
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  - cognitive load on them,
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  - you know, reduces review time. It's a great
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  - example
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  - of doing AI that really lives
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  - embedded within the workflows to help physicians.
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  - That that's that's the type of push that
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  - we want to see in AI today, and
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  - that's what is shaping
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  - how we are moving forward with implementing AI.
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  - Got it. That makes a a lot of
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  - sense. And I think especially when you look
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  - at AI, you know, there's so much potential,
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  - but that unknown and uncertainties
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  - can,
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  - bubble into a a lot of challenges or
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  - or assumptions on the team. So for the
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  - physicians, as you're looking at bringing in technology
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  - and optimizing their workflows
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  - and those types of things, how do you
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  - work alongside them, to make sure the technology
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  - is doing what it's supposed to do and
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  - adoption
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  - is likely because I think what I'm hearing
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  - from a lot of health systems is that,
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  - you know,
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  - if you aren't able to work with the
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  - clinicians,
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  - and and adoption isn't there, it's a waste
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  - of time, effort, and money. So I'm curious
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  - how do you match all that within Akron
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  - Children's and and make sure you're doing, the
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  - best you can with that those investments in
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  - the technology and AI?
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  - I mean, you're a 100% correct, Laura. The
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  - conversation is shifting from
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  - novelty to accountability. Right? That's what physicians want
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  - to see.
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  - And,
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  - our focus is to
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  - meet them
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  - in in sort
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  - of place where they feel most comfortable to
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  - use the technology,
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  - to use the implementation. That's what really
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  - matters,
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  - is how you provide solutions
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  - that are embedded into the workflows that
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  - makes it frictionless for them to be able
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  - to adopt.
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  - And, again, like I said,
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  - as long as we strengthen governance, as long
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  - as we provide them
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  - an easy to use,
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  - not just a sign in tool, not just
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  - a novelty, but a very easy to use
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  - natural
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  - way of,
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  - implementing and using technology, I think the easier
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  - it will become. So that's where I think
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  - the accountability
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  - is for us
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  - in terms of, IT. IT
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  - professionals,
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  - AI
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  - engineers, applied
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  - AI leaders,
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  - our job is to essentially allow them to
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  - be
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  - friendly,
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  - with technology. And the only way we can
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  - do that is to embed right within their
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  - workflows.
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  - That that makes a lot of sense.
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  - Thank you so much for going through that.
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  - Now could you tell us about a big
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  - swing that you took in the last year
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  - that paid off? What did you do, and
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  - what were the results?
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  - One big swing was
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  - treating analytics and AI
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  - as enterprise capability
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  - instead of scattered project.
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  - That that led us to align
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  - governance,
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  - make platform decisions,
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  - and operational priorities around scale.
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  - The payoff is consistency and trust.
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  - You we build reusable
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  - capability instead of one of tools. That matters
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  - because
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  - health care is moving beyond point solution
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  - and toward
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  - AI that is integrated into daily work. You're
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  - gonna see hear hear this from me repeatedly
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  - that the the real challenge is to make
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  - something that is
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  - integrated into the workflow, not necessary outside of
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  - the workflow. Because the more
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  - integrated the solutions are within the workflow for
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  - the providers,
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  - to your point, Laura,
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  - adaptability
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  - will increase,
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  - and people will start trusting.
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  - Obviously, we have to put very tight in
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  - governance around solutions.
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  - Absolutely. And I think that makes a lot
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  - of sense. And, you know, being able to
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  - have that type of capabilities at the enterprise
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  - level, it seems like it would make it
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  - so easy
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  - then for everybody else to know the guardrails
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  - and and be able to innovate within them.
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  - And I know creating that governance is no
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  - easy task. What was that like? Look. Look.
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  - Scale happens
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  - when AI becomes a capability,
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  - not a collection of products.
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  - Right? So if if you have if you
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  - have a collection of projects and you're working
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  - on point solutions,
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  - then it is very difficult to govern them.
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  - So and it is very difficult to even
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  - scale.
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  - Hence,
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  - we have to think AI as a capability
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  - initiative. We are
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  - putting in, we are implementing, or we are
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  - designing,
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  - and we are organically
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  - building capability within the organization. So you have
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  - to think at the enterprise level
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  - to make these things
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  - work and work the way
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  - that physicians intend them to use.
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  - Hence, it's very important. Right? You so you
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  - you think at the enterprise level,
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  - and you think about scale,
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  - and you try to bring it in a
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  - way that it pays off consistently. You have
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  - to do the same thing consistently repeatedly. It's
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  - more like grunt work. You need good data
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  - foundation layers. You need solid
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  - governance around this for people to be able
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  - to start trusting it. That's how that's how
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  - we do this. It's not easy by any
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  - means,
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  - but, nonetheless,
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  - if we are not intentional about it, if
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  - you're not intentional about doing this in a
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  - way that it scales, then it's gonna become
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  - very difficult down the road.
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  - Absolutely. And I think that makes a ton
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  - of sense and, you know, to have that
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  - foresight and ability to,
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  - build that process and the governance and then,
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  - you know, setting yourself up to have that
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  - type of growth and scalability.
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  - Really, you know, it seems to to be
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  - the way that you can most quickly transform
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  - in the future. Now based on our our
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  - conversation, it sounds like, you know, really setting
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  - the stage and and foundation for the team
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  - to be able to,
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  - work, create, innovate, and bring in,
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  - AI where it makes the most sense for
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  - the team.
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  - What is the most important problem now that
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  - you're trying to solve with AI and technology?
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  - What's kind of,
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  - currently percolating or on the horizon?
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  - The most important problem that we are trying
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  - to solve is actually turning intelligence
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  - into
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  - action inside real workflows.
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  - In health care,
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  - AI only matters when it reduces burden
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  - or it improves timing
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  - or helps people act with more confidence.
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  - We are focused on practical use cases, like
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  - I shared with you.
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  - Some note summarization is a great example.
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  - Relative insight and conversational support for some of
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  - our,
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  - analytics products is very important.
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  - But the real challenge is, again, making them
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  - usable. We have to govern them. They have
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  - to be reliable,
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  - and they have to be reliable at the
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  - moment decisions get made. Right? That's that's where
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  - this,
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  - technology and intelligence needs to be put in
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  - place.
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  - So taking intelligence by itself is is good,
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  - but from good to great is to take
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  - that intelligence and really convert that into workflows.
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  - If insight,
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  - doesn't change action,
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  - it has limited value.
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  - And I can perhaps,
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  - give you specific examples as to what we
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  - are doing in terms of making this intelligence
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  - turn into, you know, our intelligence turn into
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  - actions.
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  - We have developed
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  - a application in our organization that's based on
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  - artificial intelligence, and it allows our physicians to
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  - interactively
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  - see the care gaps. And the application, again,
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  - it obviously gives them intelligence about where care
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  - gaps are in our organization, where those gaps
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  - live, and which patients, which departments.
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  - But beyond that, the application allows so it
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  - sort of blends the intelligence into action. It
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  - allows physicians to be able to,
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  - take actions to automate some of the things
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  - that may perhaps close the care gaps. That's
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  - where our focus is. That's where we are
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  - working hard to bring into place.
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  - It's it's a it's a great example of
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  - understanding
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  - how intelligence, when it turns into actions, actually
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  - provides real value.
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  - I love that. That's such a great example
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  - of ways that, you know, you can tackle
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  - a real problem for so many organizations,
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  - in in try to figure out how to
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  - make care more accessible and and close some
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  - of those gaps. And so,
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  - you know,
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  - technology at its best is doing those things.
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  - Now when you look into the future, what
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  - are you most curious about in in in
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  - thinking about?
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  - I I mean, I think the
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  - what what I'm most curious about is what
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  - we have just talked about. I'm most curious
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  - about
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  - learning which systems
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  - build the discipline
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  - to scale AI responsibly.
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  - You know, Becker's actually I was at your
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  - last conference. There was a quite a bit
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  - of coverage
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  - that has emphasized
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  - that adoption is moving fast.
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  - But governance, trust, and execution maturity
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  - are what will separate real transformation from experimentation.
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  - I think the past
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  - maybe, three years,
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  - where institutions were focused on experimenting, doing pilots,
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  - I think the next phase is less about
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  - flashy demos and POCs and concepts.
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  - It's gonna be more about durable operating models
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  - that make
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  - AI part of everyday health care delivery.
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  - That that's what I'm really curious about. How
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  - does this
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  - experimentation
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  - phase that we're coming out of
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  - gets transformed into
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  - health care delivery, an operating model that provides
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  - AI
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  - as part of daily, everyday life for our
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  - physicians and and including, I mean, you know,
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  - maybe our patients as well. But that's that
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  - that that's where curiosity that's where, at least,
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  - my
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  - curiosity
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  - is. The winners, Laura,
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  - you know, in the in the future,
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  - won't be the most,
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  - excited organizations.
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  - It will be the winners will be the
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  - most disciplined organizations. Right? Just being excited doesn't
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  - really help to do good AI.
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  - We really have to be very disciplined in
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  - all the things that we talked about, governance,
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  - good data foundations.
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  - This requires a lot of discipline,
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  - and that's where the future is. We have
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  - to move away from just being excited to
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  - also being disciplined to do real and good
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  - AI.
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  - That, John, thank you so much for joining
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  - us on the podcast today. This has been,
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  - such a fascinating conversation. We're really cool to
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  - hear about the things you're doing at Akron
• 14:11
  - Children's as well as perspective on the future
• 14:13
  - and how AI can responsibly
• 14:15
  - become a bigger part of the health care
• 14:17
  - delivery system. I'm excited to see you as
• 14:19
  - well at our health IT and revenue cycle
• 14:21
  - conference coming up in September. I know you'll
• 14:23
  - be speaking on a panel with us, and
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  - it'll be a truly a pleasure to catch
• 14:27
  - up and see each other in person.
• 14:29
  - That's good. Pleasure as well.