Latest / Becker’s Payer Issues Podcast / Agentic orchestration: A new approach to enterprise AI

Transcript

Agentic orchestration: A new approach to enterprise AI · Becker’s Payer Issues Podcast

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  - Everyone. This is Molly Gammel, Becker's Healthcare. Thank
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  - you so much for tuning in to the
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  - Becker's Healthcare podcast series. Today, we're going to
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  - talk about agentic orchestration,
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  - a new approach to enterprise AI.
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  - Joining me for today's discussion is Munjal Shah,
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  - cofounder and CEO of Hippocratic AI. Munjal, thanks
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  - for joining me today.
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  - Thanks for having me, Molly. So agentic orchestration,
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  - Munjal, this is a a new term to
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  - me, but it's something you and the Hippocratic
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  - AI team have a a lot of depth
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  - in, and you're also
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  - launching
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  - or or agentic orchestrators or this orchestration layer
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  - that moves from individual AI agents
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  - who handle discrete tasks to coordinated teams of
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  - agents managing more complex workflows.
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  - What does that evolution look like in practice,
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  - and and what challenges are you seeing in
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  - health care that really require a shift like
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  - this?
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  - Yeah. You know, I think when we started
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  - the company, we thought we were gonna build
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  - an LLM.
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  - And then we all realized that nobody wants
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  - to take an LLM and and have to
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  - turn it into something you use for your
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  - health care,
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  - tasks. And so we actually were one of
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  - the first to say we're gonna build an
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  - agent.
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  - And to do that, we had to fine
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  - tune an LOM to be able to provide
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  - the underlying the underpinnings. We had to
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  - we then announced a set of agentic skills.
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  - We said, oh, you know, you need a
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  - skill to be able to talk to patients
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  - that are post stroke and and have slurred
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  - speech, and you need a skill to be
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  - able to deal with patients that have mild
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  - cognitive impairment and need a lot of reminding.
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  - And so we then announced that we were
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  - building a bunch of skills, and we built
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  - hundreds of them, frankly,
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  - to deal with things we saw in real
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  - life, you know, in the I think we
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  - now have 250
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  - some odd million patient interactions, and and we
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  - just saw a lot of problems that had
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  - to be solved.
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  - But as we did that, we realized that
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  - isn't quite
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  - how health care like, it's not enough to
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  - just give you an agent to do x
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  - because then you gotta say, okay. Run this
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  - agent now. Oh, wait. I want another agent.
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  - So let's take something like readmission prevention.
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  - You know, it isn't just
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  - one agent that does a discharge call.
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  - It might be an agent inpatient that is
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  - educating you before you leave.
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  - It might be one you know, we're actually
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  - working on putting our our agents into a
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  - robot that might bring meds to beds for
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  - you
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  - and actually deliver the medication to you, the
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  - first dose or the second dose, which is
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  - common. Right? Because that helps to ensure patients
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  - will start taking their medication.
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  - We then will build another agent that'll call
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  - you, you know, at some regular interval
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  - that your care plan says. And so it
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  - says, you know, on day three, you're supposed
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  - to change your band, and it's really important
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  - that you're you know, so you don't get
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  - infected.
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  - Like, our AI will make sure we call.
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  - Now what is all that? That is a
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  - group of agents designed and focused on creating
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  - an outcome.
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  - And that is what we really call an
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  - orchestrator now, which is that
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  - we think that, you know, we're not here
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  - just to deliver agents that do tasks that
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  - may or may not deliver the outcome.
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  - You know, health care wants outcomes. We want
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  - to change patient lives,
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  - and we want to improve KPIs on which
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  - we measure how well we're delivering the quality
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  - of care.
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  - And it's time to align this AI world
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  - with that.
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  - So many health systems have already seen success
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  - using AI for individual workflows, but orchestration,
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  - patient journeys. If I'm in a CEO or
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  - COO shoes and looking through their perspective,
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  - Munjal, what are some new operational opportunities that
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  - creates that weren't possible with stand alone agents?
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  - You know, I think, you know, COO or
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  - CEO,
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  - you love to manage by objective. Right? Please
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  - lower the readmission rate.
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  - Please get my loss to follow-up
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  - closed and and help us increase better care
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  - for these patients.
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  - Please avoid leakage.
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  - And I think that for the first time,
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  - orchestrators
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  - offer a solution based approach to using AgenTic
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  - AI.
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  - You just indicate the objective you want and
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  - the solution you're really after, the area of
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  - focus, and and you do that. So we're
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  - now providing orchestrators that help you improve all
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  - of your onboarding, improve all of your leakage,
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  - improve all of your loss to follow-up,
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  - improve your pharmacy,
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  - business, improve your inpatient experience, your ambulatory experience,
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  - reduce your readmissions, and improve your heated scores
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  - and your stars. And so, you know, if
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  - you think about it, it allows you to
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  - operate on an objective level rather than just
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  - on a task level. And I think until
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  - now, we really couldn't
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  - really
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  - ask our AI to achieve an objective and
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  - know it would go and figure out all
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  - the things needed to to do that. There's
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  - actually three systems where they had us do
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  - discharge calls and try to avoid, readmissions.
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  - And we realized how many different agents we
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  - had to build to be able to achieve
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  - the goal.
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  - And we were in in actually, two of
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  - the systems, both of them, we ended up
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  - lowering readmission rates by 4%. One was 18
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  - to 14,
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  - which if you know anything about readmission rate
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  - reduction was so big a change, nobody believed
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  - it.
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  - Like, we didn't believe it. Our client health
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  - system client didn't believe it. And in fact,
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  - they made us rerun the number seven times
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  - on our side.
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  - We can we actually even we reran them
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  - because we're like, this can't be true. This
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  - has gotta be impossible.
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  - And it turned out that I mean, after
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  - seven times, we all realized, wow. It it
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  - was really true. And then we did it
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  - again in another health system and saw another
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  - four point drop. And that's when we realized,
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  - oh, wow. Like, this is actually working.
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  - Absolutely. A a range of of settings and
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  - needs that you just highlighted there. And I'm
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  - I'm putting myself in the in the position
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  - of our our audience who I think hearing
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  - some of these things, Minjal, they might share
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  - that early sense of of disbelief or just
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  - like they're we need to reorient ourselves thinking
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  - about this in terms of what orchestration can
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  - do.
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  - You've shared an example. You talked about some
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  - of the discharge calls, but another type of
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  - call I've heard about is this example of
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  - an academic medical center where your agents contacted
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  - more than 1,700
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  - patients who had open CT scan orders in
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  - three days, and the result was two hundred
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  - and forty nine scheduled lung cancer screenings.
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  - So that was that was one agent in
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  - one workflow.
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  - What becomes possible when the
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  - suites of agents are working together across an
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  - entire enterprise?
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  - Yeah. Let's let's take that as, like we
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  - call that the kinda lost to follow-up orchestrator.
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  - You know, that was one list. By the
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  - way, of those 250
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  - folks who did this, scheduled it, a 180
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  - showed up. We found nine significant findings.
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  - In fact, there's probably gonna be three cancers
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  - found out of that.
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  - Oh, I know there was one where they
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  - just had to they caught it so early,
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  - they were able to just do a resection
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  - and didn't need to actually do, more,
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  - invasive,
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  - and,
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  - you know, chemo and other things. So it
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  - was great, that we caught it so early.
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  - So, I mean, you know, we're this is
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  - delivering real good. But
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  - our goal isn't just to do it for
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  - CT
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  - lung cancer screenings. We should do it for
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  - everything.
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  - Right? Lost a follow-up on colonoscopies.
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  - Lost a follow-up on
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  - mammograms. You know, some of those are care
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  - gaps, but also lost a follow-up
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  - on many there's many things that patients don't
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  - get done. And now some of that isn't
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  - just about
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  - calling the patient and persuading them and convincing
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  - him to do it. You know, that that
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  - would be fine. We have an orchestrator above
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  - of, like, five. But now think about this.
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  - What if the patient has two things that
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  - they have to do? You don't wanna call
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  - them twice with two different agents. You wanna
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  - avoid the abrasion and call them once.
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  - Second, what if,
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  - the order is expired?
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  - Now you wanna follow-up with the doctor and
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  - say, you know, it expired, but I don't
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  - think it expired because you meant for it
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  - to expire. It just expired because the guy
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  - didn't get it done in so long. Do
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  - you really think that mister Shaw still needs
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  - to get that done?
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  - And we could then activate and get a,
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  - additional kind of just refresh of the order.
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  - And then on top of that, there's another
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  - agent that we will build that'll keep track
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  - of all of your capacity.
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  - Okay. We're not gonna call the lung cancer
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  - folks anymore this week. We're gonna wait a
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  - couple weeks.
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  - Why? Because there's no slots.
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  - Mhmm. Mhmm. You're you're full. So there's no
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  - point in calling. We're not gonna be able
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  - to find any slots.
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  - And so but, oh, but our our CT
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  - machine or our other machine, our MRI is
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  - available, but not our CT or vice versa,
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  - or our bone density machine is available.
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  - Right? Our DXA scan's available. So let's go
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  - and follow-up with all of the women who
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  - had had hip fractures who we now wanna
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  - make sure they've come back in and got
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  - another bone density scan.
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  - In fact, we're doing this use case for
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  - a customer of ours right now,
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  - going after,
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  - low bone density in women in particular
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  - and trying to encourage them to get a
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  - bone density scan. And so so imagine now
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  - you have a capacity
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  - or agent combined with each of the individual
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  - loss to follow-up agents combined with the consolidation
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  - agent that's putting it all together,
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  - to avoid abrasion. And
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  - and then, obviously, you have a scheduling agent
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  - that'll do all of the actual scheduling of
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  - the thing, plus one that follow-up with the
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  - actual physician if we need to change the
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  - order in some way or get it refreshed.
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  - And so
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  - that is an orchestrator.
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  - We're trying to get to the outcome.
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  - And and this is work that is much
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  - more
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  - complex, conditional. It's it's multipart. It's open to
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  - interpretation
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  - in some ways like you just highlighted.
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  - So thanks for those examples. I think it's
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  - really helpful to start to understand how this
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  - differs from the agents that health systems might
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  - be used to in current day.
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  - Another element of all this work and and
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  - scaling AI across an organization, it just naturally
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  - raises questions about governance, about trust. Hippocratic
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  - AI's Polaris uses a primary model alongside more
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  - than 30 supervising models that are validated by
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  - thousands of clinicians. So this is clearly something
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  - you and your company take quite seriously.
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  - I guess, Munjal, at a broader level, when
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  - you look around in the environment health care
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  - leaders are operating within today, what is a
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  - a common misconception or something that we might
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  - overlook about AI safety that warrants a closer
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  - look?
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  - So I said two things on that. One
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  - is, you know, trust is is,
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  - compounding.
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  - You know? So we didn't ask everybody to
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  - trust our orchestrators that obviously are a level
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  - of abstraction
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  - and independence that's that's a little bit more,
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  - right, than you've seen before.
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  - We first said trust our agent.
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  - Now that you trust our agents, now you
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  - can trust when we combine our agents.
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  - So it's almost like I'm not gonna give
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  - you the molecule without first you trusting every
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  - atom that's going in the molecule.
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  - And so if you think about, like, we
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  - we and then and then we told you
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  - how we built the atoms themselves. We're like,
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  - look. We tested them. We did this. We
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  - did that. So I I think that you
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  - couldn't have gotten here till now.
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  - You had to start with, alright. Here's how
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  - we took the LOM. Here's how we train
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  - the LOM. Here's how we then built it
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  - into an agent. Here's how we validated the
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  - agent. Here's how we now run a bunch
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  - of you know, I think we've now done
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  - we've done this 250,000,000
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  - patient interactions without one safety incident. Because we
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  - do escalate to humans when necessary, we've escalated
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  - to humans 38,000
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  - times.
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  - Wow.
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  - So we know what happens when it happens,
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  - how to ensure, you know, and we've avoided
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  - any significant safety issues. So it's like
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  - that's that piece. But I think that I
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  - think when people are having conceptions about safety,
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  - I I think it's in the abstract mostly.
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  - It's like you've never actually
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  - done the thing, and you're worried about its
• 11:36
  - safety or not. But once you do it,
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  - you realize, a, it's not it's not a
• 11:39
  - worry in some things you were worried about.
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  - And b, it is actually so you have
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  - to be,
• 11:45
  - you know, watchful in this other area, but
• 11:46
  - you didn't know it because you've never done
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  - it. Mhmm. Like, think about when you before
• 11:50
  - you ever went skiing,
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  - would you have known, like, what you gotta
• 11:54
  - worry about when you ski to not get
• 11:55
  - injured? Right? Like, you know, now whenever I
• 11:58
  - ski, by the way, like, I I'm always
• 11:59
  - off the slope by, like, 01:00,
• 12:02
  - 02:00 at the moment. Right? Because I always
• 12:04
  - feel like you get injured in the last,
• 12:06
  - like, hour
• 12:07
  - of your skis thing when the snow is
• 12:09
  - slushy and it's horrible, at least here in
• 12:11
  - California. And so
• 12:13
  - but I wouldn't have known that if I
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  - never skied. So first, I I think you
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  - need to focus on that. In terms of
• 12:17
  - the actual things to kinda look out for,
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  - I think there's a siren song right now
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  - around smaller models.
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  - And the siren song says, I can give
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  - you a cheaper AI because I'm using smaller
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  - models. I'm calling them SDMs
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  - or SLMs, small language models. Okay.
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  - That's not a thing.
• 12:39
  - The reason this was a big innovation in
• 12:40
  - the world was it's called a large language
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  - model.
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  - Mhmm. It's the large part that made it
• 12:46
  - smart.
• 12:48
  - Actually, very directly, the size of the model
• 12:51
  - correlates to its intelligence.
• 12:54
  - Like, everybody doesn't realize this. Like, the breakthrough
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  - for CHAT GPT came because we crossed 30,000,000,000
• 12:59
  - parameters. What's a parameter? It's the number of
• 13:01
  - connections between the neurons and the neural network.
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  - It's literally like the brain. The more connections
• 13:06
  - a brain has, the smarter the brain is.
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  - So I'm like, great. You wanna downgrade to
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  - a mouse brain?
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  - Because that's kinda what you just did,
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  - literally.
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  - You literally just downgraded to a mouse brain
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  - when you moved to an SLM. And that
• 13:19
  - may work fine,
• 13:21
  - but you know what it doesn't do? It
• 13:23
  - doesn't catch all the corner cases, and medicine
• 13:26
  - is a giant game of corner cases.
• 13:29
  - Mhmm. Mhmm. And, like, you know, like, diagnosing
• 13:31
  - UTIs in women, if you you know, we
• 13:33
  - don't do diagnosis. But if you did,
• 13:35
  - yeah. Could a little rule system get you
• 13:38
  - there eighty percent of the time? Sure.
• 13:41
  - But it's that twenty percent of the time,
• 13:43
  - five percent of the time, that's when,
• 13:46
  - you know, when the UTI is a possible
• 13:49
  - infection risk to the kidney. And, I mean,
• 13:51
  - that that that's when it gets complicated.
• 13:53
  - Yeah. And so I think that,
• 13:56
  - anyways, there's there's a,
• 13:59
  - one of the things I think people don't
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  - realize is safety and size of model are
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  - directly related.
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  - Mhmm.
• 14:05
  - Mhmm. Thank you for expanding on that. I
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  - think also just for highlighting some of the
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  - the human biases we may bring to to
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  - the unknown as these are now this technology
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  - is capable of doing things that six months,
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  - eighteen months ago just was not in existence.
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  - So I think it's really helpful to to
• 14:20
  - keep that in mind, Munjal.
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  - As we wind down here, in addition to
• 14:25
  - agentic orchestration,
• 14:27
  - you also rolled out what I understand to
• 14:29
  - be a self-service agent builder. Can you just
• 14:31
  - explain what this is for one second and
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  - also what it means for the ability of
• 14:36
  - health care leaders to get to market and
• 14:38
  - production more quickly?
• 14:40
  - Yeah. So the the innovation of our agent
• 14:42
  - builder is actually not the agent builder, but
• 14:44
  - it's actually the validation.
• 14:46
  - So we developed this unique way to validate
• 14:48
  - agents that, you know, I haven't seen anybody
• 14:51
  - else do, which is we hired 7,000
• 14:53
  - now 7,750
• 14:55
  - licensed
• 14:56
  - US clinicians.
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  - We gave them, like, you know, what we
• 14:59
  - call our gig app. They actually sign on
• 15:01
  - on their iPhone every, you know, morning, and
• 15:03
  - they see if there's tasks for them to
• 15:04
  - do, and we pay them for task. You
• 15:06
  - know? And the tasks are usually act as
• 15:08
  - a patient, call this new agent we're building,
• 15:11
  - and or this new orchestrator,
• 15:13
  - and
• 15:14
  - tell us if it's making mistakes and mark
• 15:16
  - every error. And one out of 50 times,
• 15:18
  - they got a human on the other line,
• 15:19
  - so then we got to calibrate how good
• 15:21
  - the error rate was compared to the kind
• 15:23
  - of, human error rate. We used to build
• 15:25
  - all of our models that way. They would
• 15:27
  - all get validated to this process, and we've
• 15:30
  - now done 700,000
• 15:32
  - I see almost 800,000
• 15:33
  - test calls with their simulations.
• 15:36
  - And we wouldn't release a a model until
• 15:38
  - it had been validated as as safer than
• 15:40
  - the human equivalent. We wouldn't validate we wouldn't
• 15:43
  - release a use case or an agent until
• 15:45
  - we had done that, you know, and now
• 15:47
  - we won't release an an orchestrator
• 15:48
  - until we've done that.
• 15:50
  - But we had to do that.
• 15:52
  - If you we had a little agent builder
• 15:53
  - where you could build your own agent, but
• 15:55
  - then we would, by hand, take it through
• 15:57
  - this validation system.
• 15:59
  - But now what we do is we link
• 16:01
  - the two.
• 16:02
  - So you can build your own agent.
• 16:05
  - You can send it. It'll automatically send it
• 16:08
  - for validation.
• 16:10
  - It'll validate it
• 16:12
  - and give you back the feedback. But then
• 16:13
  - we said, wait a minute. We don't even
• 16:15
  - just have to give you back the feedback.
• 16:17
  - We'll run another agent to rewrite your prompts
• 16:20
  - and your scripts based upon the feedback.
• 16:23
  - We'll send it for validation again,
• 16:26
  - and we'll do this loop for a week
• 16:27
  - without you even being involved and then say,
• 16:30
  - hey. Here's what we think is the right
• 16:31
  - one. Now you try it again and tell
• 16:33
  - us where it is, and then we'll validate
• 16:35
  - it one more time.
• 16:36
  - Wow. And so the power is really in
• 16:39
  - the combination of the agent builder feeding into
• 16:41
  - it, but then making it a continuous improving
• 16:44
  - loop that actually is somewhat autonomous.
• 16:47
  - And that's the power of this self-service is
• 16:50
  - is
• 16:50
  - you know, we probably should have come up
• 16:52
  - with a better name for it because it's
• 16:53
  - not just self-service. It's our self validating
• 16:57
  - way to kind of develop agents and and
• 16:59
  - orchestrators around that. And so, you know, I
• 17:02
  - I think that's what I'm most excited about
• 17:04
  - is is
• 17:05
  - we are now exporting the ability for you
• 17:08
  - to make safe agents, not just you to
• 17:10
  - make agents.
• 17:12
  - I'm really glad you expand on that. I
• 17:13
  - thought it was more of, like, just the
• 17:15
  - access of building an agent was gonna be
• 17:17
  - the value proposition there, but it's actually the
• 17:19
  - validation and your homegrown process of safety assurance
• 17:23
  - now being accessible
• 17:25
  - to the end user, like, in in such
• 17:26
  - a way.
• 17:27
  - Right. Exactly.
• 17:28
  - Exactly.
• 17:30
  - Manjal, you you and Hippocratic AI just continue
• 17:32
  - to be I I don't even know how
• 17:33
  - many steps ahead. I I just wanna thank
• 17:35
  - you so much for your time and your
• 17:36
  - insights today. This was a really interesting conversation.
• 17:39
  - Thanks for joining me. Yeah. Thanks for having
• 17:42
  - me, Molly. It's always a pleasure.
• 17:44
  - And to our audience, thank you so much
• 17:45
  - for tuning in. You can tune in to
• 17:47
  - more podcasts from Becker's Healthcare by visiting our
• 17:49
  - podcast page at beckershospitalreview.com.