Latest / Becker’s Healthcare Digital Health + Health IT / Sameer Sethi, SVP and Chief AI & Insights Officer at Hackensack Meridian Health

Transcript

Sameer Sethi, SVP and Chief AI & Insights Officer at Hackensack Meridian Health · Becker’s Healthcare Digital Health + Health IT

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  See you there.
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  This is Laura Dirdle with the Becker's Healthcare
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  podcast. I'm thrilled today to be joined by
• 0:42
  Sameer Sethi,
• 0:44
  senior vice president and chief AI and insights
• 0:46
  officer at Hackensack Meridian Health. Sameer, it's a
• 0:49
  pleasure to have you on the podcast today.
• 0:51
  Yeah. Laura, thanks for having me. I'm really
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  excited about this conversation.
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  Absolutely. I'm looking forward to it as well.
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  I know you're doing some really unique things
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  at Hackensack Meridian, so it'll be great to
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  share with our broader audience as well as
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  get a sense of how you're thinking about
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  the future, some of the big opportunities that
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  you see ahead. But before we do that,
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  I'm wondering, can you tell us a little
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  bit more about Hackensack Meridian Health and what
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  makes it unique?
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  Absolutely.
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  So, Hackensack Meridian Health is a health system
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  based out in New Jersey with a total
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  of 18 hospitals,
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  500 plus care sites, all in New Jersey,
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  and very focused on on on the state
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  and providing,
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  providing care to the community and the patients
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  we serve.
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  I function as the AI and insights officer.
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  I I joined the organization,
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  almost three years ago now. I started off,
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  functioning as their data and analytics officer. And
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  then, late last year,
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  I was promoted to function as their AI
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  and insights officer. The reason that is, that
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  that that is unique is because, you know,
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  our CEO,
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  mister Garrett Bob Garrett has has put an
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  emphasis on
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  on AI and RPA enablement. I think the
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  the push that he has made is
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  so I is is, I think, very unique,
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  I I think in in in the in
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  various industries, but especially in health care. I
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  think everyone is looking at AI and RPA
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  enablement, but I think our focus is quite,
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  I think, driven around, you know, what it
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  does to our workforce, what it does to
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  our patients, and how fast we can we
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  can get there. So I think we're we
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  as an organization are in addition to doing
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  what other health care systems do, from an
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  AI enabled perspective,
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  we are doing more.
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  We're we're we're scouting the market consistently.
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  We're seeing what,
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  what we need right away, what our patients
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  need. So there's a very, very heavy push
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  through,
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  mister Garrett's sponsorship to get this done.
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  That's amazing to hear. You know? And and
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  really, truly
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  outstanding you've got the support of,
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  Bob Garrett and all those on the c
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  suite to move forward,
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  in really in a smart way, be able
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  to use AI, use data analytics, use those
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  insights
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  that, in a way that you can grow
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  and develop in the future. I'm curious,
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  given this unique position that you're in, can
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  you talk a little bit about an accomplishment
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  that you're most proud of from the last
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  year or so?
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  Absolutely. So, you know,
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  what's all what's a bit unique and also
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  what led to the accomplishment
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  is is a speed at which we have
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  gone at this. I think that's number one.
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  You know, we put
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  a generative AI,
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  use case in production
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  within within three months, if not sooner,
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  of of the, you know, the word that
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  that got out with ChachiPT and others. You
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  know, we we were looking at this technology
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  evolve.
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  It came at at us very quickly, but
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  we were able to make sense of it
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  and produce a use case, put that in
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  production. And then now we have close to
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  nine,
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  use cases in production here. So I think
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  I think much ahead of the market,
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  and also driving value already
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  within the health system.
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  This helps our patients,
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  it helps our our workforce, our focus has
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  been around around workforce. So I think that's
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  one piece of this, the speed at which
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  we came at this. The other is I
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  think automation.
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  You know, while we while AI is new
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  and everyone is, you know, running towards making,
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  making sure that, you know, they drive value
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  from it. You know, automation has been a
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  big focus for us last year,
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  and and the years before as well. But
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  we were we were we were a bit
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  slow at it. Generally, I think when I
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  came into into the health system, we had
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  20 automations in production.
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  Last year, we committed to doing quite a
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  bit more. And just between in the last
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  two and a half years or two to
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  do
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  two and a half years, we've gone from
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  having those 20 automations to having 80
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  automations in production. You know, this varies from
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  anything from, you know, using automation for,
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  enrolling patients in MyChart a lot quicker. We
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  have the highest and then fastest in the
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  industry today.
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  So what we're doing is we're taking the
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  human,
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  out of the the process, wherever it makes
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  makes more sense. Right? Obviously, human in the
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  loop is important that exists, but we are
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  trying to speed things up. We have put
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  use cases where we're using robotics process automation
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  to,
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  to enter lab results into into patient charts.
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  And, obviously, there's a human in the loop
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  there as well, but the person is not
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  sitting there and typing it. We use this
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  inform use technology
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  to put in our female claims, for example,
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  which were in in the numbers of thousands.
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  And if a person were to do it,
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  it would take us,
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  I believe the estimate was four months to
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  do it. And we did it in two
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  days overnight. Robots just just kept working. So
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  I think the accomplishments have been many, but
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  I think it usually is focused on bringing
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  the the tech as fast as possible to
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  bear, to drive value. And then I think
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  also continuing on the fundamentals of things like
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  robotics process automation. I'm happy to go through,
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  you know, one or two use case that
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  I'm really proud of, but in general, that's
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  the principle.
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  That's amazing to hear. And I I love
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  that overall strategy of trying to find those
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  right use cases or ways that you can
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  use the robot effectively and and use automation
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  as well as AI to boost especially the
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  workforce as I know that's a huge, huge
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  challenge for so many hospitals and health systems
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  across the country. And I definitely am interested
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  in zeroing in on some of those, use
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  cases,
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  that you find most,
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  most helpful or or beneficial. I I think
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  it's really cool to have,
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  grown that automation from 20 to a 80
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  in production so quickly, and and, clearly, the
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  culture is, is able to do that. So
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  I'm curious if you're able to dial in
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  on some of the things you're most proud
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  of there. I think, would love to hear
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  more about that. Yeah. Absolutely. I I'll talk
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  about, you know, something that's
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  that I think is is evolving today, and
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  and and it stays on top of our
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  mind or and on our minds.
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  You know, almost a year ago,
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  if not a bit more, you know, we
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  launched our use case where we were using
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  generative AI to summarize a clinical note.
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  This is so so think about how, you
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  know, when you and I go into a
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  physician office, generally, a physician is obviously being
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  present at the same time,
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  you know, trying to read on the screen
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  information about you. Now this is the patient
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  chart. It has your lab values. It has
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  what, you know, what was discussed last time
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  you were there and discussed the conditions that
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  you had. And it's quite a bit, you
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  know, we have
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  a lot of information that that that physicians
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  and clinicians in general,
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  you know, gather about the about you as
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  a patient in there. What we did was
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  we made it easier for the clinicians to
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  consume that information by summarizing all that.
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  Our first version of that was just a
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  general summary.
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  We then when when we put it in
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  production,
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  we realized that that the that the clinicians
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  were looking for,
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  summarization by specialty. So then we selected nine
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  specialties,
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  and the intention there was that, what a
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  primary care physician wants to see in a
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  summary is different from somebody in oncology or
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  cardiology.
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  Right. So we created
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  we then start to tailor these summaries
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  by specialty and that
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  we got a lot more
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  attraction and value from it. We then went
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  into now summarizing
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  lab values
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  into this this clinical note because the clinician
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  said, I would like to see lab values
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  in there as well. So I don't have
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  to go and look for it. So this
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  is an example of a use case and
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  even our strategy around doing this. Obviously, there's
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  a lot of governance around putting in team
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  production, and we abide by those things.
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  I believe we have the best AI
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  governance safety framework in place.
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  But what we try very hard to do
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  and then we this is where we push
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  the business, the clinicians, and everyone in the
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  world to put something out there as fast
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  as possible, as long as it passes through
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  the safety checks. Right. But then we iterate
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  over time. That's been a pretty big accomplishment
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  for us.
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  As I mentioned, you know, we did this
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  right right after this technology started to become
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  available to us. We put something out there,
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  and it's evolved and grown bigger and bigger
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  and bigger, and we are really proud of
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  that.
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  That's really cool to hear. And, definitely, as
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  you mentioned, something to be extremely proud of
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  knowing all the work and effort that goes
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  into it. It can be extremely complex to
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  put together on the technology side, but on
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  the people side too and just make sure
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  that everyone, is comfortable with these changes and
• 9:09
  then, being able to do them quickly, as
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  you mentioned, in order to get the best
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  results. One thing as well, when you look
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  into the future, where do you see some
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  of the big growth opportunities
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  over the next twelve months or so? Yeah.
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  I think,
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  the biggest opportunity that I am seeing is
• 9:23
  is how do we start to glue these
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  different technologies together? Right? And, you know, that's
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  a bit of a challenge. But I think
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  to me, that's a growth opportunity today.
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  You know, while,
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  you know, the industry in general thinks of
• 9:39
  AI as it's as a separate thing, when
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  in fact, that is not the case.
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  AI, if if it were to exist,
• 9:46
  separately, would will not drive value. It has
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  to work with other technologies and people.
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  It has to be embedded into the right
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  workforce so that capability is delivered at the
• 9:58
  right time to the right person in the
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  right way.
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  So in my mind, that is the opportunity
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  today is where we're taking AI
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  as as as a conversation
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  and but also but then embedding it embedding
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  it into what what our workforce
• 10:15
  do day to day.
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  You know, that's a that's a growth opportunity
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  we have, and, you know, we are really
• 10:21
  focused on that. We have, you know, folks
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  that that that abide by, you know, concepts
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  of human factor engineering. This is,
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  the the in in some words, industrial engineering,
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  where,
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  technology is looked at as a part of
• 10:36
  the process versus a technology alone. Right? And
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  and embedding that into a process. So I
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  think that's the, to me, that's the growth
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  opportunity for everyone, including us. I think we
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  are doing really well, but we can do
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  better where we are embedding different techno embedding
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  AI into the into into,
• 10:53
  into a process, but then also bringing different
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  technologies together.
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  You know,
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  if you we keep hearing about, you know,
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  and I'm digressing here a bit, but we
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  keep hearing about agentic AI. You know? And
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  agentic AI is a lot of things. But
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  to me, the biggest value there is that
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  it is an orchestration
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  of
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  of robotics process automation and and three different
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  AIs and a few other capabilities, all that
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  put together. So it works at a command
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  line. It works at a command to say
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  when a patient says, I need to go
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  to the doctor, so make me an appointment
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  and make sure that I have car service,
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  available for me. And then when I get
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  there, you know, there's a wheelchair there. That
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  is a command. And that's a command line.
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  Right? And I'm using the word command line
• 11:38
  as a as a nontechnical term here. And
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  then
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  and then agents take that command and do
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  five different things. And that's what I'm talking
• 11:47
  about bringing three to five different technologies together,
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  AI, RPA, digital, whatever you have, whatever is
• 11:53
  required to do it, maybe even a robot
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  making a phone call into a real person
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  having conversation and saying, you know, this person
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  this person is coming in for this appointment
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  at this time. Please make sure you have
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  a wheelchair available. All that has to come
• 12:05
  together. So that's the biggest opportunity, which is
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  being able to tailor all that together.
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  I love that. It seems like such a
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  a simple thing, yet I know nothing is
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  ever simple,
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  when it comes to this type of transformation.
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  And and certainly, those little things make a
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  big difference when you're looking at the patient
• 12:21
  care journey, the experience, but then to how
• 12:24
  things are operating, at the hospital and in
• 12:26
  clinics and really, you know, how teams are
• 12:29
  working together most effectively.
• 12:30
  A lot of opportunity, it seems like, with
• 12:32
  AI, especially in the technologies,
• 12:35
  as we've been talking about. But I'm curious,
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  what are the big challenges that you're anticipating
• 12:39
  as well?
• 12:40
  Yeah. You know, our challenge is, I think,
• 12:43
  the our
• 12:45
  which is a blessing in disguise, but,
• 12:47
  it's it's the pipeline. You know, we have
• 12:50
  a close to
• 12:51
  300 use cases that are waiting to be
• 12:54
  looked at, waiting to be worked on, waiting
• 12:57
  to be developed, waiting to be deployed, waiting
• 12:59
  to be evaluated.
• 13:01
  So it's, you know, our our organization,
• 13:03
  you know, is very excited about,
• 13:05
  you know, what the potential of what these
• 13:08
  technologies, you know, bring to them. And and
• 13:11
  the I think the time and space
• 13:14
  to actually do this right,
• 13:16
  is really about this is no longer for
• 13:18
  us about
• 13:19
  running after or looking at the the shiny
• 13:21
  toy. Right? In addition to, you know, this
• 13:24
  thinking about, you know, these use cases as
• 13:27
  being a good idea, it's also about ROI.
• 13:29
  Right? How do we make sure we capture
• 13:32
  that information? And, unfortunately, that's not how people
• 13:35
  are thinking about today about about AI today.
• 13:37
  People want to do AI. But
• 13:39
  when we have those conversations about, you know,
• 13:41
  what you know, how do you how will
• 13:42
  you drive value from it? And how do
• 13:44
  we make sure it it impacts the bottom
• 13:46
  line? And bottom line could be financials, could
• 13:47
  be patient experience, could be work for experience.
• 13:50
  Those those take a lot of time. I'm
• 13:52
  not saying people aren't ready to have that
• 13:53
  conversation,
• 13:54
  but exercising those, I think those steps as
• 13:57
  a part of our governance process,
• 13:59
  is is is a is a challenge for
• 14:01
  us. There's a large demand,
• 14:03
  and we have to cater to that demand.
• 14:06
  And it's, you know, it it requires times.
• 14:08
  It requires resources.
• 14:10
  That's a big challenge. These are all great
• 14:12
  ideas. We just have to pick the right
• 14:14
  ideas to work on because we can't work
• 14:15
  on all of them.
• 14:17
  That's such a great point. And I know,
• 14:19
  as you mentioned, having so many ideas coming
• 14:22
  in and so much,
• 14:24
  possibilities,
• 14:25
  and and then truly understanding the benefits, the
• 14:28
  different, you know, return that you can get
• 14:30
  from these, what should be prior prioritize is
• 14:32
  key. When looking at that governance structure, I'm
• 14:35
  wondering if we can, dig just one bit
• 14:37
  deeper there. I know a lot of, again,
• 14:39
  hospital and health systems are trying to figure
• 14:41
  out where to do this right and understand
• 14:43
  what they need in order to develop
• 14:45
  a strong and and meaningful governance
• 14:48
  process for especially,
• 14:50
  AI driven projects.
• 14:51
  What did you do at Hackensack Meridian, and
• 14:53
  what have you found really effective? If there's
• 14:55
  anything that you've changed or tweaked, that you
• 14:58
  found didn't work, I'd just love to hear
• 14:59
  your perspective on that briefly before we wrap
• 15:01
  up here. Yeah. I think the biggest piece
• 15:04
  that, you know, just looking back now in
• 15:06
  our journey for approximately three years or, you
• 15:08
  know, my journey at three years at Hackensack,
• 15:11
  you know, I think we've done a lot
• 15:12
  of great and lot of right things.
• 15:15
  Having said that, I think it's fair to
• 15:17
  say that, you know, we could have done
• 15:18
  a lot of things better as well. One
• 15:20
  thing that
• 15:21
  continuously comes to mind,
• 15:23
  you know, is is education. Right. I think
• 15:26
  educating people
• 15:28
  on this technology
• 15:30
  is something that I think us as an
• 15:32
  industry have haven't thought about or acted on
• 15:35
  as much as we should have. Right?
• 15:38
  People
• 15:39
  have a sense of what AI is.
• 15:42
  People don't know how
• 15:45
  best to
• 15:46
  use it
• 15:47
  correctly.
• 15:48
  You know, as a result of that, this
• 15:50
  technology ends up, you know, getting a lot
• 15:52
  of bad rap at times
• 15:54
  when, in fact, in my opinion,
• 15:56
  it's not used correctly.
• 15:58
  To explain this, I'll use example of, you
• 15:59
  know, the the likes of,
• 16:01
  you know, chat GBT or the generative AI
• 16:03
  capability. Right? I consistently have folks,
• 16:07
  looking
• 16:08
  for answers from such technologies,
• 16:11
  that are that I think this technology cannot
• 16:14
  answer. So they think of this as
• 16:16
  one place for everything that solves, you know,
• 16:19
  world hunger. Right? When that's not the case,
• 16:21
  you know, I the the analogy that I
• 16:24
  have used is, you know, the likes of
• 16:25
  chat could be Gemini or it could be,
• 16:28
  you know,
• 16:29
  chat.
• 16:31
  So it's it's like the high fructose corn
• 16:34
  syrup or sweeteners.
• 16:35
  The reason I call it that is because
• 16:38
  it does the job. It's really sweet, but
• 16:40
  it may not be good for everyone or
• 16:42
  all use cases. So when a clinician is
• 16:44
  going in and typing a use case question
• 16:47
  in there that is very clinically focused
• 16:50
  and demanding a accurate response. And when the
• 16:52
  clinician doesn't get it, you know, this technology
• 16:55
  gets bad rap.
• 16:56
  We
• 16:57
  educate our our clinicians internally
• 17:00
  to find ways to ask the question correctly.
• 17:03
  We actually build a use we we built
• 17:05
  this capability where we use this technology called
• 17:08
  RAG, r a g,
• 17:10
  which sits on top of these generative AI
• 17:12
  capabilities.
• 17:13
  So it provides a better answer, but I
• 17:14
  think it all comes with education. The point
• 17:16
  I'm trying to make is that I think
• 17:18
  we have ran fast at especially this generative
• 17:20
  AI technology,
• 17:21
  and folks are,
• 17:23
  are not educated. It just, you know, if
• 17:25
  if I had to go back, which is
• 17:27
  which no one can,
• 17:28
  I would say, you know, let's in parallel
• 17:31
  invest enough in educating
• 17:33
  the public and our workforce on what this
• 17:34
  is and what this isn't and how it
• 17:36
  should be used?
• 17:38
  As we work on building this technology, I
• 17:40
  think we all ran at building these technologies.
• 17:42
  And then we we we didn't invest as
• 17:45
  much in educating our masses around what this
• 17:48
  is and what this isn't and how this
• 17:50
  is useful to you.
• 17:52
  That's such an excellent point. I I really
• 17:54
  appreciate your perspective on this. And, certainly, I
• 17:56
  can imagine some efforts to to try to
• 17:58
  do that now, but still, you know, having
• 18:01
  it from the beginning, it sounds like it
• 18:03
  is really beneficial or would have been beneficial.
• 18:05
  Yeah. It's not late. I mean, we're, you
• 18:07
  know, we're we're now charging at this at
• 18:09
  Hackensack where, you know, we have more people
• 18:12
  involved than ever. You know, I'll give you
• 18:13
  an example of our governance team. When we
• 18:16
  started AI governance, it was, you know, it
• 18:18
  was,
• 18:19
  we we we it we had a total
• 18:21
  of 13 domains
• 18:23
  that evaluated all these use cases, and it
• 18:25
  was 13 people. Today, it's over 26.
• 18:29
  Right? So we have doubled.
• 18:30
  We have better engagement from our leadership,
• 18:34
  which is our executive leadership team on which
• 18:36
  use cases to work on,
• 18:38
  in in in the past. I'm not saying
• 18:40
  that wasn't there, but they are more present.
• 18:41
  They're more talking about this. They're demanding and
• 18:44
  saying, why should we do this? So I
• 18:45
  think it all came from learning. So I
• 18:47
  think it's not too late. We're, you know,
• 18:49
  you know, Hackensack is is obviously very keen
• 18:52
  in making sure we get this right. Other
• 18:54
  organizations are doing this as well. So it's
• 18:56
  I I say better late than never.
• 18:59
  Absolutely. Absolutely. I I love that. Now, before
• 19:02
  we wrap up, one more question. What is
• 19:04
  the number one thing that you're doing right
• 19:06
  now to set the organization up for long
• 19:08
  term success?
• 19:10
  I think,
• 19:11
  it's it's about being very I'm asking the
• 19:14
  organization to be open to ideas.
• 19:17
  Right.
• 19:18
  There is no bad idea,
• 19:20
  but it requires,
• 19:22
  you know, attention from people. So I think
• 19:24
  what I am asking the organization is is
• 19:27
  is is open your door, listen to, you
• 19:30
  know, how this technology
• 19:32
  can help you, you know, come to us
• 19:34
  with ideas that you have come to us
• 19:36
  with ideas that you are hearing.
• 19:39
  And I think we should
• 19:40
  problem solve around what is the best thing
• 19:43
  to do for you,
• 19:44
  that that I think sentiment existed. It's not
• 19:48
  fair. It's not something that,
• 19:50
  that didn't exist, but it's it needs to
• 19:52
  be hyperscaled.
• 19:54
  Right? This is new tech, and there's some
• 19:57
  great ideas out there. There are companies that
• 19:59
  are doing it really well.
• 20:01
  There's it. So I I think what what
• 20:03
  I am
• 20:04
  telling them is is this AI is not
• 20:09
  a similar problem,
• 20:10
  or similar initiative. This is an organizational
• 20:13
  initiative. So keep your ears open.
• 20:16
  Speak to us about this. We will tell
• 20:18
  you let's collaborate.
• 20:20
  Right? And and and get to a place
• 20:22
  where either we decide to do this level
• 20:25
  of AI enablement in your in your in
• 20:27
  your team and in your department, or we
• 20:29
  decide not to do it at that point
• 20:30
  and move on to the next use case.
• 20:32
  But I think it's all about being open
• 20:34
  to what AI is,
• 20:36
  realizing where that where it it drives value
• 20:38
  and where it doesn't.
• 20:40
  Absolutely. I I really appreciate that. Sameer, thank
• 20:43
  you so much for joining us on the
• 20:44
  podcast today. This has been such a valuable
• 20:46
  discussion, and I look forward to connecting with
• 20:48
  you again soon. Yeah. Likewise, Zor. Thank you
• 20:50
  so much for the opportunity. Really excited to
• 20:52
  talk to you again.