Latest / Becker’s Healthcare Podcast / Delivering Real AI Results and Why Enterprise Data Strategy Comes First

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Delivering Real AI Results and Why Enterprise Data Strategy Comes First · Becker’s Healthcare Podcast

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  Hello, everyone. This is Erica Spicer Mason with
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  Becker's Healthcare. Thank you so much for tuning
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  in to the Becker's Healthcare podcast series.
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  Today, we're going to talk about delivering real
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  AI results and why enterprise data strategy comes
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  first.
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  And joining me for this conversation, we have
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  with us Ajay Kapare,
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  the president and CEO of Elkay.
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  Ajay,
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  welcome to Becker's podcast. Thank you so much
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  for being here today. Thank you, Erica, for
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  having me here.
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  Really happy to have you here. And I
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  thought to get us started, it'd be great
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  if you could share just a little bit
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  about your background and also offer our listeners
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  just a quick snapshot of what Elkay does.
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  What are the types of data challenges that
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  you're helping health systems navigate right now?
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  So, Erica, you know, last two decades, I
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  worked in health care and health care IT,
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  and,
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  you know, I have seen I joined
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  right when we started with the wave of
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  meaningful use.
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  And,
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  from meaningful use, from
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  getting all the revolution done towards health care,
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  electronification
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  of EMS, and
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  every physician in The US
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  trying to actually adapt to that change, to
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  population health, community health, and now we all
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  are in the AI wave.
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  Elkay,
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  to be honest, we play as a central
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  role towards all the things going around us.
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  Elkay as a company, we are in five
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  segment of health care IT.
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  We are an enterprise interop solution provider company,
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  where primarily the three solutions that we offer
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  and the three problems that we solve,
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  we are LK Opera, which is a platform
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  to orchestrate all the interoperability. Then we also
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  take care of live and legacy data platform
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  for a long term digitization
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  strategy with LK OSIS. And then platform to
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  expand the possibilities that things beyond connectivity with
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  LK Orbit,
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  our lab network is a pretty big place
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  out there.
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  And as we look at LK, the next
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  generation where things are changing,
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  we all are looking at what the power
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  of AI and what AI can do. And
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  I feel like we are the foundation for
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  that clean and clear data
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  that drive these strategic initiatives
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  to make AI enabler.
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  This is where we are all actually very
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  excited about.
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  Great to learn more about you, Ajay, and
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  LK as well. It's
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  fantastic that you have this kind of central
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  role as an organization.
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  I think that's an important place to be,
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  especially as we're seeing so much
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  enthusiasm
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  and even hype around AI and health care.
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  And we hear from leaders across the industry
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  that a lot of pilots
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  struggle to scale.
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  So I would love to know from what
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  you're seeing across your own client base, where
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  is AI actually delivering
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  measurable results? And, conversely,
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  where are organizations hitting walls that maybe they
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  didn't anticipate hitting?
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  I think if you look at it,
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  as we talk about fueling the AI innovation
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  with access to quality data,
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  that quality data access is where we play
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  the big role. If you look at it
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  today, 91%
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  of health care leaders,
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  they believe AI or machine learning will be
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  an integral part of their organization's growth.
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  But at the same time, if you actually
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  do a survey with
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  20 different CIOs,
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  chances are you might get actually 20 different
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  answers.
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  Because everyone's strategy is different. Strategy is still
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  a foundation
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  where LK plays or LK actually try to
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  help them
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  in the in the way in the initiatives,
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  what they are trying to achieve is data
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  integrity, which also includes, you
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  know, today if you look at it, there
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  is a lot of data silos. There is
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  a lack of sufficient data. There is a
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  poor data quality. There is inconsistent
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  data formats, inadequate data governance. Where LK can
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  come and really put a foundation is we
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  can do the groundwork for AI and machine
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  learning success. By ensuring that there is a
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  proper data integrity, we are actually
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  ensuring that there is a quality of data
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  which goes in. Otherwise,
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  you are always going to see that garbage
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  in. You are going to see the garbage
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  out. So we are the enablers of the
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  data.
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  That's the big role that we are playing
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  right now with our data and interoperability strategy.
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  Oh, fantastic. Thanks for giving us a kind
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  of on the ground feel of what you're
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  seeing, Ajay.
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  And when you look at the organizations that
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  are seeing real traction
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  with AI versus those that have not yet,
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  What would you say is the distinguishing factor,
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  and how much of that comes down to
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  the data foundation that they had in place
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  before they even started to leverage an AI
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  tool?
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  I think I think there is always going
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  to be, like, you know, there is always
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  going to be that excitement
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  looking at the use cases and looking at
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  what you can actually achieve.
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  A lot of times with that excitement, actually,
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  a lot of dollars gets actually plugged in,
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  and then you see at the end of
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  the day that there is no ROI.
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  In today's time,
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  when things are difficult, I mean, you know,
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  you open,
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  you know, Becker's the daily Becker's report, and
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  you will see that, you know, things are
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  not that easy. And in these times, you
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  really have to start fixing,
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  your data strategy with data foundation.
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  The real organization who are seeing real traction,
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  it it's not just that, you know, here
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  is a use case or here are the
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  tools I'm going to use it.
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  And it's not even the budget, it's the
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  discipline. How do you put together
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  you know, you got to actually make sure
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  that there are you are unifying the system,
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  clean up your interfaces, and and you get
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  serious about governance.
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  The one struggling
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  part that everyone is looking at it is
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  how the top of the layer will perform
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  in this complex environment if the bottom of
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  the data which is
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  inserted is not correct.
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  So that foundation piece, the quality and connectivity
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  of your data, one has to look at
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  it. And I personally feel anyone who works
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  super hard on that foundation is going to
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  absolutely come,
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  successful at the end of the day. The
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  integration, the data
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  aggregation, the quality and insight, and then the
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  reporting of that, it all plays,
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  you know, cohesively
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  with each other. And that is the part
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  I feel like we can really help a
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  lot.
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  Yeah. Ajay, I I know in a lot
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  of these AI conversations that I've heard or
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  participated in myself, the data foundation is something
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  that's stressed
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  pretty,
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  universally
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  among experts such as yourself. So I appreciate
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  you sharing that.
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  And I'd love if you could just walk
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  us through what an enterprise data management strategy
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  actually looks like in practice.
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  Maybe there's a lot of moving pieces. So
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  if we could just give our listeners kind
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  of a more granular sense of what that
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  data management strategy looks like. And for a
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  CIO who's heard that term
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  frequently, you know, in vendor pitches,
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  what does it mean to really get it
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  right?
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  Yeah. And, you know, we we as a
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  company, we are moving towards from being enterprise,
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  you know, in broad solution part of company,
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  and it's a journey for us.
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  We started with being a data plumbers. From
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  being a data plumbers, we started getting into
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  becoming a enterprise interrupt solution for the company.
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  And from there, as we look at it,
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  we are slowly and slowly becoming an enterprise
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  data management
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  as a company.
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  Or we we call ourselves very proudly, we
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  are your enterprise
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  health care data partner. But enterprise
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  data management, it's a platform. It's a foundation
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  for a data driven health care strategic initiatives,
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  and it's the innovation across the care of
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  continuum that we're trying to provide.
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  Now what comes in enterprise data management platform?
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  It's your interface. It's your interoperate. It's the
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  translation of data. It's the aggregation of the
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  data, and it's the access of the data.
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  And as you as you look into it,
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  you know, there are multiple pieces to it
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  with our enterprise data management we are bringing
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  in. You know, we are trying to build
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  a network strategy, maintain a legacy data, experience
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  that speed to value that everyone is looking
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  at it today.
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  You want to accelerate your innovation, you want
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  to put your AI
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  elements on top of it, that innovation we're
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  trying to accelerate.
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  If you look at the complete data visualization,
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  that is achieved.
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  In in small words, I can actually just
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  say that, you know, we are trying to
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  make sure the competitive advantage,
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  the models that you are putting, it all
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  starts with the quality and connectivity of your
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  data. And we are actually trying to make
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  sure we can do this.
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  We there are there are a lot of,
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  you know, there are a lot of sales,
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  lot of noise around and enterprise data management
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  or data strategy. It's easy to say than
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  to actually put it in place.
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  But we have proved ourselves today,
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  you know, when you look at our lab
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  network, two third of this country, you know,
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  they access their lab order and result through
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  LK. If you look at our partnership with
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  Commonwealth,
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  there are more than 200,000,000 patient transaction which
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  happens through us. So we are we are
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  we are making sure that, you know,
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  the AI play is not just a pilot
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  mode, but we are we are trying to
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  build build a future of five to ten
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  years strategy
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  across the
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  across the segment of, health care.
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  Mhmm. I really appreciate you kind of translating
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  enterprise data management strategy for us, Ajay.
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  I feel like we have a much better
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  picture
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  of what that actually looks like. And,
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  you know, just to take this one step
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  further,
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  I guess, I'm here to close this out.
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  If you're sitting across the table from a
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  health system later today, especially someone who's in
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  informatics or technology,
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  what is the one infrastructure investment that you
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  would tell them to prioritize
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  in the next year to really set themselves
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  up for meaningful
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  improvements with AI?
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  I think, you know, it's not even fair
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  to say. If there was one one advice
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  I can give and that can solve all
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  their problems, it's easy. Right?
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  But but the fact is
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  we are we are in this time frame
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  right now
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  where innovation and the changes of innovation with
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  AI are changing every day.
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  Lot of excitement around that. At the same
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  time, you have so much budget constraints.
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  You know, you are trying to do more
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  things in less money today.
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  And,
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  these budget constraints, they're real.
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  And everyone is looking at it, what can
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  I get the best out of a product
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  with a great ROI?
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  And my my simple simplification with everyone is
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  that, you know,
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  you got to actually look at your foundation.
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  You have to look at your interoperability. You
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  have to look at the number of systems
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  that you're working and how you can actually
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  bring all that data together with the one
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  single strategy.
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  Enterprise data management definitely plays a big role
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  here.
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  AI is going to keep evolving. New models
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  are going to keep coming. New vendors are
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  going to keep coming. New partners are going
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  to keep coming, and you will end up
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  doing more and more. But your foundation,
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  and especially your data foundation, data governance
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  has to be on the top. That is
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  definitely going to be one thing that that
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  is going to differentiate.
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  The organization
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  that win, they're not going to be one
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  that were adopting
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  any new technology first, but they are the
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  one who are going to build the infrastructure
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  to sustain it for the longitude.
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  Mhmm. Remember, you also have to look at
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  the
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  compliance piece. You have to look at the
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  security piece. You have to look at the
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  changes happening in regulations.
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  And all those things can happen only with
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  the foundation of the data layer that you're
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  putting together.
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  Ajay, it's been so insightful hearing what a
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  sound data foundation can enable in terms of
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  innovating in the first place or just responding
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  to the rapid changes that you're speaking to
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  that are happening right now and will only
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  continue
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  as AI evolves. So I just wanna thank
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  you for for sharing your time and your
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  your thought leadership with our listeners today. It's
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  been great learning from you. Well, thank you
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  for the opportunity. And, you know, I always
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  love Becker's. I come to Becker's, and I'm
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  looking forward to attending in person. And I
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  hope,
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  more and more people can actually get advantage
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  of the conference.
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  Oh, Ajay, thank you so much, and we're
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  excited to see you at our events this
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  spring.
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  And listeners, we'd like to thank you for
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  joining us, and, of course, would also like
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  to thank our sponsor for today's podcast, Elkay.
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  Be sure to tune into more podcasts from
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  Becker's Healthcare by visiting our podcast page at
• 12:47
  beckershospitalreview.com.