Latest / Becker’s Healthcare Digital Health + Health IT / Shahidul Mannan, Chief Data Officer at Bon Secours Mercy Health

Transcript

Shahidul Mannan, Chief Data Officer at Bon Secours Mercy Health · Becker’s Healthcare Digital Health + Health IT

• 0:00
  - This is Laura Dedo with the Becker's Healthcare Podcast.
• 0:03
  I'm thrilled today to be joined by Shahi Manan,
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  who's a Chief Data Officer at Bon Secours Mercy Health.
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  Shahi. Do, it's a pleasure to have you on the podcast today.
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  - Same here. Thank you, Laura.
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  - Now I'm looking forward to diving in to our discussion
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  and really learning more about
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  what you're doing at Bon Secours to, um, stay on ahead
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  of the curve and use data and information
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  and technology in a better way.
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  But before we dive into my questions,
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  can you tell us a little bit more
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  about yourself and your background?
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  - Yeah, thank you. Thank you for having me.
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  This is, uh, great to be here and, uh, talking to you today.
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  Uh, so I'm the Chief Data Officer at W Secor Mercy Health.
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  And in this role I oversee anything, everything related
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  to data analytics
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  and, uh, now, uh, going into cloud platforms,
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  data analytics, um, and ai, uh, driven innovation
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  and then, uh, data characterization.
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  And we have data partnerships
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  and, uh, uh, various, uh, productization work, uh, uh,
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  in my, uh, overview as well.
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  And, um, I have been in this role for about two years
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  before this when I was with Mass General Brigham, uh,
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  in a similar role, um, as head of engineering
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  and, uh, head of innovation.
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  Uh, and, uh, I've been in the intersection of, uh,
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  big data AI technology for over 20 years now.
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  And, um, over a decade in financial FinTech space, um,
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  then high-tech space for several years
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  and, um, now in healthcare for some time.
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  And, uh, I think healthcare is, uh, uh, is a great space
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  to be, uh, being in data
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  and AI as, uh, we are on the verge of, uh,
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  huge innovation cycle in my belief.
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  Um, and I think it's a great time
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  overall being in technology,
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  but especially in healthcare,
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  we are seeing the transformations
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  through digitization coming to a maturity level, uh,
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  where we are gonna jump to the next level with AI
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  and innovation with data.
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  So very excited to be in this role and in this space.
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  - Well, that's great to hear.
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  And you know, I think you're right.
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  It's such an exciting time to be in healthcare right now
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  and really being on the precipice of
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  what technology can bring to this space
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  and really support caregivers as well
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  as the health system at large.
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  I think when you look at everything that, um, you know,
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  we've seen over the last year,
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  could you talk about one change that you've made recently
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  that's yielded some great results?
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  - Sure, yeah. And, uh,
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  before I do that, I just wanna also talk about a little bit
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  of the context of how, uh, data
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  and ai, uh, is coming into play in healthcare today.
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  Uh, as we all know, the, uh, biggest trends, uh,
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  we are seeing is more and more focus on value-based care.
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  And, uh, actually McKinsey is projecting that the number
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  of patients covered in this space will be doubling in the
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  next few years, and it'll become over a trillion
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  dollar, uh, market.
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  And in that space, uh, if you look closely, you'll see data
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  and analytics have huge role to play
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  and the digital transformation that I talked about.
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  Uh, so the key drivers of value-based care,
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  if you look at it, are in my mind, um, mainly threefold.
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  One is, of course, uh, the value from, uh,
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  better patient outcome and having better patient engagement,
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  and then overall reducing the operational cost
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  and bring in more efficiency.
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  And if you think about it, our data is at the core of all
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  of these, and we can have so much, um, innovation
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  and so much, um, enhancement of these drivers
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  to data AI applications.
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  And that's what, uh, i, I think is the biggest, uh,
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  value that we see from, uh, data
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  and AI driving healthcare digitization
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  and the transformation in this space.
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  And in that context, what, um, me
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  and my team are doing is focusing on the next generation
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  of data enablement
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  and then, uh, specific innovation using, uh, big data
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  and AI for better patient outcome, better engagement,
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  and driving the cost down where we can
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  or bring in more efficiency.
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  The one thing that made a big difference in my view is we
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  put out a new multi-tenant, uh, big data cloud,
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  uh, analytics platform that is
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  consolidating not only our clinical and EHR data,
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  but all system data
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  and helping to build that longitudinal patient 360 view
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  and connecting it with other, uh, clinical as well
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  as operational supply chain, hr, uh, billing,
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  financial, all information together, uh,
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  to drive better insights.
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  And then we already have, um, are seeing the value, uh,
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  coming out of it with, uh, various types of insights
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  and analytics that we are helping out across the system
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  to drive better operation
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  and also have, um, six to eight AI driven use cases
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  that are in production based out of these, uh, data assets
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  and these analytics capabilities.
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  So we are very excited about that,
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  and we certainly have a plethora of use cases
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  and application areas utilizing these platform
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  data and analytics.
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  - Well, that's great to hear, you know,
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  and certainly exciting that, um, you're able to, uh,
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  really utilize that data platform
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  to make such a difference within the workflows and,
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  and see the information, um, that, you know, uh, is,
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  is flowing throughout the health system.
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  I'm wondering especially, you know, for, uh,
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  as you're looking at the data and the information,
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  how do you effectively communicate that
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  with leaders of different departments?
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  And then how does that information flow down to be as, um,
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  I guess make a difference in, in become actionable items,
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  um, within the organization?
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  - Yeah, great question.
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  I think, um, you know, this is such a large space
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  and it touches pretty much any and every department
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  and every key stakeholders, key players, uh, leaders
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  in, um, in a system like ours and in general in healthcare.
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  So you're right, the big part of it is that, uh, evangelism
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  that can help develop the strategy that covers across,
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  uh, departments across our system
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  and to build that vision together
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  that can help everyone align, uh, on this value proposition.
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  And that's what I have been my team have been focused on
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  over the last few years.
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  And we made a great stride by building out our roadmap,
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  not only our roadmap,
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  but also, um, building out the capabilities
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  that I was mentioning and demonstrating value.
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  And I think three things come into play when you want
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  to have that alignment across leaders
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  and, you know, large system across departments,
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  and then adaption for data ai and the application of those.
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  And, um, not only development of those,
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  but actual operationalization of those which are
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  building the vision, building the vision and strategy,
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  and then building the roadmap
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  and execute by demonstrating value.
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  And we have been very on that
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  not only building the foundation
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  and building the long-term goals and capabilities,
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  but also demonstrating value incrementally in a three
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  months, six months period at a time.
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  So what that does is it helps people understand
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  and process the strategy and the long-term goals,
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  but also helps build the credibility
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  by actually showing the results
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  and actually demonstrating the value.
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  So that's how I think we have a good run
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  and, uh, we are focused on doing more with, uh,
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  larger collaboration across the system.
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  - That's great to hear and certainly an exciting time
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  to see all that evolve.
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  Now, I think when you zoom out into healthcare in general,
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  what are some of the top trends
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  that you're following right now?
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  Um, what is top of mind for you as you're going about,
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  um, planning for the future?
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  - Yeah, um, great, great time again, uh,
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  to be in healthcare
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  because of those reasons, um, that, uh,
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  there are certainly exciting things happening
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  and a lot of trends in technology as well
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  as overall healthcare transformation.
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  Uh, so the top ones I would start with certainly is, uh,
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  on the technology side.
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  Uh, we see more and more machine learning, um, data driven,
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  uh, various capabilities, uh, from realtime alert system
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  to personalized medicine to, uh,
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  more customized patient 360 driven patient care,
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  uh, which certainly is, um, you know,
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  improving patient's experience, improving various outcomes.
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  Uh, for example, we, uh, have rolled out
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  the risk stratification for patients to have more
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  targeted care for certain risk, uh, patients in COPD
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  or in behavioral health or CHP and now in diabetic patients.
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  And that certainly, uh, can,
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  and, uh, we are seeing that result in better outcome from
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  the patient's per uh, treatment perspective.
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  And then we are, uh, able to focus on more, uh,
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  understanding better diagnostics, early detection,
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  uh, and then building out real time alerts on various
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  operational levels.
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  For example, uh,
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  during covid time we rolled out the real-time monitoring
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  of our bed sensors
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  and then extended it to various supply chain, uh,
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  related items, which was very critical.
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  And that is, that trend is ongoing on various
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  other operational areas.
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  So all these are, I think, um, enabling the system
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  to be more efficient and more focused on patient engagements
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  and better patient outcomes.
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  So all in all,
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  I think the biggest trend is these machine learning
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  and data driven various applications.
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  And the latest one of course, uh, you probably hear it in
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  all conversations is the gene AI applications.
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  So I think that is also gonna pick up a lot, uh, in terms of
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  handling and managing the patient's, uh,
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  unstructured information, uh, building out better chat bots
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  that can interact with the patient, uh, more like WebMD,
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  you know, at your fingertip anytime, uh,
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  and various other gene AI
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  and, uh, LM driven innovative technologies evolving
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  that's gonna grow bigger.
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  Uh, so those are, I think some of the top ones,
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  but overall, um, the adoption of machine learning
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  and uh, AI driven applications is, uh, certainly, uh,
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  in the top of all of these.
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  - That's really cool to hear.
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  And definitely, you know, super exciting
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  to think about the possibilities once you've got, um,
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  harnessing that artificial intelligence,
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  large language models and can really adapt it broadly.
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  Um, you know, the idea
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  of personalized medicine is particularly exciting as to
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  what it can really bring to that healthcare experience, uh,
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  especially when you're looking at the next 12
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  to 24 months or so.
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  What does growth look like for you within the health system?
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  How do you really see your teams
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  and yourself, um, expanding the capabilities?
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  - Yeah, sure.
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  Um, I, I think with the new,
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  or kind of I would say more, um, upgraded version
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  of our platform where we can enable more
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  and more, uh, independent data science teams or more
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  and more other, um, independent organizations
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  to enable their analytics activities
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  and innovation activities is gonna be big.
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  So we call it, um,
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  self-service democratization, uh, environments.
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  So anyone who doesn't have the full cycle of,
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  uh, technology expertise
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  or full uh, stack of, uh,
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  talents in-House would be able to come
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  and, uh, play with the data
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  and innovate with their ideas on our platform
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  and would be able to, um, we would also be able
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  to integrate their data
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  and provide it in a more meaningful fashion.
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  And not only that,
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  we would also look at operationalizing the artificial
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  intelligence capabilities that we have built
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  and shared the goodies with larger, uh, group of, uh,
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  hospitals in internal
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  and potentially external eventually.
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  Uh, we haven't fully opened up to the external yet,
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  but the platform, the capabilities
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  and the innovations we have are moving towards, uh,
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  enabling, uh, US to do that.
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  And I think that will be a big jump in terms
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  of our services, in terms of our value extraction.
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  I'm super excited about that, that, uh, this multi-tenant,
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  uh, and information asset is gonna help us,
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  uh, with that path.
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  The other part is we are
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  actually continuously working on several use cases that are
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  in those three key areas
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  of value-based care that I mentioned.
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  And, uh, our, um, teams are working hard to add those
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  to our, um, capability
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  and application areas, uh, over the next year
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  and we should see more and more, uh, application and value
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  and adoption of those.
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  Um, the last one is we are also partnering with various, uh,
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  other systems, other data
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  and technology partners for more innovation,
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  more revenue generation generating opportunities.
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  And those I think would be also coming to the next level
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  of maturity for, um, revenue generation
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  and value extraction.
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  So exciting and busy time ahead.
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  - Absolutely. For sure. You know, it seems like, um,
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  so many opportunities and ability to, um, jump in
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  and really transform the way healthcare is, is moving.
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  And in particular, when you think about those value added
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  opportunities or revenue generating, um, areas, you know,
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  what does that really look like?
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  What are some of the, I guess the discussion points when you
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  look at how, um, you know, you can continue
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  to add value in revenue to the organization overall?
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  - Uh, sure. Yeah. So I'm gonna give a couple of examples.
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  So one is that, uh, we have actually, um, also, uh,
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  with the team and some
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  of these technology partnership solved out
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  and built in the capability to de-identify our patient data.
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  We are extremely, um, uh, uh, cautious
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  how we handle our data, respecting the privacy
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  of our patients and, uh, being compliant with HIPAA
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  and other regulations.
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  But, um, this de-identification
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  and anonymization technology has enabled us
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  to share the data for more innovation
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  and revenue generation.
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  So that's certainly something that, um, has also, uh,
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  helped us build the partnership with some
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  of the technology companies who are focused on,
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  uh, targeted innovation.
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  Uh, for example, we are working on building out a chat bot
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  that, uh, helps, uh, patients, um, respond
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  to their questions
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  and queries, uh, real time anytime of the day.
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  It's like, uh, WebMD on a chat bot.
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  Um, that can help, uh, with patient engagements
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  and patients' understanding, um, of the situation
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  or the problem and guide them
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  for the next steps, uh, instantly.
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  We are also looking at using LLM for nurse triaging, uh,
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  for better efficiency and again, better patient engagement
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  and, uh, experience.
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  We are looking at various, um,
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  operational machine learning driven applications.
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  Some are already in production for, uh, reducing
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  or optimizing hospital stay length of stay
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  or, uh, predicting hospital readmission
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  so we can better take care of those high risk patients.
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  So all these are in play
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  and some of those through partnerships or innovation
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  and productization, we are hoping will earn our, um,
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  more revenue and, uh, expedite some more innovation
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  and some will extract more value by cost reduction
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  or optimizing our operations.
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  Um, and there are, these are some of the, I'd say top
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  of the iceberg, uh, high value items that we are focused on,
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  but we are constantly looking at more opportunities
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  and we have, uh, you know, built out kind of a queue of, um,
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  a next set of use cases that we'll continue to work on.
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  - That's amazing to hear.
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  Well, thank you so much for joining us on the podcast today.
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  Shahi, do. This has been a really fun discussion
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  and I look forward to connecting with you again soon.
• 17:54
  - Thank you. It's been great, uh, talking to you
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  and a pleasure being here.
• 17:58
  Thank you for having me.