Welcome to Hard Problems, Smart Solutions - the Newfire Podcast, where
we dive into complex challenges and ways to solve them with top industry leaders.
I'm Gordon Wong, VP of Data and AI at Newfire Global Partners,
and your host for this episode.
In each episode, we bring you conversations with top innovators
and decision-makers, tackling the biggest issues across industries.
Whether you're looking for insights to drive your own strategies or to learn
from the best, you're in the right place.
Let's get started.
Hi, everybody.
Welcome to the first episode of Hard Problems, Smart
Solutions, the Newfire podcast.
Today, I'm thrilled to introduce our guest, Emily Lindemir, Executive
Director of Data and Healthcare Innovation at JPMorgan Chase Co.
Emily has led data science initiatives at Cityblock Health and
Wellframe, focusing on innovative AI solutions to improve healthcare.
She holds a PhD in medical engineering and medical physics
from the Harvard MIT Division of Health Sciences and Technology.
Emily has been instrumental in advancing how we use data to
improve healthcare outcomes.
This conversation is especially timely, as many organizations Including
us and our clients at Newfire.
We'll look at how AI can move the needle from analytics to
actionable insights in healthcare.
Welcome, Emily.
Thanks, Gordon.
Great to be here.
Excited for the conversation.
Yeah, me too.
I've been looking forward to this for weeks.
Um, so a little bit of background for those of you who are listening.
Emily and I actually have crossed paths multiple times within the
Boston healthcare ecosystem.
Uh, we've actually worked at some of the same firms, we've consulted
with each other as colleagues.
And so I have a lot of respect for Emily.
So I'm, again, I feel really privileged to have this opportunity
to have this conversation with her.
And so today's topic is, you know, when we were looking for guests to explore
this topic, Emily is really one of the first people that came to mind, because
of her focus on data science and AI and really, impactful, actionable insights.
Today, we're going to dig into AI's role in healthcare transformation.
Right, so Emily, you've been at the forefront in this industry for a
while, and arguably, your entire career and education has led to this point.
So, how do you see AI evolving from really just analyzing data and producing
descriptive analytics to driving actionable business focused solutions?
It's a great question, Gordon.
This is one of my favorite topics and probably one of the things
I get asked about the most.
Before I answer, let me give a little bit of an overview of kind of like
where my journey in AI healthcare has been just for listeners to
see kind of like what I've seen.
Um, I, I started my career in the imaging world, which is where AI,
I think, really started showing its first promise in healthcare.
Um, it is the perfect application of AI
images are, um, you know, we think back to, like, Identifying
cats versus dogs, right?
Like the early, early applications of AI that were across the world.
Like you can apply them to medical images as well.
Um, and so I started in neuroimaging and one of my first jobs out of grad
school was working at IBM Watson Health,
where I was part of their AI imaging team trying to make new algorithms
that we were bringing to market, um, in mammography, chest CT.
My career slowly transitioned out of imaging more to broader health systems,
more broad healthcare applications, kind of working with digital health
companies, working with, uh, providers like Cityblock, where Gordon and I
crossed paths, um, and really kind of seeing more at a global scale
rather than singular applications, how AI, and just data science in general
was evolving across the industry.
And so just to say, I did all of my training like way before LLMs came out.
Um, there is, we are in a new generation of AI when we're thinking about
LLMs and generative AI and, um, and so I have a lot of thoughts there.
I think that where we are today is that there's a lot of excitement about AI.
People are making discoveries, training algorithms to be incrementally better
than the last and sometimes more than incrementally at like light speed.
But if you know healthcare, you also know that healthcare kind of like
exists on somewhat of a delayed timeline when it comes to data and technology.
A lot of our data and infrastructure in the industry isn't there yet
to support these super advanced technological applications.
So, I am kind of coming at this conversation from a place of, um, I've
kind of, I've seen under the hood of what our data infrastructure often looks like,
and, and I'm coming at it with this view of a little bit of skepticism, honestly.
So I'll, I'll give you that as sort of a backdrop for my perspective.
And it really is just my perspective that I'm sharing here.
So let me, after I say that, let me give the question back to Gordon and kind
of where do you want to go with that?
Yeah, absolutely.
So that actually resonates with me quite a bit.
Um, because as a, uh, someone who's been building data platforms for
more years than I care to count, um, we've been asked do many
cutting-edge things, but frequently the organizations are not ready for it.
So I use the term pragmatic cutting-edge a lot, and I suspect
that might resonate with you a bit.
I love that.
So let's, let's really drill into that then, right?
So we, we are really looking for, you know, solving for
actionable business problems.
Right?
So, you know, what, what, where are you seeing progress in terms of you
using AI to actually move the needle?
So I think about AI and healthcare in like these two broad camps.
There's administrative and there's clinical.
Um, and people are interested in both and there's really, really
important applications in both.
If we start with administrative, administrative are the things like, rev
cycle management, billing, coding, um, even just scheduling patients, like, all
of, all of these things in healthcare that aren't directly caring for the patient,
but cause a lot of burden on the industry.
Clinical applications are the things that I think we think about as like
these really sexy, like, going to move the needle and change patient care.
Those are things like automatically diagnosing a patient or, you
know, prescribing like the absolute best treatment regimen
based on a patient's history.
Um, I think that where we are today is really just the first camp.
The real promise and the early promise of AI and healthcare is going
to be on that administrative side.
I say that, and it's maybe not as exciting, like, Oh, why can't we do
like these crazy futuristic things?
I'll get to that.
I think the industry is just not there yet, but I also wanted to
say with positivity about, or speak with optimism, I should
say, about those administrative tasks and the promise there.
And the reason is that part of the reason that the US healthcare system is just so
complicated, so expensive, so burdensome, is the tremendous amount of administrative
burden that we have placed on it.
And so actually using AI to alleviate some of that burden is a great thing.
And I think will actually pay really meaningful dividends in the end for
patients, providers, the system at large.
Um, so I can go into details there, but just to say, like, I think about those
two camps, and I really think about the administrative side is where we're going
to see change in these next few years.
I, you know, I actually really agree with you.
I see that, I, I see that too.
And I've, I've, I've seen the statistic that perhaps something
like 40 percent of our healthcare spend goes into administration.
So it feels like of opportunity there.
That's right.
And I mean, you have to, I think, believe that a lot of things will
chain together and, and lead to that trickling down to patients having
cheaper and more affordable care.
But that's what I think the goal should be.
As an, or as an industry, when we think about, okay, we're going to really
tackle this administrative problem with AI, I think that we should say more
than just: let's make the burden lower.
I think that the goal should really be, and make it cheaper for patients.
Make the whole system, patients included, less burdened by this extreme cost.
Because you're right, it is up to 40 percent administrative
costs in some cases.
So at Newfire, I'm heading up data analytics and AI.
So I have the opportunity to speak to a lot of our prospects in terms of how
to use AI within their organizations.
And so if, you know, what's your guidance to them, like what problems
should they look at first in terms of using AI to, to improve things?
Um, so my advice there is, I think this is an obvious question for a
lot of business leaders, but it's worth saying is, you know, what is
the ROI if you solve this problem?
Um, a lot of people, like I said, they really want to tackle these
really exciting clinical things, and I don't want to discourage from that,
but we actually don't know what the ROI is often of solving
some clinical problems with AI.
I think that the ROI is much more clear for some of these administrative.
So if you're a business leader, I mean, that really should be the first
question you're asking yourself.
Um, you know, the second question is kind of this technical
feasibility question, right?
If you are thinking of building and selling an AI product as a company,
you have to think about do all of your customers have the technical
infrastructure to support this?
Do they have, like, the data to support this?
And that is often not the case right now in healthcare.
There are a lot of health systems out there who are, like, just
migrating to the cloud, you know.
They, they don't have the tools to load all their data into some kind of
AI algorithm and get an answer back.
Um.
So that's the second thing.
I think the other thing is generalizability, you know, is what
you're building something that can generalize, can safely exist outside
of some very, very small testing grounds that you've built it in.
That's a trap that a lot of people fall into, I think, with AI, is they can
build something that performs amazingly, and then they take it and they try to
have it perform somewhere else and it doesn't do as well, and I think the
trust starts to really erode in the industry, um, with AI for those reasons.
And I think the last thing is, like, keep an eye towards the regulatory concerns
around what you're trying to build.
Those administrative tasks that I mentioned are often not so, they're
not so prone to regulatory challenges, whereas the clinical side of things,
that's a big hurdle to get some kind of AI cleared by the FDA for actual
patient use in the clinical setting.
So, you know, keeping those things in mind, I think, is really critical.
Now, acknowledging that healthcare companies, uh, come in lots of different
sizes and different flavors, but I think about Fisher Price, my first AI project.
Um, which C level officer would you typically think would be like a
good first customer for a user AI?
That's a great question.
I have, um, I've been talking to some folks about this recently, and I was
just invited to, like, a roundtable to listen to chief information officers and
how they're being approached about AI.
And interestingly, I think what's happening right now is I think
it's the CFOs of most organizations who are being approached about
AI and the decision-makers
and that comes back to that ROI question and the fact that a lot of these are
really being targeted for a lot of these applications are being targeted
for administrative tasks, internal efficiency gains, things like that.
So what you might think of as like these CIO, CTO folks who are first
approached decision-makers, I think we're actually seeing that it's more people
on the financial decision making side.
Yeah, that, that resonates for sure.
You know, part of the pur purpose of this, uh, this podcast is really
to give our listeners some ideas of where they can get started, right?
Pragmatic advice.
In that vein, do you have any specific examples from your recent history
where AI has made an impact in terms of patient outcomes or operational
efficiency or just reduction of toil?
Definitely.
So some of the applications that I've seen that I think have been really
exciting in AI, that are, they, they just are adjacent to clinical, but they
really are administrative applications, are things that help reduce the
burden of clinical documentation.
So, really solving this problem of providers are getting burned out,
they're spending way too much time having to write up patient notes, sift
through patient notes and synthesize past medical information about a patient.
When you kind of boil that down, those are information synthesis problems,
which is what AI is really good at.
The applications that I've seen that do things like ambient documentation.
So for example, a voice recording, just like you and I are doing right
now, of a patient-doctor conversation, which gets translated to text.
That's easy.
That's been around for a while, but is then with an LLM translated into medical
jargon that can go into a patient's chart.
That's incredibly powerful.
That, you know, that saves the doctor so, so much time.
And I think I've seen amazing reviews and feedback from clinicians themselves
on how powerful that type of technology is going to be in the clinic.
I've also seen a lot of applications of AI that I think are really
promising from like an investment and investability perspective of revenue
cycle management, billing and coding.
Those are heavily manual processes right now that are prone to error
that I think AI is excelling at.
And then this is my favorite example, just kind of throwing back to my imaging days.
This is not AI the most recent example that, I encountered this years ago,
but I remember at an organization I was at, we were working on really advanced,
really cutting-edge neuroimaging where we were developing technologies that
could, like, automatically segment images of the human brain into all of these
amazing, very descriptive statistics.
And it was hard to sell that because it was hard to tell a clinician why this
would help improve patient care or like why this would save them money for many
nuances related to like human brain stuff.
But um, it was amazing technology that couldn't find a home.
Conversely, there was this technology coming out at the same time that could
take a cue of like a hundred images in an emergency room and automatically identify
if any one of them was a brain bleed.
And just for those listening who don't know, almost any of us could be trained to
a spot a major brain bleed pretty easily.
It's a very visually obvious thing.
But so what this algorithm did, it wasn't like finding some really hidden
issue, but it would find something that was incredibly emergent that might be
really low in the radiologist's read queue and pop it to the top and say,
basically, if you don't read this image, this patient might die in the next hour.
And that's like an orchestration task, right?
That's like a clinical workflow task, but it was such a better at
the time application of AI, if you really wanted to, like, help patients.
So I just put those two side by side.
It's one of my favorite examples here of, you know, sometimes the
simpler solution, the better one.
I love that.
I mean, it reminds me that we should be always thinking about kind of the basic
variables that drive outcomes, right?
Time, timeliness, reduction in effort, all these things, right?
So, you know, uh, stealing from the product manager book, so looking at
your typical, let's say healthcare provider, what person or role would
you want to target to, uh, delight with AI within that organization?
Whose job do you make it easier?
How do you help some be more successful?
That's such a good question.
So, personally, I think we should be really making things for patients
and physicians, like, the people delivering and receiving care and
physicians need so much tooling like this to help with burnout.
So I think delighting the physician, the ambient documentation, question
or example I gave is perfect there.
However, usually physicians are not the financial decision-makers,
they're not purchasing your product.
And so, I do think that you really have to be able to always show delight to
the customer that's paying in the end.
And so, being able to say to them, this is going to allow your physicians to
spend X amount more time with patients, or, show some real financial ROI there is
really, really important to keep in mind.
Yeah.
Thank you for that.
I, really, that really does make a lot of sense to me.
You know, I personally have always loved the lens of like trying to delight
somebody because we could understand that.
But let's take a, let's get a little speculative for a second, right?
Think about the future AI in healthcare.
What do you think, what are you excited about?
What do you think are the big opportunities and challenges
we might be able to tackle?
So what I'm excited about, this is, this is a hard question.
I've actually been asked this a few different times and I'm always
kind of evolving my perspective, I think, on a regular basis.
Let me zoom out before I answer what I'm most excited about and tell you what
I think some of the biggest problems are and we'll, we'll work backwards.
Always lead with problems.
Some of the things in healthcare that are just, in my mind, so broken
and in need of change really do come down to what we started talking
about was data infrastructure, right?
Data fragmentation and data infrastructure.
This, like, shows up in a lot of different ways.
This shows up as technology not being able to scale across organizations,
technology not being able to generalize.
And one of the things, I think, that's becoming more and more
apparent in the US is kind of health disparities across the country.
There are growing parts of America that are losing doctors.
What are those people doing?
Like, that, that, that's going to cause more chronic conditions and everything.
And so I think when we think about like, where do we really want to be, like,
where's technology really, really going to help us in the next 20, 30 years?
Personally, I'm not sure that it's going to be in these super futuristic things of
like, doing a full body scan and finding like the one cell in your body that is
potentially cancerous that I think is like a lot of the sci-fi things we think about.
I think the promise is really, how do we get equal care
to people that live in every single setting in America across the whole
country when there's obvious resource deficiencies and the supply and demand
is just like not the same everywhere?
So if someone can figure out how to fit AI into that problem, like, that is what
I think will really, really drive change.
Really lofty goal, but it's probably the thing I think about the most when I think
about AI and tech making a real impact.
So, to put words in your mouth, I think you're reminding us that AI
is a tool, not the end, the outcome.
Yes, I think that's a great way to put it.
I mean, there are so many healthtech and healthcare companies out there
right now that are using AI internally, but they're not like AI companies.
And I think that is the right way to be thinking about it.
You know, use AI to get done the things that you need to
get done and do them better.
But sometimes when we have a hammer, everything looks like a nail, and
we, you don't need to become an AI company to really change healthcare.
Let me share with you some, I'm seeing a little bit, I'd like to know if
you're having similar experience.
So, you know, in Newfire, we, we do talk again, we talked to a lot
of clients about implementing AI.
I've, I've noticed a funny thing, it's because you mentioned data foundations
and fundamentals that made me think about this, was that as we talk about
AI, frequently the conversation starts becoming like, oh what's the state of
your data platform, your data warehouse, your data quality, and it's driving
more awareness of these systems that have been languishing for a long time.
Are you seeing the same thing?
Yes.
I think that that's a great observation.
People are, to your point, like, have kind of allowed their systems to deprecate
It just not be as advanced anymore.
And now they're like, Oh no, I can't, I can't apply the cutting-edge technologies.
And that's okay.
I think that this is a great impetus for us to all look and say, how
do we build a better foundation?
So we definitely are seeing the same thing.
And that's why the problems that excite me are actually really these
infrastructural problems and solutions so that we can enable these things
that are much farther down the road.
So let's take some of the implementation challenges off the table for a second.
Where do you wish you could build an AI solution?
Well, what problem do you wish you could address with AI?
That's a really good question.
I will, I'll go back to what I said about like this really future vision.
I wish that we could figure out, and I mean plenty of people are working on this
just to be clear, and I think plenty of people like share this view, this is not
something I just thought of, but this idea of like a person who lives in rural
Arkansas who has a stroke and cannot get seen by a neuroradiologist in time.
Yet neuroradiologists exist in this country who could read their scan
and advise, bringing expertise to places where it currently does not
exist is where I would really want to focus my time in like an ideal world.
And it's not just telehealth.
It's not just like connecting, uh, a radiologist who's remote.
It's maybe it's not even connecting a radiologist, maybe it's actually making
an AI that can automatically read that image, which people are working on.
Maybe it's creating better surgical techniques and tooling and suites that
do more robotic tech surgeries, right, that don't need as much human expertise.
I don't know.
But I do think that the thing that's most exciting to me is like
fixing this supply chain issue.
Thanks for sharing that because it makes me, it allows me to sort of
categorize, um, you know, something we, we see a lot at Newfire.
So we have the privilege to work with a lot of healthcare startups.
And I realized what a lot of them are trying to do is bring
expertise to the problem, right?
They're trying to solve for this expert gap and they're trying to bring it to
the patient, which to me, intuitively feels like the right thing to do.
I totally agree.
Yes, I've seen some amazing healthtech startups out there that are, this
is exactly their model, is you know, there's patients out there that need
a certain type of care and either the place that they live or the insurance
coverage they have, or maybe both prohibits them from getting those things.
And I mean, and even at Morgan Health, we have done research and we've learned
that physical lack of access to care is a key driver of poor outcomes and, you
know, misdiagnoses more so than not being able to afford care, which is interesting
because sometimes we think of them as being the same thing, but physical access
is different than financial access.
I have colleagues in Canada who are in healthcare and they are trying
to tackle this problem is very, yeah, a front-of-mind for them
because of course how disparate Canada is in terms of geography.
Oh, that's really interesting.
Yeah.
I'm sure it's an even bigger challenge for them there.
Particularly around things like LGBTQ care, just having a
primary care physician, if you're in northern British Columbia.
Yeah.
I mean, and you know where we see this a lot actually is, is
mental health in the US too.
There are, there's kind of like a dearth of local mental health providers who
will take insurance, and then when you talk about specific populations,
like LGBTQ populations, it's like you have an even harder time of finding
somebody near you in your network.
And so connecting expertise across like geographies is huge.
Emily, similar to me, you've been both a provider of technology,
also a consumer of technology.
So let me give you an opportunity as a customer and a consumer of
technology, of solutions, what do you want companies like Newfire to build?
How can we make a bigger difference in this industry?
Scalable
Scalable data platforms.
I think something I have experienced, and you alluded to this earlier, is so many
health tech companies are out there, and this is, completely not a knock on them.
I I've lived it.
I understand why these decisions are made, but so many health check companies are out
there building internally from scratch.
They, they're coming in and they're saying, how do I build this, like,
bespoke something, data platform, data warehouse for my needs.
And, as I said, there's many reasons why people choose to build versus
buy, but it really, I think, is not always a good long-term investment.
And I, I wish that there were an entity out there, and maybe it's Newfire, who
could build more kind of scalable data platforms so that these early healthtech
start, healthtech startups that have a lot of promise don't actually need
to burn all that capital building what they think they need to do so bespoke.
Um, I could, you know, we could do a whole podcast on build versus buy data
warehouses, so I won't go further.
I, I, I think that's that, you know, and I do think the, the
world is moving in that direction.
That's one of the benefits of having gone remote.
Of course, there's a lot of costs having gone remote, but there's an
opportunity to pull in expertise of all types from around the world and,
you know, in different time zones, um, you're starting to bear some fruit.
Yeah, yeah, definitely.
I mean, the world has changed quite a lot.
I will say I do so many people are still, you know, building
the same thing in like silos.
Well, so Emily, do you have any other, uh, any, uh, final words you want to
share as we wrap this conversation up?
30 minutes goes by really fast.
It goes by so fast.
I would say I, I hope everybody like stays excited, stays hungry about
all of the amazing opportunities there are right now for AI and just
data in general and healthcare.
I'm a, I'm a first principles type of woman.
I think a lot about like, "what is my base case?"
anytime I'm trying to solve a complex problem.
And I think approaching things that way when we're thinking about these
like lofty, complex technology problems is really helpful and
grounding and sort of actually driving more effective solutions early on.
And, often your base case is going to come down to infrastructure,
which we talked about a lot.
So that's my, my parting wisdom is to try to approach problems in that way.
Yeah, this is not a, uh, this is not a 30-day mission, right?
This is a lifetime mission and none of us can do it by ourselves.
Yes, definitely.
I, I think that state working in healthcare data is, um, there will
always be work to be done for sure.
Well, Emily, thank you so much.
This has been an awesome conversation.
I really hope that our listeners have gotten something out of this.
I think some of the topics I've heard were AI is here.
It's making a big difference, but you need to be careful
about what problems you tackle.
Don't be afraid of focusing on efficiency as that may have the biggest
impact on your patient outcomes.
And finally, look for partners who have the skill sets to help you be successful
like our clients at Newfire have.
Yes, that's great.
Thanks Gordon.
This was a great conversation.
Thanks Emily.
I hope you have a great day.
Cheers.
Thanks, Gordon.
You too.
Thanks for tuning in.
We hope today's conversation with Emily Lindemayer, Executive
Director of Data Healthcare Innovation at JPMorgan Chase &nCo.
has given you a fresh perspective on how AI is transforming health, starting
with the foundational need to streamline data and reduce administrative burdens.
Stay tuned for more episodes where we continue to explore the toughest
challenges and smartest solutions in business and technology.
Like and subscribe.
Until next time, keep innovating and solving the hard problems.
This is Hard Problems, Smart Solutions, The Newfire
Podcast.