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- This is Laura Dedo with the Becker's Healthcare Podcast.
I'm thrilled today to be joined by Dr.
Neil Tala, medical director of structural heart disease
and intervention for the VA Eastern Colorado Healthcare
System, as well as Assistant professor at the University
of Colorado School of Medicine, and Co-founder
and chief Medical Officer for High Labs.
Dr. Al, it's a pleasure to have you on the podcast today.
- Thanks for having me. Excited to chat today.
- Now, I'm looking forward to our discussion.
I know you've got a lot of really neat things going on, um,
right now, but you know, as we,
before we dive into the deeper discussion,
can you tell us a little bit more about yourself
and your background and how High Labs came about?
- Sure. Happy to. Um, so I founded High Labs.
I co-founded High Labs when I was doing my MD MBA, um,
at Yale, uh, at this point, you know, almost 10 years ago.
And, and there, uh, initially we founded the company is
almost like a, a pop health company.
Um, looking, you know, myself
and my co-founder, who was an executive MBA student at Yale
at the time, um, who was a tech technologist.
He'd worked in a number of tech companies and startups.
Uh, and so we sort of wanted to combine his AI background
with my healthcare background
and really tried to, um, you know, figure out if we could,
you know, predict who's gonna get sick
and make them better before they get sick.
And, uh, we realized pretty quickly that, um, a lot
of the data going into these models was total garbage.
And so we shifted upstream, uh, to focus on, uh, using AI
to improve data quality in healthcare.
And that actually got, uh, a lot of, uh, traction.
And so, um, right now High Lab sort of serves, you know,
for the top 10 largest health plans as well as a number
of other regional health plans and, uh,
and small specialized insurers really working
to improve data interoperability
throughout the healthcare system.
And, uh, improve the quality of data to then enable people
to do cool things like predict who's gonna
get sick and, and make them better.
Um, so that's sort of my background and,
and how I labs have started and how it's evolved.
- Well, that's amazing to hear.
And certainly, you know, having that mindset
and understanding what data can do in medicine
and healthcare, um, has been really important
and it certainly, um, accelerated growth in,
in many respects over the last year, um,
and a lot of work to be done too in the future.
What do you really see as being some of the, um, you know,
big changes that you're excited about?
Where do you see as technology today
and then headed going in the, into the future?
- Sure. I mean, I could focus a little bit on what I,
I specialize and do research in as well as work
with High Labs in an then top bigger picture about,
you know, healthcare data quality.
You know, one thing that, um, you know, we found out, um,
last year from my academic hat, I did research on, you know,
the quality of provider directory data, um,
and across, you know, health plans.
Uh, and that in the study was published in jama, uh,
last March, which really showed that about, you know, 80%
of all physicians, um, have some incorrect information
and health plan provided directories.
And that, um, it's a pretty
profound, you know, a large number.
It's consistent with a lot of other studies
and, um, really goes to show the scope
of this provider directory, um,
inaccuracy problem in the us.
Um, and it's funny 'cause I talk to, you know, um, you know,
pretty much anyone I talk to about, you know, this study
and, and sort of this research
and really what High Labs does, um, you know, I'm like, oh,
you know, provider directories are incorrect.
Like, oh yeah, here's an example of where I had, you know,
took three hours to find an appointment
to get a physical therapist or something.
So people in the US just assume
that this information at Health Plan product
directories is not gonna be accurate.
And I think that's, um, you know, an industrywide problem,
um, or a healthcare system-wide
problem that really needs solving.
Um, so moving forward, you know, that we,
we demonstrate the scope of that problem, you know, um,
do this research and have another paper that's sort
of hopefully coming out in the next month
or two, characterizing a little further, um, last year, um,
I think, uh, this actually is informing some new legislation
coming through, uh, hopefully, uh, in the next few months.
So, um, the Senate Finance Committee is looking into, um,
you know, the, uh, using, you know, legislation
to tackle ghost networks.
And ghost networks are essentially, uh, you know,
providers listed in health plan directory, uh,
health plan directories
that aren't really practicing if locations are listed at.
And this is a big problem in mental health in particular
where, you know, a a patient who's, you know,
having enough issues as is accessing a be behavioral health
or mental health provider looks at, uh,
provider up in a provider directory calls them up or,
or even goes to place in, uh, a physician's office.
And really they're not practicing there anymore.
And so that's actually caused a lot of, you know, friction
and really decreased access, uh,
or led to sort of the maintenance
of decreased access to care for a lot of people.
And so the Senate Finance Committee passed, um, you know,
I think the, the Real Pro Health Providers Act is a,
is a legislation that passed unanimously actually
by partisan support, um, I think last month.
And that hopefully should be up for discussion of the house,
um, in, in January or so.
That's something that, um, I I'm definitely watching,
at least from my research angle,
from provider directory standpoint, um, as to, you know,
how the, uh, legislation sort of actually goes through
and what impact it has on, on he the healthcare industry.
- That's fascinating to hear about. Yeah, absolutely.
- And, and one more thing I'll, I'll add to that.
So just a little more on the provider directory piece, uh,
from a high lab hat, um, you know, they, one of the things
that High Labs does is actually uses technology
to queen provider directories.
And this again was, you know, born from the idea that,
you know, what's, you improve data quality in healthcare,
what's the first interaction somebody has
with the healthcare system finding a doctor?
And that is often a challenge for a lot of people,
and particularly those, um, you know, with, um, you know,
who are most affected, you know, most comorbidities,
the ones with the, you know, um, you know,
we socioeconomic disparities, ones
with the least health literacy, those people are affected
the most by this access to care problem,
even getting in the door to see a provider.
And so, um, you know, high Labs focused on improving
provider directories for that reason.
And that actually has gotten a lot
of traction in really using technology
and AI to clean provider directories.
Um, and so that I think is,
is a solution a lot of people are employing now.
Um, and again, I think this
legislation will sort of buttress that.
Um, 'cause I do think, you know, the root
of the problem is not just like, uh, you know,
health plans are doing a bad job
or providers doing a bad job.
It's fundamentally an information disconnect problem.
And, um, that I think, um, can only be solved by technology
and happy to chat about that
and other solutions, as you know, again, I think
that's gonna be an important thing coming into play this
year as if legislation sort of, um, goes through, um,
at least sort of the one, one of the big things I
think I'm excited about this year.
- Yeah, yeah, definitely. That really seems to be a,
a huge shift and,
and could be make a big difference for the research
and the data and, and how that's used.
Now. I'm wondering too, um,
when you look at artificial intelligence, especially,
you know, it's important to employ the
responsible AI as a physician.
Could you talk a little bit about that
and what that really means
and how you're viewing AI
altogether in the healthcare space?
- That's a great question. So I,
and a couple of different perspectives on that.
So first, you know, as a practicing clinician,
I think we're still a ways away from AI
to clinical decisions before.
I think there was actually just a paper out a few days ago
in, in jama, uh, on, um, you know, whether using an ai, uh,
to sort of help someone read a chest X-ray
or guide a clinical decision making,
and it's helpful or not.
And I think there's a lot of gaps still.
Um, and even explainable AI where it shows you
where exactly the chest x-ray, you know, is concerning
to an AI algorithm, I think there's still a lot of room
for improvement there in terms
of guiding clinical decision support.
I mean, that's a goal. I mean, that, that's like a, a goal
that we really should get to.
And I'm hopeful that the kinks can be worked out,
but we're not quite there yet.
Uh, despite all the excitement around it.
Uh, where I do think AI is actually gonna be important in
healthcare, um, is actually gonna be upstream of that, uh,
addressing the boring problems in some sense, um, of data
and, and how people, you know, um,
you know, deal with operations.
I think AI is amazing at, you know, uh,
improving operational efficiency.
And this is seen across industries, right?
So whether it can, you know, automate
email marketing campaigns
or automate, you know, generation of, you know, logos
or it could, um, automate some back office stuff
and, um, you know, uh, take notes through meetings, a lot
of the operational things that AI is certainly, um, geared,
you know, to do perfectly that are sort of road tasks.
And I think that can really improve, um, you know,
manual processing of a lot of things that happens,
particularly in healthcare where everything is.
So, you know, it just takes a long time for, for innovation
to really, um, take hold in a lot of these business,
you know, established business processes.
So I think that's where their, you know,
AI has a more direct impact.
And I think that's really where we're,
we'll see the first sort of, um, changes as a result of ai,
and again, it's gonna be solving, solving
the boring problems, right?
Like, so how is, you know,
how are we gonna do billing in, in healthcare?
Like, how is that gonna be improved by ai?
How is prior, prior authorization gonna, you know, uh,
be more efficient or how are we gonna, you know, improve
provided directories or improve, you know, transmission
of clinical data from one hospital to another
or improve, you know, um, you know,
interpreting clinical data from
another hospital or something like that.
So I think all these sort of data problems, a lot
of operational issues where a lot of manual works involved,
I think that's certainly going to, uh, be right
for transformation with AI as at least in the near term
and the clinical decision report,
that stuff we all dream about, uh, where, you know,
it's like a, you know, AI driven, you know,
physician or clinical judgment.
I think that's still a ways away just based on at least the,
the research I've seen and the step I've seen on clinical
practice and is practicing as physician, right?
I mean, there's only a certain, you know, I think we're,
it's gonna be challenging, um, for, um, some of these, um,
AI models to really, um, recreate, uh, the level of, um,
you know, clinical interaction
that a physician has with a patient.
And there's multiple dimensions to it.
It's not just what a patient says
or, you know, it's a physician
or types into a box in an AI engine.
It's, it's like, you know, what's the patient's, you know,
what's their tone like when talking
to you about something sensitive
or that actually informs a lot of the clinical judgment,
which I think is gonna be hard to replace
with ai, at least in the near term.
- That's helpful to know, you know,
and definitely, um, important to keep in mind as we think
through how AI can really affect
healthcare and where it's headed.
Um, I appreciate your analysis there. Um, Dr.
Al, is there anything else you wanted to connect about,
especially looking over to the next
two to three years or so?
What is exciting, um, and
and what do you really see as, um, you know, medicine?
How is it evolving in, in, uh,
especially thinking about the data and,
and information that clinicians as well
as healthcare organizations will have in the next few years?
- Yeah, I mean, one other thing is just, um,
interoperability of clinical data.
So right now, you know, there are regulations
that are coming into place, especially the next year,
that really, um, are encouraging the use of, um, you know,
standards like fire, FHIR, fire, um,
to transmit clinical data from one hospital to another.
So let's say I have a patient who comes in
with a heart attack in my hospital
and, um, you know, they just had a procedure done
another hospital a few weeks ago.
The ability to transmit that information in some sense,
it's required actually, uh, now by law.
And the ability to interpret that information once it gets
from one place to another is gonna be key.
And so as more people start complying
with these regulations, I'm excited for the potential to,
you know, for this data interoperability to, uh, you know,
reduce doing, redoing unnecessary tests
or really improve, um, you know, clinical decision making
of these more information about a holistic, you know,
a patient's holistic medical record across different, um,
entities, whether it's across the health plans,
across physician groups, across hospitals.
So I'm really excited about that, uh, especially
as regulations kind of pushing this along.
Um, I do worry, again,
the data quality piece is a problem that needs to be solved, right?
That's a boring problem that needs to be solved
before the exciting part
where we can actually know everything about a patient
who's coming in with an acute problem.
Um, and so the data quality piece is gonna be key.
And again, that's where AI, again, can help.
I think, uh, and High Labs has worked on this a fair bit
with a couple large insurers.
We see data from thousands of different providers
and being able to standardize that
and sort of, um, read clinical notes as a human can
and then sort of summarize them.
So there's AI ways to go around, around this and,
and address this issue, uh,
and actually, you know, realize the potential
of interoperability, which people have been talking out now
for, you know, uh, years if not a decade.
So that's something I'm excited about with these regulations
and the hope of AI to really, you know,
make this data useful.
I think the regulations that are sort of, uh, mandating some
of these pipes in place between ENT entities,
but then being able to make that data useful once it reaches
and then location, um, I think that's
where AI can certainly help.
And I'm excited for that to also come to fruition, um,
hopefully in the next year, uh, in practice at scale.
- I love it. Dr. Butala, thank you so much
for joining us on the podcast today.
This has been a really fun and interesting conversation,
and I look forward to connecting with you again soon.
- Problem. Thank you for your time.
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