At athenahealth,
we know your ambulatory practice wants healthier, a
healthier business, healthier care teams, and healthier patients.
But the complexities of modern health care tech
make it hard for you and your care
teams to focus on what matters
most? That's where athenahealth can help. Our AI
native all in one solutions reduce administrative burdens,
streamline billing and payments, and deliver critical insights
when clinicians need it most. That means fewer
clicks, more time for patients, and stronger bottom
lines.
Practicing medicine is complex, but running a practice
can be that much simpler with athenahealth.
See how simpler is healthier@athenahealth.com.
Hi, everyone. This is Scott King with the
Becker's Healthcare podcast.
Brilled today to be joined by Donald Casey,
associate professor of internal medicine
at Rush Medical College. Donald, how are you
doing today? Thanks so much for joining us.
I'm good, Scott. Please call me Don.
You got it, Don. Will do.
Yeah. Appreciate you joining us. We're gonna have
a a great conversation today. A lot of
kind of big topics and trends,
in health care to discuss. But before we
get there, I was wondering if you could
just please tell us a little bit about
your background.
Sure thing, Scott. First of all, thanks for
the invitation. It's been a pleasure to be
working with Scott Becker's team,
over the years on on a lot of
great events and and a lot of great
insights, so I appreciate the opportunity to connect
with you.
I am by by training a general internist.
I trained at Rush,
in Chicago here
for first half of my career and then
later went into management, and then ultimately,
where I am now. I'm sort of a
hybrid of everything I've done in my career.
I am no longer practice internal medicine nor
am I in a health care delivery setting.
I'm really focused mainly
on the on the medical side as faculty
at Rush where I'm an associate professor of
internal medicine,
but also at the Thomas Jefferson College of
Population Health where I'm an adjunct
professor of
quality
patient safety
and also population health. And then,
I've also, for the past eleven years, been
on the faculty of the University of Minnesota
Institute for Healthcare Informatics
up in Minneapolis.
And that's been quite,
interesting,
in my career to be hanging out with
all these health care data scientists. So,
and then I'm a subject matter expert in
a number of a number of areas. I
was just in
Boston
this past, Friday with a group called ISER.
You may know about them.
Institute for Clinical and Economic Review. We're really
focused on,
as one example of, where I work, on
rational pricing for new,
drugs,
that are coming on the market,
especially those that are very expensive. So it's
it's a hybrid of a lot of things
at once, and, it's doing me well at
my point in time.
No. You you certainly are doing a lot
of things. I appreciate the the background info
and the specifics there, Don.
First topic I kinda wanted to get to
is, you know, nearly half
of medical practices reported using AI in some
capacity last year and remains a key topic
for health IT leaders.
From your perspective,
what are the use cases that are making
a difference right now
and how are you leveraging them in in
your organization?
Yeah. I I appreciate that question. It's it's
one that, I see often. And,
again, I with the disclaimer, I'm no longer
in medical practice, but I am a patient,
and I'm involved with number of health systems.
It's interesting that,
we talk about nearly half are, quote, unquote,
using AI.
And I would I would venture to say,
since AI is not a new topic, we've
been using AI, so to speak,
for years
in many, many capacities. Now in particular, I
think,
as borne out by the recent event that
I attended at, at Becker's.
Certainly,
medical practices from a business standpoint
are beginning to use a lot more
strategies
and,
and in information systems, for example, that deploy
AI techniques. And and those are, you know,
very vast in terms of,
the issues. I've said, for example, the big
one's reducing workflow burden. That's big one, you
know, using natural language processing
so that, physicians and other clinicians don't have
to spend time
standing in front of the,
electronic health record as much we hope.
So that that reducing that burden is certainly
front and center in terms of the applications
that are coming up, not just with the
large electronic health
care records, vendors, but also with, you know,
other groups that are trying to solve for
specific problems and specific,
specialties.
Obviously, revenue cycle is
a big deal too. You know, you gotta
keep the lights on, and revenue cycle
is a is a,
it's it's
it's a big game in terms of and
I'll use my that's my term. But, obviously,
with keeping up with all the rules, regulations,
and requirements of
submitting,
claims and then getting paid for them is
becoming,
I think, a big deal in terms of
just making
it easier, more accurate, better, obviously avoiding any
sort of
problems with fraud and abuse
and cybersecurity.
And then I think the the biggest part
that's exciting for me from patient care is
the access to
clinical information
in many fronts. Some of it is good
and some of it isn't so good. For
example, the alerts that come up in the
electronic health record,
we're trying to minimize
those that matter so that they don't burden
people. And yet on the other hand, the
amount of information that's much more readily available
through,
the use of, let's say, Copilot, which is
what I use. But there are others
compared to just using search strategies. I I
sort of think search is, for the most
part, unless you're trying to find a restaurant,
pretty much, out of out of favor and
that the ability to put more rational and
useful information together
on the informatics side is becoming,
very exciting for me. This obviously translates into
other issues such as clinical decision support. But
also, and and we're gonna get into this
a bit more,
Scott, is patients are using it
immensely and showing up in the in the
health care environments with lots of interesting and
useful information.
And, that's requiring the the clinicians at the
front end to
handle that with respect, but also,
to be sure that we're not getting us
going down the wrong pathway with with, let's
say, a hallucination about some strange drug that,
is used,
you know, in the woods, so to speak.
So,
we'll we'll leave it at that. I I
think the, the bottom line is it's it's
an exciting and particularly
growing,
challenge and opportunity.
Yeah. Don, that's really interesting because we we
talk obviously with a lot of systems
about using AI. But you mentioned patients coming
in and and, you know, kinda making,
the appointment or whatever, therefore, easier and more
efficient because they they have used AI and
are prepared for that meeting. What what uses
are you seeing from patients specifically? What are
what are they doing?
Well, I I I would say,
let's put it together. I mean, we have
the the doctor
slash clinician relationship that includes,
other other,
practitioners,
who may or may not have doctor in
their name. Yeah. But, you know, you can
look around,
Scott, and let's just say this has been
going on for a while. I can name
I wrote down a few specialties for which
the amount of information
requires
sort of these data science machine learning applications.
I I don't like to use the term
AI because it gets too hyped up, and
I think it sounds
to a lot of people
who aren't familiar with this like it's something
new. I mean, it actually
first was coined almost about,
seventy years ago at my alma mater at
Dartmouth, had a had a meeting. The first
that was the when the first term
artificial intelligence appeared. But if you look at
let's let's just tick off some specialties. Is
that right? Radiology.
Huge opportunities here to
look at, for example,
reading by,
a nonhuman
on top of
what radiology experts are using to enhance, not
to
not to substitute,
better insights.
Pathology, you know, that's a huge area where
the the for example, the tissues that are
being
analyzed are subject to tremendous amounts of computing
power
in terms of learning. You know, just pick
the cancer cells, for example.
Dermatology.
Right? That's another area that
is is really being used on a daily
basis. So patients can walk into their dermatologist
and have, let's say,
a problem with risk of melanoma and be
subject to cameras that will
be used using artificial intelligence to help guide,
not only the presence of melanoma, but perhaps
the risk of it. Oncology is a huge
issue,
something that I've been focused on a lot.
Cardiac electrophysiology.
I mean, you if someone goes in for
atrial fibrillation, for example,
the amount of data in these electrophysiologic,
mappings when ablation is done is huge. You
know, there's biosensing
in in the world of, cardiac cardiovascular medicine
all over the place. Ophthalmology,
you know, getting your eyes checked
with a camera that then can automatically, without
even seeing a doctor, be, subject to an
analysis that tells you if you're at risk
for, let's say, early
macular degeneration or diabetic eye disease. I mean,
these things are in practice now, Scott. So
what I'm what I'm trying to say is
that,
there are,
you know, a growing growing large number of
use cases, so to speak, from the standpoint
of patient care. Certainly on the business side,
we we talk about it a lot. But
from the standpoint of what matters in health
care, that to me is where
the new evolution and discovery are very, very
exciting.
Absolutely. And, Don, as virtual care expands from
AI enabled tools
and remote monitoring to broader digital health platforms,
introducing new technology
brings new challenges. So what advice do you
have for leaders navigating everything from governance to
patient engagement?
And
can you share an example
from your experience that's kind of balanced innovation
with operational constraints?
Well, obviously,
there's the monetary aspect of this. I mean,
you can't you don't you don't have limited
unlimited budgets like some large health systems
do. I mean, Mayo Clinic's got a whole
office building with IBM across across the way
from it.
But that's obviously,
a business strategy that is also enhancing Mayo's,
you know, supportive
care.
That's an extreme example. But from day to
day, the average health system doesn't have that
type of act,
access, so to speak.
I'd say, let's back out of that question
for a minute if I might. I mentioned
sensing.
I mean, sensing devices that patients are wearing,
and I don't like the term wearables because
not all sensing devices are wearable. But,
I'm working, for example, on
a device that can measure your blood pressure
every beat.
And, if your heart rate is is 60
per minute, that's 3,600
blood pressures taken in an hour.
So whether we like it or not and
these are this is one of
many dozens of different types of devices around
the world.
And part of it is we don't know,
you know, what's real and what isn't. The
the ability to validate these devices
you know, we're just getting started on it.
But, certainly, you're gonna need, let's say, that
we use the term artificial intelligence to sort
out
what these new devices are actually telling us.
It isn't just, you know, my blood pressure
was one forty anymore.
So I think that patients
are are
already using more and more AI in their
daily lives.
And,
you know, how how do we decide what's
what's good and what's bad? And that's that's
a an interface that, health systems have to
really start working much more hard on. This
is this is a new game. Certainly, in
in the inpatient side,
hospitals are using,
you know, machine learning
and predictive modeling and all these other
things for people with really sick,
who are really sick in, let's say, intensive
care units trying to find out who's at
risk for sepsis, etcetera. I mean, there's a
lots lots going on in that regard that
apply to the daily practice.
And with that comes responsibility again to be
sure that we're not causing harm.
And my colleague, Constantina Alferas, who's the head
of IHI, the Institute for Healthcare Informatics
at University of Minnesota, has a book out
about 450 pages saying the,
it's it's called the,
the strengths, so to speak, and the perils
of artificial intelligence. And it's it's a book
I recommend to everyone. It it's
available. I'm not pitching the book. I'm just
telling you that's 450
pages.
So we have a lot a lot a
lot of, thinking to do about
how we, go down this road.
I also think that in the health system,
to your point,
I I think that,
leaders can't just shoot from the hip and
delegate and say, wow. Here's an it's something
with AI that we're doing.
I think they have to get down in
the weeds every day
and continuously
get hands on with what's happening. Alright? Because
clinicians, as even some of the specialties I
mentioned that are relevant to health systems,
are already doing this. And so the other
the other lens is governance can no longer
be passive.
They have to show up and be, ready
to support
the health system as well. So it's a
it's a it's a hybrid of looking at
the organizational
structure
from the standpoint of who exactly is accountable.
And I think the leaders have to hold
themselves accountable as well. I mean, hospitals, as
we know, will
more and more become what I call, centers
for advanced sick care.
That's without question, not gonna change.
But as we know, you know, there are
new areas where, you know,
patients are using,
different aspects of this, including hospital at home.
That's one paradigm. It isn't the only paradigm
to get out of the hospital and not
be in there. So I think that,
from my standpoint,
the best thing they can do also is
e even within their health system is engage
the clinical experts in the specialties.
As one example that I spoke about,
with the use cases that show, and this
is the important word, Scott, impact
on population health, not just, wow, it did
x, y, and z, and someone likes us.
I mean, that's important. But on the other
hand,
there's a lot of this that's embedded in
the technology and will become,
heavier and heavier
as we go along. And so I I
do think it requires,
leaders to, in essence,
get out of the house and learn as
much as they can.
And as I said, read books like, doctor
Alifaras and find out what the what the
risks are, not just the benefits, because there
are risks.
Absolutely. And it's certainly a very
timely topic that everyone does need to kind
of stay current on education with,
of course.
How are you seeing recent legislation,
both state and federal, affect health care organizations
and health care IT
specifically.
Have you adjusted
strategies in response?
Well, I I think that the question
there there's a lot in there.
I I do know because I watch c
span a lot that certainly if you just
look at the, commerce department, there's a whole
lot centered in that one area of the
government
that's focused on this large scale
discussion
of not just AI, but the advancement of
again, I don't even like the term digital
technology
around the world. I mean, there are all
sorts of issues with cybersecurity that we can't
even imagine right now, right, as one issue.
But, you know, that's important
to pay attention to from a legislative standpoint
to to not just
wait for a bill to come out, but
to listen to these discussions that are occurring.
I I find, quite frankly, people get upset
about politics.
If you listen to the commerce committee in
the house,
there are a lot of smart people there
who actually have like minds about some of
these issues such as cybersecurity.
So,
I I think it's, if you're you're just
gonna learn about it watching the news or
reading the the New York Times, forget about
it.
I do do spend a lot of time,
specifically on the regulatory side. The FDA,
the Food and Drug Administration has something which
we nicknamed Cheddar,
c d r h, Center for Devices and
Radiologic Health,
is really
evolving,
its regular regulatory approach, not just to the
devices themselves, but something called,
and this is their term, software as a
medical device. So you can imagine
let's say you've got an app on your
phone that will tell you if
you have signs of depression and that you
should seek help.
Those things have to be regulated from a
from the standpoint of being sure that these
aren't,
you know, hooting and hollering about nothing and
just sort of stuff. I mean, we're talking
about peace people's health and people's lives now.
And they're just beginning,
to look at, they've been they've been at
this for several years. I I would bet
you most, health care leaders are not even
aware of what CDRH is or does. But
I think they're at least looking at things
like AI enabled device software functions,
which includes,
how
manufacturers
should handle updates,
monitoring, and transparency.
You know, then these things are
always,
subject to lots of change on a regular
basis given
the,
evaluate the evolution of the software, but also
the evolution of information that comes out of
the work.
So that, you know, if we're just talking
about, let's say, algorithms,
how are we learning and improving over time?
And then the amount of data that we're
generating
is enormous. It it's beyond belief.
And the use of real what we call
real world evidence is also
coming about so that these devices, for example,
that are occurring everywhere
contain information. We have to analyze those
and,
and use,
you know, them to
not just evaluate
the
device itself from a post market surveillance standpoint,
but for effectiveness evaluation. Right? That's really what
we're what we're looking for.
And, you know, here in Chicago, if I
can put a pitch in, Scott,
we have an exciting time because
and this is a little bit off message,
but I do think it's going to be
applicable to health care.
We have a giant effort in quantum computing
down on the lake that's begin just beginning
to start up. I'm sure you've heard about
this.
And it's amazing to me to see that
University of Illinois, University of Chicago Northwestern, Illinois
Tech, which used to be IIT and some
other,
local universities here are going together
out to Silicon Valley to raise money for
this. There's also IBM, Google.
You know, you can you can throw all
the big names into that.
And quantum computing is going
to obviously take a lot of energy, but
it's also gonna increase the speed
by which we, are now using the so
called AI enabled devices here,
which means that,
we have a long way to go in
terms of
learning as much as we can about, what
works. And so that to me is kind
of exciting for the future and an example
of why
Chicago is, still a pretty exciting place to
be.
It certainly is.
Don, you shared so so many great thoughts
on where technology is at in health care
and so many great practical use cases. I
what I wanna ask for your last question
is, what's your top piece of advice for
health care leaders as they prepare for further
advancements in technology
and rising demands for care?
Okay. Well, now you've now you've got me
right in my sweet spot. My number one,
number two, number three, number 10, big dot
is high blood pressure.
Yeah. It's not anything that anyone pays attention
to in the C Suite or in the
Governance Room. But to me, it's the largest
problem in the world.
And I'm wearing a device.
I wore it last night, but I'm wearing
a device that measures my blood pressure every
beat. I told you about that.
And I think that,
I I would advise
each of these,
leaders to get something that's validated
and learn more about their blood pressure, not
I went to the doctor's office, you know,
in April, and it was one thirty eight
over 70.
I mean, that's like saying the temperature in
Chicago in April
was
62.
And therefore, the temperature in Chicago the whole
year is 62. It's just just not correct.
Right? So I I would say,
learn learn some of these. Get get to
get to learn some of these things. Get
down in the weeds. Go go into the
radiology
department and see how the radiologists are using
this. Talk to the,
cardiac electrophysiologist.
I'm I'm dealing with specialists here, but, obviously,
I think the frontline clinicians in general internal
medicine and family medicine can easily
tell you and show you how they're beginning
to to use this type of information. Certainly,
we wanna worry about revenue cycle too. So
maybe they can have a conversation about documentation
and coding as well. But I I think
it's just get out of the house and
get in the weeds.
Don, thanks so much for joining us. It
was a great conversation. I look forward to
working with you again soon. Thank you, sir.
At athenahealth,
we know your ambulatory practice wants healthier,
a healthier business, healthier care teams, and healthier
patients.
But the complexities of modern health care tech
make it hard for you and your care
teams to focus on what matters most.
That's where athenahealth can help.
Our AI native all in one solutions reduce
administrative burdens,
streamline billing and payments, and deliver critical insights
when clinicians need it most. That means fewer
clicks, more time for patients, and stronger bottom
lines.
Practicing medicine is complex, but running a practice
can be that much simpler with athenahealth.
See how simpler is healthier at athenahealth.com.