- Your depiction of where we are
and where we're gonna be in three years,
five years, 10 years,
is remarkably exciting.
And I think your analogy to
the phone is exactly the right.
Bob and I were probably
thinking back to our first days
when we were taking calls for, uh
- For GUSTO.
- GUSTO calls, exactly,
and we were carrying around a phone
that was pretty darn heavy.
- Yeah.
- In a bag. And now, to
think about, on the way here,
to try to get around the
traffic in San Francisco,
I was using, we were using
our cell phone to find
various ways, and where every single car
on the side of the road was
marked on the technology
that was telling me which
way to go, at which moment.
- And by the way, 10 years
ago, the iPhone did not exist.
Most people don't realize that.
10 years ago today, the
iPhone did not exist.
- So, no one in San
Francisco needs to be told
technology is gonna move,
and the world's gonna change a lot.
I love the fact that you chose
your examples predominantly
around cardiovascular
disease, which, in our minds,
has been at the cutting edge
of a lot of new technology,
be it stents, be it in
the medicines that we use.
So, to think of us on the
verge of a new era of medicine
that is using the technology
you're talking about
is quite exciting, and I know
for the Heart Association,
leading that charge is
pretty darn exciting.
I guess my question, though, is, you know,
when we're talking about
using that technology
to monitor people 24/7,
we're thinking about
using that technology to make decisions
and help to guide where we're going.
It implies, in part, that
there is a partnership
between people like yourself,
who are both on the side of
consumers of health care,
and also people like Dr. Harrington here,
who is actually having
to deliver healthcare.
And that's, in part, what
this conference is about,
and I think, in part, where
the first question is.
How do these two worlds meet up?
And maybe, Bob, why don't you
sort of describe a little bit
of your first thoughts here.
- Well, first, I want to thank
you for being here tonight,
it's a privilege to hear
your thoughts on the topic.
The thing that first goes through my mind
is I wish Epic were a public
company, so you could buy it.
(laughs)
And fix it, because it's a disaster,
for the reason that you've pointed out.
It's interesting, if you ask doctors
about the Electronic Health Record,
there's been a lot written
about the misery it caused,
but if you also ask doctors,
would you want to go back to a world
without the Electronic Health
Record, they uniformly say no
because we all recognize
the power in being able
to aggregate information and
to be able to, as you've said,
make decisions based on that information.
To me, there's been sort
of two fundamental flaws
in the way that a lot of health
technology has developed.
It's either been
developed by technologists
who don't necessarily understand
the clinical imperative,
or it's been developed by
clinicians who have a good idea,
but who have no sense of technology.
And somehow, and I like
the fact that the HA
has brought a lot of us together for this,
we need to merge those two worlds,
so that we can both learn from each other
what the issues are, and move it forward.
I like to say that, as a physician,
there is only two types of
decisions I should ever make.
One is that the answer's
known, and as you say,
you can get that answer quickly.
If the answer's known, the
system should just do it for me.
Aspirin after a heart attack
maybe is a good example.
The other is when you
don't know the answer,
and the system should
force me to study it,
and engage me in the study of it.
And we're not perfect in
either one of those yet,
so I'd love to be in a world
where those two things
are coming together.
- And they are coming together.
In fact, there's two
companies I can mention
that are essentially doing this.
Lumiata probably has 60
million medical records,
and for your condition, it
finds the closest patients.
And the way they're approaching
the physician adaption
problem is interesting.
The way they're gonna present it
is go to insurance companies and say,
"Pre-approve the best outcome."
And others, the doctors do
this extra work to justify,
so they're not taking choice away,
they're saying here's one
pre-approved one, here's,
and they provide clinical rationale.
They say here's the
rationale for this decision,
there's other decisions
other physicians have made
out of those 60 million medical records,
and you can provide rationale for it.
By the way, if you have good rationale,
their system learns additional rationale,
so the more it gets
used, the better it gets.
But the doctors, of course,
will handpick the paths
that the insurance
companies will pre-approve,
so you're taking advantage
of a bad thing (laughs),
which is the insurance approval cycle.
- So, it's interesting, as
I flew out on the plane,
I'm having to do my
re-certification for boards,
which is gonna happen next week,
and hopefully you'll hear
that I'm re-certified
by the end of the week.
(audience laughs)
Else, my days as a cardiologist
may be very short-lived.
But, as I said on the
plane, I studied facts like
Noonan syndrome as associated
with pulmonary valve stenosis.
And I'm thinking, why am I
re-learning all these facts?
- That's exactly right.
- When, in fact, they
could be in a database.
If I had a patient, I've never had one,
but assuming I would
have a patient someday
with Noonan syndrome,
I should be able to have a
computer prompt me to say,
By the way, you might
wanna think how their
pulmonary valves are doing.
But, yet, we re-learn
these facts every 10 years,
in part to re-certify as a cardiologist,
yet there's a lot of
other skills potentially,
as me as a cardiologist,
that maybe I should be
tested on, but I'm not.
- I think that's Vinod's point,
I think that we absolutely have
to get to a system of medicine
where we think that way.
You know, one of yours and my
favorite people in medicine,
Gene Stead, thought of this
stuff in the 70s, right?
And one of Dr. Stead's famous quotes is
"Never teach anything
that you can look up."
And he said this in the 60s and 70s,
and what he meant by that
- Before Google!
- (laughs) Way before Google.
But what he meant by that
is that what you should be teaching people
is a way of learning.
And a way of learning is
to be a sophisticated user of technology,
so that you can learn,
I mean, learning about Noonan's, just,
I passed the boards last
year, so don't feel stressed.
(laughs)
- Sorta down, but that's okay.
(laughs)
- But, what a waste of time.
And why are we allowing
the system to dictate
that that's how we're gonna do it?
It's just a waste of time.
- By the way, my cell phone
did tell me I was a little
depressed on the way out here (laughs),
so, I think one of the
programs is working quite well.
- So, let me make a comment on this,
this is really important.
Absolutely anything you can access,
and most facts you can
access, now on Google.
10 years down the line,
you'll be able to not only
access all those facts,
but do complex logic on it.
If you have this, but you happen
to have low blood pressure,
and you're more than 70 years old,
and if you have three
comorbidities that are serious,
then go for a stent.
Otherwise, if you're lookin'
for a prettier optimization
because you're otherwise
healthy, go for cabbage.
That is complex logic on
essentially facts you can look up.
That's the next generation.
We have this complex logical scenario
gets subject to the same
kind of decision-making,
so you don't have to.
One of the most interesting questions is,
What is the role of the human, then?
And we know a patient,
and you could say that for a doctor, too,
but the patient isn't a rational being.
He or she is an emotional being first.
We know because the placebo effect works,
that things aren't rational,
love and support, as Dr.
Dean Ornish would say,
actually change the outcomes.
To what degree?
Different people may...
So, the role became, for the human,
may actually change
dramatically from specialist,
and this what I was referring to,
this integrative, confidence-giving person
that maybe serves a very,
very different role.
I'm intrigued by what that role might be,
and if you're completely
free to essentially just
interact with the patient,
what would you spend your time doing?
- Right. It is interesting,
though, and, in part,
I remember back now to my
earliest days in medical school
at Pittsburgh, you mentioned there,
Jack Myers, in those
days, was one of the first
in the idea of using sort
of this computer technology
to make Kaduceus, which
was a decision-support tool
that was supposed to
revolutionize medicine.
Didn't happen.
We lived through the Duke Databank days,
where we had predictive
algorithms that were helpful
to help decide who should
get bypass surgery,
who should get medical therapy, et cetera.
Those came, and unfortunately,
even went at our own institution,
only to be now rethought
about once again, now,
20 or 30 years after
they were first invented.
So, I would, on the one hand,
I think that absolutely,
the technology is there,
but are we as a field
gonna be in a position
to be ready this time through, to adopt?
In part, you've given
me some solace, though.
Your idea that first generations of things
sometimes are clunky and
are sometimes not accepted.
Great ideas sometime take 20 and 30 years
to be accepted by the humans around them,
who have their own challenges.
- I had an interesting meeting yesterday
with our chair of pathology,
who is going to go through the process
of getting rid of slides,
and digitizing everything.
And his estimation is that 80 to 85%
of what pathologists do could very quickly
be read by a machine,
and identify patents better
than the pathologists
probably do reliably.
- There's plenty of research
to indicate that's true.
Andy Beck, at Howard, has proven
that you can read much more
than a pathologist could offer the same,
you're dealing with cancer pathology.
- And more reliably.
- And more reliably.
We know in radiology that's also true.
- Another question is when you
ask what a doctor's gonna do,
he believes it's the other 15, 20%
where the machine's not
ready yet to read it,
that there's still some need
for the expert interpretation,
and then, but even that, at
some point, will go away.
- Yeah.
Look, a good analogy is, in the 1930s,
as soon as flying airplanes got complex,
the number of crashes
went through the roof
because the complexity,
and autopilots came to be.
The humans really only intervene,
in flying from here to New York,
in exceptional circumstances.
Otherwise, autopilots do
almost all the flying.
We haven't gotten rid of the pilot.
I'll give you another piece of data,
which is very, very intriguing.
The best computer program
in the world easily beats,
at chess, the best human.
But a mediocre computer program
with a decent chess player
can beat any computer.
Which is interesting.
Now, will cardiology or
medicine be like that?
Or something different?
I don't know the answer.
I'll give you another amazing statistic
because these are really critical
to how these systems evolve.
AlphaGo, which Google used to play Go,
is stunningly sophisticated AI.
It's the first system where, essentially,
the system developed could only
be described as great intuition.
Lee See-dol, who is the world
champion, played the computer,
was confident he'd win, he lost badly.
Prior to this game with the computer,
he was winning 75% of his
matches against other humans.
After this, and after studying
what the computer did,
he's now winning 90% of his
matches against other humans.
So, he's become much better.
How that happens in
cardiology or other fields,
it's a little hard to speculate,
but there are indicators
that these systems
will make us better,
and there will be exceptions
where we need them.
- So, we'll learn from the machine.
- We learn from them.
- So, you're imagining
that the next generation
to replace Bob's favorite, Epic,
will be somebody that will compete on
not the ability to better bill,
but on the ability to better diagnose,
and use the data that lives
within their own system.
- Oh, it's gonna be the
computational lens for sure,
that's where the magic is gonna happen,
not on the entry level.
I mean, that's the
problem with Epic, right,
all the energy is in the entry level,
and the computational
part of it is terrible.
- Let me give you a simple example,
something I'm dying to
build one of these days.
If a patient comes in, comes,
walks in the doctor's office,
fills out that little form in pen
that's just stuck away,
somewhere that nobody
ever actually looks at.
In asking those questions,
if you could dynamically
adjust the questions,
so you came in with, say, a bad cough.
It could easily adapt
to asking the questions
that eliminated either strep throat
or something even more serious,
by three or four additional questions.
You could almost finish the diagnosis
by just having them enter their condition,
and exclude possibilities,
especially the rare possibilities
which are always, almost
always, missed early
because physicians don't
run into them very often,
so you're not gonna look for Ebola
for typical patient that comes in.
Or Zika.
Um, it's very interesting to speculate
how those systems actually
finish the diagosis
before the doctor's seen them,
and help the doctor
include rarer conditions
that may be more serious, or exclude them.
Those are really interesting
human/design interaction possibilities.
- People are actually developing,
for emergency medicine physicians,
those sorts of symptoms, I
mean those sort of systems,
where, based on key symptoms,
creating a differential,
and quickly going down, just
in the way that you said,
so you're absolutely
right, it's on the horizon.
- And the first, simplest
version is, in an emergency room,
is to do triage.
That's the most critical,
short-term decision
when somebody walks in the
door at an emergency room,
is triage.
- Right. Is it really bad,
or can I think about it?
- Yeah.
- So, we've decided that in 10 years,
my boards will be passed
by my partner next to me,
so, which will be good, this'll be good.
I can get through.
Now, let's move on to
sort of a second concept
that you brought up,
which is the monitor of self,
and the discussions about where
both technologies are going,
but also, perhaps, how
people are gonna use it.
I guess, the question I have,
and you brought up even
the AliveCor example
where somebody's taking a
bunch of EKGs every week,
is the potential for who's both
adopting these technologies,
and then, potential, maybe even use,
but also misuse or challenges
that come from that technology.
The monitor itself, in certain
ways, has created, perhaps,
the most likely to most
likely to adopt it,
or the worried well,
as opposed to those who
may have illness and/or
that digital divide that exists
for certain groups across America.
Thoughts on those topics.
- You know, I generally
view technology as a tool.
It can be used for good or bad.
It can be used well or poorly.
Nuclear weapons is obviously
the most important.
You can use it for nuclear
power, or nuclear weapons,
technology is not, doesn't
have a value system.
So, it depends on us humans
to decide how to use it,
how to use it well.
Here, take AliveCor.
If every afib episode,
it shows up on the doctor's dashboard,
they call you in, and say,
Here, I now have a billable event.
That's not very good.
On the flip side, because they're seeing
tens of thousands, or
hundreds of thousands
of afib events a month,
if they can differentiate,
further beyond afib,
what's harmful afib,
and what might have negative consequences,
or training the bad way,
or what, you know, yes, it's afib,
but there's a simple at-home,
take it easy kind of.
I think we will have that information
once we have, say, a million afib events
and the right data science
associated with that.
How we use it, whether we
use it to increase billing,
or keep the patient at home,
and then if they're
really at risk of a stroke
or some other event,
we call 'em in, and we can
predict hours in advance,
maybe we know what to do about it.
Those are choices that we will have.
They're incentive systems.
Are you a cost-based system like Kaiser,
which doesn't want to increase cost,
or pay for service.
Incentives will determine
how technology is used,
it's sort of the bottom line.
- So, I know that Mintu
Turakhia is gonna talk tomorrow
about evaluating technology,
including, I suspect,
he's gonna talk about some
of the arrhythmia monitoring,
and how one uses that and tests it
for what actually is the value add,
'cause that's the big question,
and I think that's what you're gettin' at.
Let me turn it over to something
that you think a lot about,
which is blood pressure,
and the big controversy that
came up over the SPRINT trial,
which was a trial that basically showed
that lowering blood pressure a little more
was better than lowering it not so little.
And, pretty simple question,
but great controversy over,
well, did they actually
measure blood pressure
in a way that's consistent with practice?
In some ways, who cares?
They showed that lower
was better than higher.
But, I'm of the mindset that,
why can't we live in a
world where we get hundreds
of blood pressure
measurements, on a patient,
and then figure out what
actually is good blood pressure.
- Right, now you
- But we're not there yet.
- Yeah, no, and we'll talk about
- And this is important because today,
when you take blood pressure
at home or in the hospital,
you have a few data points, right?
So, AliveCor just introduced
a relationship with HOMERuN
where they'll need to automatically
feed the AliveCor app.
If you have hundreds and hundreds
of data points for a patient,
you suddenly start to get intelligent.
Now, the studies that
you're talking about,
it's generally done with
300 or 3,000 patients,
I don't know how big
- Oh, this is tens of thousands.
- Okay, but if you're talking
about millions of patients,
it's very hard to fool the
system, real data comes out.
And you avoid some of
the problems of data,
which is overfitting data,
and I don't want to go too much
into the technical details.
But, you see that so often in studies
because the sample sets are so small
and overfitting becomes easy,
and that's why you have
conflicting studies.
One says one thing, and
another says something else.
It's because of data overfitting
and misuse of data.
- So, you're not gonna
get two people arguing
for very large trials
through much of their lives.
I think we'll support you there.
I think, also, the point
of continuous monitoring
versus episodic is certainly continuous
is going to be better,
and the ability to sort of
see how somebody responds
in their own home environment
versus how they sit in
an office after they sat
for x number of minutes
in a darkened room with a,
somebody takin' their,
monitoring one set of
blood pressure cuffs.
- Oh, it's gotta be a
lot more informative.
We just don't know yet what
to do with the thought.
- Let alone the idea that
even on the response side,
I mean, once we even
diagnose high blood pressure,
the idea of seeing how
the, how do you respond
to that blood pressure
early in the morning
versus later in the afternoon,
how do you do it if you miss a dose,
could you get that feedback
that you, low and behold,
missed the dose, and your blood
pressure's now high today,
good feedback system,
that would be important
for that patient to have.
And those, again, those
technologies to do that,
although they're getting
better and better,
and smaller and more continuous in nature,
even just the blood pressure
cuffs connected to a phone,
as you've already
indicated, or other forms,
are ways in which that technology
could actually reach doctors in real time.
Which brings us to the next problem,
which is that the degree to which, now,
you link these new technologies
and new monitoring systems
to health systems in a way
that will actually gain value
on both sides.
And that's where the challenge exists.
- Well, that's the concept
that the IOM talks about
with the Learning Healthcare System,
is that we've gotta get to
a point where, you know,
I think Vinod is exactly right,
we don't need, what is
it now, it takes 17 years
to actually change medical practice?
We need two, three years.
And the only way we're gonna do that
is if the learning is
built into the system,
so that if we get hundreds of thousands
of blood pressures
coming in to our system,
we're learning from
that, and we're adapting,
like, in many ways, you
gotta take the human being
out of that in order to
get the system to adapt.
- So, we'll talk about this
tomorrow in another example,
but there was actually a good study,
a randomized comparative
trial look at Fitbits,
and how they might help with weight loss.
Everybody thinks that
if you have a monitor
and get feedback, you would
get better weight loss
than if you didn't.
In the study that was carried
out and published in JAMA
just a week or so ago,
the results actually
were counter-intuitive,
where the actual technology
showed less weight loss
than in the side that didn't.
Kind of hard to explain exactly
why they got worse results,
but, in response to that,
the comments were made
that this was old technology,
and the critics were absolutely right,
this was probably two or
three-year-old technology,
and as Mr. Khosla points out,
that cycle within this field,
is two or three generations behind
where the best and latest is.
So, part of our challenge,
both as medical doctors,
but also as researchers, are,
can we develop systems that will keep up
with the evolving technology.
- No, it is intriguing.
First thing I'd say is
studies are confusing.
If you got, if you didn't get
weight loss, why did you not?
People who exercise more,
generally, actually,
don't lose weight, it's just conversion
of fat to muscle mass.
Is that what's going on?
I don't know the answer.
So, you have to ask the
next level of question,
that's one point.
The other point I'd make is,
and I tried to sort of
briefly touch on it,
as these systems get much more responsive
and adaptive, and consumers use them,
I think it will change the rate
at which medical practice changes,
so the 17 year number
is often talked about,
but let me give you a counter example.
As soon as patients start
looking on Google or Wikipedia
for their conditions,
it took probably a few years for Wikipedia
to become the most
consulted medical resource.
Why? Because patients walked
in with their Wikipedia page
about their condition, and
doctors didn't want them
to be embarrassed, so they read that page.
I'm saying patients can
force behavior change,
and much better informed
patients will increase the rate
at which doctors adapt to new knowledge.
I'm very, very bullish,
and that pressure will come
from patients, doing Q and A with systems,
and today, WebMD is a
free, archaic system.
That will become much better
and I'm very optimistic
that will then drive doctors
to be much more responsive
to changes in practice,
changes in knowledge, new studies.
And I'm excited about that.
- I think that's true even
if you watch our trainees,
when you, you know,
it used to be that you'd make
rounds with your trainees,
and you'd say, well, I think
that was in circulation
in, you know, five years ago,
and now, before you open your mouth,
they give you a full literature review
'cause they're sitting
there on the computer.
So, it has changed.
It's changed the way you teach,
it changes the way you interact,
we look things up constantly
as we're walking through the hospital,
in ways that we never did before.
You're absolutely right.
- Is there questions from the audience?
- There's a microphone, here, come.
- First of all, that was very inspiring,
I really enjoyed hearing
this, it was great.
I have a silly question
and a real question.
The silly question is
it says "Fireside Chat,"
and I wondered, is this a video of a fire
or is it a teleprompter?
(laughs)
- We got the Yule Log goin' there.
(laughs)
- So, the serious question, though, is,
a first year engineering
student would tell you,
if you want to control something,
you have to measure a
parameter that tells you,
are you controlling it?
So, blood pressure, maybe.
And you have to act faster than the system
you're trying to control.
That's basic control theory.
So, if you're trying to build a car
that would stay in the middle
of lane and drive itself,
you probably wouldn't see it once a year,
and give it a dose of something, and say,
"Keep taking that and check in in a year."
Right?
So, we're not doing it right,
and I think it's not just
what you measure or how often you measure,
but the whole loop.
You measure the right
thing, you act fast enough,
and you adjust the right parameter.
So, I think there's a long way to go,
but the mindset you
guys established tonight
is really, like I said, it's inspiring.
I really appreciate it.
So, I guess that's sort
of an open question
or begging for a response.
- Well, this gets to Vinod's comment
about aligning incentives,
and, unfortunately,
the American healthcare
system has been predominantly
this fee for service thing,
so, what do you want?
You want people to come back and see you
to adjust their blood pressure medicine.
As we move more towards accountable care,
what we oughta be doing is
caring for a population,
and, so, the incentive, then,
becomes not to have you come see me,
but to help you figure
out your blood pressure
in the context of your life.
And for me to help
manage, or, not even me,
for the system to help
manage your blood pressure
in the context of what
you're doing all the time.
And I think we are moving that way,
and some health systems,
Kaiser's a great example,
Intermountain in Utah's
another great example,
are making big strides towards that.
I mean, Kaiser has now, what, Eric,
80% blood pressure control?
- Yeah, I know, it's
amazing what can be done.
The other thing I would link
to this is that, you know,
we talked about the
redundancies and parts,
that sort of physicians that
don't have to do anymore,
and, in fact, would do better
if machines did it for you.
It's kind of, if you really
want to push this envelope,
I don't know how far we wanna go, but,
if you look at actually
the taking of medicines,
which I struggle to do when I now am on
to my own statin every morning,
and, did I remember to
take my statin today?
It's kind of an embarrassing
sign of my times.
But, should I have to remember that?
Or should there just be a
little implantable in me
that will release a certain dose
of my favorite statin for the day?
And, yet, osmotic pumps
are coming out soon
that will probably allow lots
of the medicines that we take
on a routine basis to be distributed,
even not in bolus form,
but now in a continuous
fashion throughout the day
without me ever thinking about it.
- Well, lots of possibilities there.
There's sort of, I saw
an interesting start-up.
If you're taking four
different medications,
they actually custom 3D
print one pill for you.
(laughs)
So, there's all sorts of possibilities.
But there's an important
element to this question.
A friend of mine at UC San
Diego, Dr. Larry Smarr,
decided about 10 years
ago to do weekly testing
on about, I think it's 30
or 50 variables he measures.
And he found a couple of
the numbers going off-kilter
about three or four years ago.
He went to his doctor and said,
"Hey, these numbers are shifting."
I forget which variables,
but they were pretty normal blood tests.
The doctor said, "How do you feel?"
He said, "Great."
Said, "Well, don't worry about it."
He discovered his
bio-markers detected his IBD
two years before it was diagnosed
in a traditional medical practice.
Retrospectively, if you'd done
that to thousands of people,
you'd be able to detect it early.
And he found, then, over the
course of the last few years,
that the probiotics dramatically shifted
how bad or good it got.
That is an example of measuring variables.
I happen to measure 1,200 variables
on Metabolon test every quarter,
I do a full marker biome test,
I do probably 500 other
variables every quarter.
But, I'm only doing it to
see how much they shift.
But, frankly, I'd like to
see a million people do it,
so we know what's statistically valid.
I came from a sample of one.
Say what's valid, it's still nice to know
what happens to be between age 60 and 70,
and I wish I started it when I was 30,
and my son's doing it now.
He's in his 20s.
We start to measure.
But, measuring all that
will change medical practice detecting,
proactively and dramatically.
And these numbers will tell a story way
before symptoms appear
or things get worse.
And I'm very, very optimistic
of the following fact.
And most people have a
hard time believing it,
most people say every time
you have great technology,
it increases medical costs,
and so, constant argument.
And, yes, with proton beam
accelerators, that does happen.
But, if we, say, decide that every person
should measure 300 variables every week.
I'm almost certain the cost of that test
will get to 300 dollars a year.
Why?
Because you couldn't do that
with a venous blood draw.
You suddenly have to do
it off a fingerprint.
It suddenly starts to look
like a diabetic finger prick test.
And I know Theranos is being discredited,
we have nothing to do with Theranos.
Those technologies would
show up if that was practice,
they haven't shown up
because they aren't practice.
So, Metabolon test is a mass spec,
and it's, I think, five, 700 dollars
to get my quarterly blood test,
which, to me, is totally
worth it, given everything.
But measuring is important,
and I think measuring more
will change practice, which
will then change the cost
of providing that service.
There's no question that
blood draw and blood transport
cost more than any of
these tests themselves.
- Yeah, I mean, some of you in the crowd
probably know Mike Snyder at Stanford,
the chief, the chair of genetics,
who wrote a very well-cited paper now
where he measured everything
on himself for several years,
and detected pre-diabetes.
And you can always tell Mike on campus
because he's got sensors all over him
(audience laughs)
- So, I'm patient number
two in his genomic study.
My daughter's patient number three.
(laughs)
So, I subscribe to all data gathering.
(laughs)
- It's only 900,000 more to
go and we'll have our million.
There was another question. Yeah.
- Oh, thanks, Mike McConnell.
So, a lot of this brings
up one of the criticisms
of technology and healthcare,
which is that it's
increasing disparities in care, right?
We have the significant
portion of the population here
who aren't even getting
sometimes the most basic care,
and we're talking about
very advanced care.
On the other hand, you
could argue that, you know,
cell phones and mobile
phones have been disseminated
throughout the world in a
fairly rapid time frame.
How do we try to make sure that,
when we're talking about all
this advanced technology,
that we're trying to keep
in mind how to scale it
so that we could really distribute it
to our US or world population,
and not increase health
disparities in the process?
- So, I'll actually start that.
I think you're right to worry about it,
but the answer may be counter-intuitive.
So, we think about the fact
that technology innovation
will then benefit the wealthy
and leave the others behind,
but, in almost every way,
as technology comes into a society,
it tends to flatten out
the differences that exist in the world,
and give many people who didn't
have chances better chances.
Think about even cell
phones is one example,
where basically become
ubiquitous, and, in part,
have allowed a lot more technology,
and applications, and
even access to internet
and services that were
never possible in the past.
Go to Africa, where they can
do banking on cell phones,
which would never have happened.
But now, you take it to
the world of healthcare,
and we'll use, the blood
pressure example is just one.
If you had, you know, if you're wealthy,
and you can take days off your work
and go in to see your private doctor
and have him monitor your blood pressure,
and you can come in as
many times as you want
because you're one of his
VIP patients, life is good.
Now, you work in a job where
if you miss your hours a day,
you miss your pay for the day.
And now how much encouragement do you have
to get your blood pressure monitored
where you go and you sit in
my office for an hour or two
to get one measurement of blood pressure
so I can tell you to change your medicine,
which I should've been able
to monitor from your own home
and call that medicine in.
So, you could imagine in a new world,
monitoring blood pressure
would be a lot easier
for the disparate population
and probably less attractive to the rich,
who will still get their
monitor by Dr. Harrington
in his office.
(audience laughs)
- [Mike] I had another question.
- I'd add one or two things to that.
I jokingly say I hope in 15 or 20 years
I get better medical care
at a village in India
than I get at Stanford
because Stanford will still
have experts and humans.
(laughter)
It is counter-intuitive, but,
in fact, it's very likely.
Now, you have to get a little bit nuanced.
If I'm talking about diagnosis
or prescription or
monitoring, almost certainly,
the best care will be
delivered by machines.
And I'm much more likely
to get better care
when there's no humans with
their biases in the loop.
Dr. Ioannidis at Stanford,
one of my favorite researchers
who talks about all the biases in medicine
and the sources of error in medicine.
Now, if you're talking
about procedural medicine.
So, you're not likely to get
better procedural medicine
at a village in India.
I still want to go to Stanford for that.
So, it is nuanced, and will
vary by specialty and category,
but it will also depend on
how much we let machines
take over and have humans
serve the right role.
Already, it's clear, even
at the very early days
of driver-less cars,
Tesla's having fewer
accidents per mile driven
than human drivers.
If we're not comfortable
letting that take over,
then we're probably not even ready for it.
But people speculate that
within some period of time,
like 15 years, humans may
be banned from driving.
The question is will doctors
be banned from diagnosing?
(laughs)
- Good quote for the day.
- Interesting way to end.
- He's gonna tweet that one
out, I know it's coming.
Last question.
- It's Taha Jangda with Redox
and integration engine for EHRs.
I had a couple questions,
and you actually transitioned
into it perfectly,
about clinical decision support platforms.
You know, in the beginning, a lot of it,
the whole time, I was
thinking it was simple,
it's a CDS, CDS.
Outside of, you know, Epic's CDS platform,
where are we in terms of adoption
of clinical decision support platforms?
And then, my follow on that is, you know,
as you're training residents
or fellows, you know,
a lot of times in medical
school, I won't say where,
I was always instructed, oh,
CDSs aren't really
gonna be that beneficial
compared to you yourself.
And then my second question was,
you also transitioned into it,
was, as far as patients owning data.
So, for a lot of people,
when they come from outside of healthcare,
it makes logical sense
to think that a patient
should own their own data
simply because they're consumers,
but as consumers of healthcare services,
where are we in terms of
patients owning their own data
and being able to research it,
and able to facilitate better practices?
Sorry for the long question.
- So, I think clinical data support is,
it was in its infancy years
ago, and it wasn't very good,
it was a little clunky, it
was that earlier generation
that Vinod and Eric were talking about,
but it's getting so much better.
And there are systems,
Intermountain is a great example,
where Brent James talks about this notion
of 85% of what you do the
machine can take care of.
We want to save you for the other 15%
and for doing procedures, et cetera.
So, there are systems, there
are examples of doing it.
It's an institutional bias,
I mean, there are people,
I have people on my faculty who say,
"Oh, I don't need guidelines,
I know what to do."
I don't wanna go to that doctor.
I wanna go to the doctor who says,
"I'm gonna follow the guidelines,
and why wouldn't the machine
just help me do that,
and let me spend time with the patient
figuring out, well, why don't
you take your medicines,
what's going on in your life, et cetera."
That's the human element piece of it.
In terms of the training
piece, I think that the
trainees today are a
lot more sophisticated,
they're a lot more quantitatively inclined
than they were in our generation,
and I think they are adopting
these a lot more readily.
- [Taha] Do you feel Watson's
a good example of that CDS tool?
- I've not... Watson's
used at Duke, isn't it?
- No.
- It's not been used at our place.
I don't know enough about it
as a technology to say that,
other than watching it on Jeopardy.
(laughs)
- Let me speak to this.
I've spent a fair amount of time looking
at clinical decision support systems.
Most of them in the past were fairly poor.
Take one of the best known
they explain out of MGH, right?
It is about 30,000
hand-crafted rules today
collected over 20 years of practice.
There's a problem with that.
When you enter rules over 20 years,
and there's 30,000 rules,
many of them are
conflicting with each other.
Two, when a new paper comes
out, new piece of research,
you're waiting for somebody
to turn it into a rule,
and almost certainly, when
they turn it into a rule,
they're not gonna check
against the existing 30,000
to see where the conflicts are.
A great example that
I've covered elsewhere
is beta-blockers and perfusion therapy.
Two things that were
independently verified,
but together, they're a disaster.
Now, I just sent an email to Dr. Boyd,
and he was surprised.
For non-cardiac surgery, if you're taking,
if you had perfusion therapy,
then beta-blockers before other surgery
increase your risk of mortality 37%.
But these are conflicting
rules that came up
in different eras because the technology
came in different eras.
So, things like MGH's
system are really poor.
Watson's a slightly more
sophisticated rule system,
and it's probably equally fragile.
About a week and half ago,
a detailed conversation
on Watson oncology with the folks
at MS Memorial School in Kataran.
It doesn't work for the following reason:
as new things come up, these
rules aren't automatic,
and they aren't checked
against everything else,
and only a completely machine-run system
can do this well.
And that doesn't exist
today, as far as I know.
What some of the recent systems have done,
we have a company called
Lumiata that's doing this,
is essentially statistical
rule generation.
And that's better, and they
started the following way,
and this is sort of interesting.
They started by taking all the literature,
analyzing it, making
rules out of literature.
Then, they took tens of
thousands of physician hours
to curate the rules by hand,
and they found that
was a complete disaster
because it introduced
kinds of contradictions
that you didn't want in your rule set.
The thing is, they used that, then,
to get access to medical records,
made a clinical decision system
that I was talking about,
they're introducing through the payer,
through the insurer, or whoever's at risk.
Those systems are better,
but the real system
that uses the kind of
intuition I was talking about
in AlphaGo, and can compute
logic on these factual studies,
that's yet to come.
It's sort of one of my dreams,
we'll see it in the next five to 10 years,
it's doable today, nobody's doing it well,
mostly because the people who can do it
don't have access to all the data.
- So, I'd like to thank you both.
I spent the other part of my,
when I wasn't studying for the boards,
the second part of my time
was actually helping my son
with his medical school applications.
And I had this great
debate about, on the plane,
whether or not my son
should actually be pursuing
this career.
After this session,
it's become clear to me
that his world will be as exciting,
or perhaps even more exciting,
than the medicine that Bob and I practice.
We've had the opportunity to
see where innovation is going,
and the vision of a future empowered,
and not threatened, but more empowered,
where both patients and
individuals, as well as clinicians
and health systems, will use
data to drive better health.
This is a remarkable
kick-off for this conference.
It's not the end, but the
beginning of a very exciting day.
Thank you very much.
(applause)
