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PETER SZOLOVITS:
As David said, I've

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been at this for a long time.

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I am not a medical doctor.

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But I've probably
learned enough medicine

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to be able to play one on
television over the years.

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And actually, that's
relevant to today's lecture

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because today's
lecture is really

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trying to set the scene
for you to say, well,

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what are the kinds of
problems that doctors

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are interested in by looking
at what is it that they do.

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OK.

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So that's our goal for today.

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So what we're going
to do today is

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to talk about, in a
very general way, what

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are the goals of health care.

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So how many of you are doctors?

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A couple.

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Great.

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OK.

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So fix me when I blow it.

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All right.

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Please feel free to interrupt.

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So that's going to
be my first task.

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And then the second
one is going to be

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what are the things that
people actually do in order

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to try to achieve these goals.

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What is the practice
of medicine like?

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What is the process
that generates the data

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that we're going to be
using to learn from?

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And then I can't resist
talking a little bit

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about paying for health care
at the end of the lecture

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because a lot of the
problems that come up

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and a lot of the
interest that people show

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in doing the kind of analysis
we're talking about in fact

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is motivated by money.

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They want to be able to
save money, or spend less

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money, or something like that.

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So it's important to know that.

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OK.

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Medicine's been around
for a long time.

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I think from probably the
earliest of recorded history,

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there are discussions
of people wondering

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what the cause of disease
is, how to cure it.

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They came up with some
fairly cockamamie theories

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because they didn't have a
lot of scientific, modern

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approaches to it.

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But for example, this
is a photo on the left

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of a shaman I think from a
Canadian Indian tribe who's

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working on the boy
lying there who's sick.

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And this shaman would use
his knowledge of experience

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that he'd had with
other patients.

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They did know a lot
about medicinal plants.

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They knew something about how
to care for injuries and things

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like that.

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And so this was an effective
form of health care.

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Not much record keeping.

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You don't see electronic medical
records system in that scene.

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On the right is a modern
shaman practicing at one

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of the New York area hospitals.

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And so there are
traditional cultures

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in which that sort of hands
on interaction with the healer

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is considered a
very important part

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of the practice of medicine.

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And if you listen to futurist
doctors talking about what

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medicine is likely to be
like, they emphasize the fact

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that the role of a
healer is not just

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to be a good automaton who
figures out the right things

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but is to persuade
a patient to trust

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them to do the things that
he or she is suggesting

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to the patient.

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And there are a lot
of placebo effects

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that we know from lots
and lots of experiments

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that say that if you think
you're going to get better,

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you are going to get
better, on average.

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No guarantees.

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OK.

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Now modern medicine actually
looks more like this.

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So this is an
intensive care unit.

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And what you see
is a patient who

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has got all kinds of
electrical leads, and tubes,

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and things going into
them and is surrounded

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by tons and tons
of equipment which

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is monitoring him and
perhaps keeping him alive.

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And so this is the
high tech medicine

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that we think of as the
contemporary version

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of clinical care.

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Well, you might say, OK, what
does it mean to be healthy?

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Right.

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If the goal of medicine
is to make people or keep

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people healthy, what is health?

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So we turn to the World
Health Organization.

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And they have this lovely,
very comprehensive notion

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of the definition of health.

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A state of complete physical,
mental, and social well-being,

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not merely the absence
of disease or infirmity.

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And then they categorize.

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This and they say, well,
there's physical health,

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there's mental health,
and there's social health.

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Social health is
especially hard to measure.

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And I'll come back to
that in a little while.

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So what's easiest to measure
is how long people live.

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And so we've had data
on survival analysis

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for a long time.

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And this is kind of shocking.

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If you look here, this lower
curve is from around 1800.

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And what it shows you is that if
you lived in India around 1800,

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your life expectancy
was about 25 years.

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It's not very good.

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And if you lived in the richest
countries, which in those days

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were typically
European, in Belgium,

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your life expectancy was
way up there at 40 years.

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Right.

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How many of you knew that?

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OK.

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Good.

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I didn't until I
started looking at this.

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Now by 1950, which is
not that long ago, it

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was like 69 years ago, in
Norway your expectation

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was that you'd live
into your early 70s,

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in the US that you would live
into your late 60s on average.

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There was still a
huge cliff where,

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if you lived in Bhutan,
or Somalia, or something,

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you were still down around 30.

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Today, well in 2012,
we're doing a lot better.

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And the thing that's
striking is not only

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that the people who were
doing well have gotten better

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but that a lot of the people
who were doing very poorly

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have also gotten better.

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And so we're now up--

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India, remember, was at 25
years of life expectancy

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and now it's in the high 60s.

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Now of course,
these are averages.

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And so individuals vary a lot.

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But it's kind of interesting.

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So if you look at
the numbers, you

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see that even on a shorter
term, there are big changes.

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So for example, if
you're a male living

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in Rwanda, which is among the
worst places in terms of life

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expectancy, your
life expectancy,

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if you were born today,
is about 62 and 1/2 years.

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Right.

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If you were born in 2001,
it was only 38 years.

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Now what was going
on in Rwanda in 2001?

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Genocide.

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Yeah.

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They were killing each other.

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So that's sort of an
exceptional situation.

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And that's gotten much
better because they've

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stopped killing each
other as genocidal attacks

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between the Hutu and Tutsi,
I think, if I remember right.

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What about South Africa?

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What was going on in
South Africa in 2001.

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I'm not sure I heard you.

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AUDIENCE: Failure to
address the HIV crisis.

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PETER SZOLOVITS: Yes.

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The government at
the time was claiming

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that HIV was not
the cause of AIDS

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and therefore there was no point
in controlling HIV infections

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because AIDS was caused
by something else.

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Pretty crazy.

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So that was terrible.

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And they've gotten
much better at it.

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So that's what you tend to see
in a lot of African countries.

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And what you also see is
that there has really been

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improvement everywhere.

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So in the US, we went
from males expected

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to live 74 years to almost 78
years, so about a four year

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increase in life expectancy
over a period of just 17 years.

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You women, by the way, are going
to outlive us men, on average.

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There's some biological thing
that seems to work that way.

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OK.

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So a typical way
that people look

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at the survival
of a population is

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to say, well, given
a cohort of people

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born at some instant zero,
what fraction of them

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are still alive after a
certain period of time?

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And what you see
is that, of course,

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2031 we haven't reached yet.

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And so these are
projections based

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on sort of theoretical
extrapolations of actual data.

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But the older data is real.

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And what you see is that
from 1851 to, you know,

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2011 let's say, these
numbers have gone way up.

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Now where have they
gone up the most?

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Well, it used to be that
childhood mortality was

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enormous.

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And so if you look at 1851, by
age 10 about 30% of children

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had died.

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And so we've gotten a lot
better at stopping that

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from happening.

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People also look at
curves like this.

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So this is a distribution
of death rates by age.

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This happens to be for
Japan a few years ago.

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And again, females
do better than males.

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The gold curve in the middle
is the average of the two.

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And this is very typical
of almost any country

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that you look at.

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The shape of this curve
is pretty universal.

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So what does this say?

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It says that when you are born,
there is a relatively high risk

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that you're going to die.

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So these are kids who have
congenital abnormalities,

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have prenatal problems, have
all kinds of difficulties.

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And they don't make it.

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So there's a fairly high
death rate at birth.

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But once you make it to, I
think, about two years old,

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the death rate is down to
about one in 10,000 per year.

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Right.

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And then it stays quite low
until you become a teenager.

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Now why might the death rate go
up when you become a teenager?

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Well, suicide is the
extreme example of that.

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But teenagers tend
to be risk seeking

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rather than risk averse.

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You know, they
start driving cars.

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They go skiing,
skydiving, whatever

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it is that they're doing.

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And they start dying.

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But then if you
make it to about 20,

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then there is a relatively
flat region where by then

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you've developed enough sense
to know what risks are worth

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taking and which ones aren't.

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And so it's relatively flat
until about age 35 or 40

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at which point it starts
inexorably rising.

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And of course, as you
get older and older,

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the probability that you're
going to die in the next year

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becomes higher and higher.

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Right.

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This is uncomfortable
for somebody

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with my amount of gray hair.

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Now there is a peculiarity
in Japan which people

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puzzled over for a while.

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And that is that weird
dip up at age 106.

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So first of all, that's a
very small number of people

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that that represents.

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And it turned out
that it was fraud.

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So there were
families who failed

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to report the death of
their ancient grandmother

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or great-grandmother
because they wanted

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to continue collecting
social security

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payments from the government.

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So that's an artifact.

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OK.

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Now this is a
serious problem which

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we're going to return to in
a more technical way later

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in the semester, which is
this problem of disparities.

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So if you look at, for
example, the difference

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between white and black
female life expectancy

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in the United States, you
see that everybody's life

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expectancy, as we've shown,
is going up gradually

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in this case from 1975 to 2015.

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But there continues to be a gap
between black and white females

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and between black
and white males

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where black patients
are more likely to die

254
00:14:05,910 --> 00:14:12,120
or less likely to survive longer
given the disparities that

255
00:14:12,120 --> 00:14:14,760
exist socioeconomically.

256
00:14:14,760 --> 00:14:16,560
Maybe medically.

257
00:14:16,560 --> 00:14:18,570
We don't know exactly.

258
00:14:18,570 --> 00:14:21,000
And then if you
look at Hispanics,

259
00:14:21,000 --> 00:14:23,610
however, they do pretty well.

260
00:14:23,610 --> 00:14:26,940
So in 2015, you're
actually a little bit

261
00:14:26,940 --> 00:14:30,000
better off to be Hispanic,
either male or female,

262
00:14:30,000 --> 00:14:32,640
than you are to be
either white or black.

263
00:14:32,640 --> 00:14:36,800
But it's still worse to be black
than to be white or Hispanic.

264
00:14:36,800 --> 00:14:37,500
Right.

265
00:14:37,500 --> 00:14:40,680
So these are the
kinds of facts that

266
00:14:40,680 --> 00:14:46,150
drive some of the issues in
what we do in medical care.

267
00:14:46,150 --> 00:14:48,180
Now what do people die of?

268
00:14:48,180 --> 00:14:52,020
Well, about a quarter of
them die of heart disease.

269
00:14:52,020 --> 00:14:54,510
And a little over a fifth
of them die of cancer.

270
00:14:54,510 --> 00:14:59,100
This is USA data from 2014 so
it's not completely up to date

271
00:14:59,100 --> 00:15:01,920
but hasn't changed that much.

272
00:15:01,920 --> 00:15:04,950
And then there's a
decreasing number

273
00:15:04,950 --> 00:15:08,290
of deaths from
various other causes.

274
00:15:08,290 --> 00:15:10,110
So heart disease,
cancer, or chronic

275
00:15:10,110 --> 00:15:11,790
lower respiratory disease.

276
00:15:11,790 --> 00:15:14,640
So this is like
COPD that's caused

277
00:15:14,640 --> 00:15:18,150
by smoking, stuff like that.

278
00:15:18,150 --> 00:15:22,110
Accidents account for
about 5% of deaths.

279
00:15:22,110 --> 00:15:27,240
Stroke, cerebral vascular
events, Alzheimer's disease,

280
00:15:27,240 --> 00:15:31,740
diabetes, influenza, pneumonia,
kidney disease, suicide,

281
00:15:31,740 --> 00:15:34,920
and then everything else
is about another quarter.

282
00:15:34,920 --> 00:15:36,340
OK.

283
00:15:36,340 --> 00:15:38,440
Now take a look at those.

284
00:15:38,440 --> 00:15:42,035
What kind of diseases
are these, the biggies?

285
00:15:47,400 --> 00:15:47,900
Well.

286
00:15:47,900 --> 00:15:48,830
They're chronic.

287
00:15:48,830 --> 00:15:51,950
Most of them are chronic.

288
00:15:51,950 --> 00:15:54,910
They're also not infectious.

289
00:15:54,910 --> 00:15:58,010
But except for
influenza and pneumonia,

290
00:15:58,010 --> 00:16:02,310
nothing else there is
infectious as far as we know.

291
00:16:02,310 --> 00:16:05,840
I mean, yeah.

292
00:16:05,840 --> 00:16:10,460
That asterisk you should
put after every statement

293
00:16:10,460 --> 00:16:13,040
about current medicine.

294
00:16:13,040 --> 00:16:16,820
So that's interesting, because
if you wrote the same table

295
00:16:16,820 --> 00:16:20,730
back in 1850, you would
find that a lot of people

296
00:16:20,730 --> 00:16:22,860
were dying of infections.

297
00:16:22,860 --> 00:16:24,810
And they weren't
typically living

298
00:16:24,810 --> 00:16:28,170
long enough to develop these
lovely chronic diseases

299
00:16:28,170 --> 00:16:30,540
of the aging.

300
00:16:30,540 --> 00:16:35,040
So there have been
big changes there.

301
00:16:35,040 --> 00:16:37,860
Now the other thing
that's worth looking at

302
00:16:37,860 --> 00:16:42,120
is, in addition to the
reasons that people die,

303
00:16:42,120 --> 00:16:43,860
they start getting sicker.

304
00:16:43,860 --> 00:16:48,780
And they are getting sort
of less value out of life

305
00:16:48,780 --> 00:16:52,670
because they're developing
all these other conditions.

306
00:16:52,670 --> 00:16:58,310
So if you look at people
over 65, about half of them

307
00:16:58,310 --> 00:17:02,970
have some form of arthritis.

308
00:17:02,970 --> 00:17:05,220
And about 40%-- yeah.

309
00:17:05,220 --> 00:17:09,329
About 40% have hypertension.

310
00:17:09,329 --> 00:17:12,750
By the way, if you
have trouble with any

311
00:17:12,750 --> 00:17:15,839
of the medicalese
words, just interrupt.

312
00:17:15,839 --> 00:17:18,569
Hypertension is
high blood pressure.

313
00:17:18,569 --> 00:17:21,240
Hearing impairment.

314
00:17:21,240 --> 00:17:25,079
Me, I'm wearing a
hearing aid on one side

315
00:17:25,079 --> 00:17:28,470
because my ears are going bad.

316
00:17:28,470 --> 00:17:30,640
Heart disease, about a quarter.

317
00:17:30,640 --> 00:17:33,840
Orthostatic impairment--
that means people who wobble

318
00:17:33,840 --> 00:17:37,440
because their sense of
balance is not so good--

319
00:17:37,440 --> 00:17:38,580
16%.

320
00:17:38,580 --> 00:17:43,530
Cataracts, chronic
sinusitis, visual impairment,

321
00:17:43,530 --> 00:17:48,430
genitourinary problems,
diabetes, et cetera.

322
00:17:48,430 --> 00:17:50,500
So these are all growing.

323
00:17:50,500 --> 00:17:53,400
Here's the list of the next 10.

324
00:17:53,400 --> 00:17:58,890
And varicose veins, hernia,
hemorrhoids, psoriasis,

325
00:17:58,890 --> 00:18:02,430
hardening of the arteries,
tinnitus, corns, calluses,

326
00:18:02,430 --> 00:18:06,450
constipation, hay fever, and
cerebral vascular problems.

327
00:18:06,450 --> 00:18:07,410
All right.

328
00:18:07,410 --> 00:18:13,500
So people develop these by
the time they're over 65.

329
00:18:13,500 --> 00:18:16,230
So one question we
might ask is, well,

330
00:18:16,230 --> 00:18:18,670
what is the quality of life?

331
00:18:18,670 --> 00:18:22,740
So for example, a
lot of the doctors

332
00:18:22,740 --> 00:18:26,280
that I started working
with in the 1970s

333
00:18:26,280 --> 00:18:29,580
were great advocates
of the application

334
00:18:29,580 --> 00:18:32,280
of decision analysis
decision theory

335
00:18:32,280 --> 00:18:34,870
to making medical decisions.

336
00:18:34,870 --> 00:18:39,240
And so the problem is how
do you evaluate an outcome?

337
00:18:39,240 --> 00:18:42,810
And they said, well, the way
we evaluate an outcome is

338
00:18:42,810 --> 00:18:45,330
we look at your longevity.

339
00:18:45,330 --> 00:18:49,350
Obviously, the longer you
live, the better typically.

340
00:18:49,350 --> 00:18:53,400
But we also look at your quality
of life during that time.

341
00:18:53,400 --> 00:18:58,710
And we say that if you're
confined to a wheelchair,

342
00:18:58,710 --> 00:19:01,320
let's say, your quality
of life might not

343
00:19:01,320 --> 00:19:05,520
be as good as if you
were able to run around,

344
00:19:05,520 --> 00:19:08,370
or if you're suffering
from chronic pain,

345
00:19:08,370 --> 00:19:10,890
your quality of life
might not be as good as

346
00:19:10,890 --> 00:19:13,930
if you were pain free.

347
00:19:13,930 --> 00:19:16,750
And so we came up with
this model that says,

348
00:19:16,750 --> 00:19:23,730
well, the value of your life
is essentially an integral

349
00:19:23,730 --> 00:19:26,760
from time zero to
however long you're

350
00:19:26,760 --> 00:19:30,510
going to live of
a function Q that

351
00:19:30,510 --> 00:19:34,920
says this is a measure of
how good your quality of life

352
00:19:34,920 --> 00:19:39,420
is at that particular point
in time and then some discount

353
00:19:39,420 --> 00:19:40,600
factor.

354
00:19:40,600 --> 00:19:41,100
Right.

355
00:19:41,100 --> 00:19:43,770
So what's the role of
the discount factor?

356
00:19:43,770 --> 00:19:45,810
Well, it's just
like in economics.

357
00:19:45,810 --> 00:19:50,790
If I offer you some
horribly painful thing

358
00:19:50,790 --> 00:19:56,400
today versus 10 years from now,
which are you going to choose?

359
00:19:56,400 --> 00:20:00,340
Most of us will say later.

360
00:20:00,340 --> 00:20:02,940
So that's what the
discount factor does.

361
00:20:02,940 --> 00:20:06,780
Now who knows what the
right discount rate is?

362
00:20:06,780 --> 00:20:09,600
So in some of their work,
they were doing crazy things

363
00:20:09,600 --> 00:20:14,340
like taking the financial
discount factors about bank

364
00:20:14,340 --> 00:20:16,470
interest rates and
things like that

365
00:20:16,470 --> 00:20:18,810
and applying them to
these health things

366
00:20:18,810 --> 00:20:21,690
just because they didn't have
any better numbers to do.

367
00:20:21,690 --> 00:20:23,370
That seems a little suspicious.

368
00:20:23,370 --> 00:20:25,440
But nevertheless,
methodologically, it's

369
00:20:25,440 --> 00:20:27,490
a way of doing it.

370
00:20:27,490 --> 00:20:28,200
OK.

371
00:20:28,200 --> 00:20:30,930
So how do you measure
the quality of life?

372
00:20:30,930 --> 00:20:35,680
Well, there is this notion of
the activities of daily living.

373
00:20:35,680 --> 00:20:38,000
So can you bathe and shower?

374
00:20:38,000 --> 00:20:41,940
Can you brush your teeth
and groom your hair?

375
00:20:41,940 --> 00:20:42,900
Can you get dressed?

376
00:20:42,900 --> 00:20:46,170
Can you go to the toilet,
clean yourself up?

377
00:20:46,170 --> 00:20:49,260
Are you able to walk,
get in and out of bed,

378
00:20:49,260 --> 00:20:51,300
get in and out of a chair?

379
00:20:51,300 --> 00:20:53,530
Can you feed yourself?

380
00:20:53,530 --> 00:20:56,850
And then there are a bunch of
instrumental factors, things

381
00:20:56,850 --> 00:20:59,700
like are you able
to clean your house

382
00:20:59,700 --> 00:21:02,890
and can you manage
your money and so on.

383
00:21:02,890 --> 00:21:06,150
So these are typically
for older people.

384
00:21:06,150 --> 00:21:10,440
But they are ways of trying to
quantify that quality of life

385
00:21:10,440 --> 00:21:15,070
by saying how many of these
things are you able to do.

386
00:21:15,070 --> 00:21:17,040
And there are a lot of
federal regulations,

387
00:21:17,040 --> 00:21:22,180
for example, that take advantage
of quantification like this.

388
00:21:22,180 --> 00:21:26,760
So if you're asking to be put
on some sort of disability

389
00:21:26,760 --> 00:21:30,900
where the government sends
you a check to keep you alive,

390
00:21:30,900 --> 00:21:33,390
you have to demonstrate
that you are

391
00:21:33,390 --> 00:21:35,550
at a certain point
on the scale that's

392
00:21:35,550 --> 00:21:38,640
derived from these
capabilities in order

393
00:21:38,640 --> 00:21:41,860
to justifiably get that.

394
00:21:41,860 --> 00:21:45,220
So occupational therapy
is one of the things

395
00:21:45,220 --> 00:21:49,300
that people try to
teach the elderly.

396
00:21:49,300 --> 00:21:54,250
My parents died in their
late 80s and my dad was 90.

397
00:21:54,250 --> 00:21:59,270
And I remember when he would
have some medical problem,

398
00:21:59,270 --> 00:22:01,360
then he would be put
into the clutches

399
00:22:01,360 --> 00:22:05,230
of an occupational therapist
who would try to make sure

400
00:22:05,230 --> 00:22:13,240
that he was able to communicate,
and get around, and not

401
00:22:13,240 --> 00:22:17,620
fall for tricks where people
wanted to get him to send all

402
00:22:17,620 --> 00:22:21,790
his money to them,
or meal preparation

403
00:22:21,790 --> 00:22:22,940
and stuff like that.

404
00:22:22,940 --> 00:22:24,580
So these are sort of the--

405
00:22:27,090 --> 00:22:29,160
occupational is a
funny term for it,

406
00:22:29,160 --> 00:22:31,710
because this typically
applies to people

407
00:22:31,710 --> 00:22:35,190
who are retired so it's
not really an occupation.

408
00:22:35,190 --> 00:22:36,870
But it's the sort
of things that you

409
00:22:36,870 --> 00:22:40,960
need to do in order to be
able to have a decent life.

410
00:22:40,960 --> 00:22:44,450
Well, now there's an
interesting valuation question.

411
00:22:44,450 --> 00:22:48,750
So if you look at
the top right model,

412
00:22:48,750 --> 00:22:53,160
we actually don't have very
good data on anything other

413
00:22:53,160 --> 00:22:54,240
than mortality.

414
00:22:54,240 --> 00:22:56,490
So mortality is who dies.

415
00:22:56,490 --> 00:22:59,850
So the blue curve
there is the curve

416
00:22:59,850 --> 00:23:02,910
that you've seen before,
which is, of a cohort,

417
00:23:02,910 --> 00:23:05,550
how many people are still
alive after a certain number

418
00:23:05,550 --> 00:23:07,360
of years?

419
00:23:07,360 --> 00:23:11,730
The red curve is
a morbidity curve

420
00:23:11,730 --> 00:23:17,700
which says how many of those
people are still alive and have

421
00:23:17,700 --> 00:23:22,830
no sort of problematic
chronic diseases.

422
00:23:22,830 --> 00:23:24,600
So they're not in constant pain.

423
00:23:24,600 --> 00:23:27,090
And they're not immobilized.

424
00:23:27,090 --> 00:23:31,710
And they're not unable to
do the things that I just

425
00:23:31,710 --> 00:23:35,520
listed on the previous slides.

426
00:23:35,520 --> 00:23:39,870
And then disability
is when you really

427
00:23:39,870 --> 00:23:43,500
become incapable of
taking care of yourself.

428
00:23:43,500 --> 00:23:47,160
And it typically involves
moving into an assisted living

429
00:23:47,160 --> 00:23:50,520
facility, or a nursing home,
or something like that, which

430
00:23:50,520 --> 00:23:53,760
is kind of a nightmare
for many people

431
00:23:53,760 --> 00:23:56,770
and also very
expensive for society.

432
00:23:56,770 --> 00:24:00,270
So as I said, the
blue curve there

433
00:24:00,270 --> 00:24:05,940
is based on actual data for
American females in 1980.

434
00:24:05,940 --> 00:24:09,360
The red curve is a
hypothetical curve

435
00:24:09,360 --> 00:24:14,760
where I just assumed that the
rate of developing a morbidity

436
00:24:14,760 --> 00:24:18,220
is about twice
the rate of dying.

437
00:24:18,220 --> 00:24:22,060
And the green curve I
assumed that the rate

438
00:24:22,060 --> 00:24:27,040
of developing a disability is--

439
00:24:27,040 --> 00:24:30,430
I can't remember-- I think
three times as high as the rate

440
00:24:30,430 --> 00:24:32,180
of dying, something like that.

441
00:24:32,180 --> 00:24:35,290
So that's why those
curves are lower.

442
00:24:35,290 --> 00:24:37,210
And they look
approximately right.

443
00:24:37,210 --> 00:24:40,270
But we don't have
good data on those.

444
00:24:40,270 --> 00:24:43,360
Now the question
that you have to ask

445
00:24:43,360 --> 00:24:46,060
is, how do you want
to change this?

446
00:24:46,060 --> 00:24:50,980
So for example, suppose that
we kept the same situation.

447
00:24:50,980 --> 00:24:56,280
We reduced mortality to
20% of its actual rate.

448
00:24:56,280 --> 00:25:00,810
But we kept the disability
and morbidity rates the same

449
00:25:00,810 --> 00:25:03,460
as they are on the top left.

450
00:25:03,460 --> 00:25:04,690
So what would that do?

451
00:25:04,690 --> 00:25:07,980
Well, what that would do
is create a huge number

452
00:25:07,980 --> 00:25:18,000
of people who are
disabled because they're

453
00:25:18,000 --> 00:25:21,000
going to live longer
beyond the point

454
00:25:21,000 --> 00:25:24,620
where they're able
to function fully.

455
00:25:24,620 --> 00:25:26,180
So this is-- yeah.

456
00:25:26,180 --> 00:25:28,450
AUDIENCE: Can I just ask,
why does green not just

457
00:25:28,450 --> 00:25:29,223
mean healthy?

458
00:25:29,223 --> 00:25:30,140
To me, it just seems--

459
00:25:30,140 --> 00:25:31,830
PETER SZOLOVITS: Green
just means healthy.

460
00:25:31,830 --> 00:25:32,372
AUDIENCE: OK.

461
00:25:32,372 --> 00:25:33,183
It does.

462
00:25:33,183 --> 00:25:34,100
PETER SZOLOVITS: Yeah.

463
00:25:34,100 --> 00:25:37,910
So beyond green is what I
meant is the morbidity curve.

464
00:25:37,910 --> 00:25:40,850
And beyond red is
the disability curve.

465
00:25:40,850 --> 00:25:41,870
OK.

466
00:25:41,870 --> 00:25:44,630
I may have misspoken.

467
00:25:44,630 --> 00:25:47,520
So green is healthy.

468
00:25:47,520 --> 00:25:52,160
Red means suffering
from some morbidity.

469
00:25:52,160 --> 00:25:55,680
And blue means disabled.

470
00:25:55,680 --> 00:26:00,000
So if we just extend life but
we don't make it any better,

471
00:26:00,000 --> 00:26:03,470
then that's not a very
attractive picture.

472
00:26:03,470 --> 00:26:06,710
So other possibilities are
compression of morbidity.

473
00:26:06,710 --> 00:26:15,080
So for example, if we reduce the
rate at which people get sick

474
00:26:15,080 --> 00:26:20,300
and die but we increase it--

475
00:26:20,300 --> 00:26:23,600
we decrease it initially
and then increase it

476
00:26:23,600 --> 00:26:27,260
so that, on average, people live
about the same length of time

477
00:26:27,260 --> 00:26:31,580
that they now do, then we're
going to have fewer people who

478
00:26:31,580 --> 00:26:33,830
are suffering from
morbidity or who

479
00:26:33,830 --> 00:26:41,490
are disabled because you last
doing well and then you die.

480
00:26:41,490 --> 00:26:44,960
So this is the
wonderful one horse shay

481
00:26:44,960 --> 00:26:50,360
where everything falls apart at
once, which, you know, frankly,

482
00:26:50,360 --> 00:26:52,790
as somebody who's a
little closer to the end

483
00:26:52,790 --> 00:26:56,640
than you guys are, I wouldn't
mind that kind of exit.

484
00:26:56,640 --> 00:26:57,830
Right.

485
00:26:57,830 --> 00:27:01,610
I don't want to be
disabled for 20 years.

486
00:27:01,610 --> 00:27:03,560
I'd rather live a
long time healthy

487
00:27:03,560 --> 00:27:05,710
and then drop dead
at some point.

488
00:27:05,710 --> 00:27:06,210
OK.

489
00:27:09,650 --> 00:27:12,440
My dad used to say
that he wanted to die

490
00:27:12,440 --> 00:27:15,080
by being hit by a meteor.

491
00:27:15,080 --> 00:27:15,800
Right.

492
00:27:15,800 --> 00:27:17,570
He wouldn't know it's coming.

493
00:27:17,570 --> 00:27:18,770
It's instant.

494
00:27:18,770 --> 00:27:20,420
No suffering, no pain.

495
00:27:20,420 --> 00:27:21,590
Perfect.

496
00:27:21,590 --> 00:27:25,760
He almost got it but not quite.

497
00:27:25,760 --> 00:27:26,620
OK.

498
00:27:26,620 --> 00:27:30,700
And then the final story
is lifespan extension,

499
00:27:30,700 --> 00:27:38,640
which is where we simply lower
the mortality rate and all

500
00:27:38,640 --> 00:27:40,870
the other rates in proportion.

501
00:27:40,870 --> 00:27:42,630
And what happens
is that you start

502
00:27:42,630 --> 00:27:48,780
having healthy 107-year-olds
and not so healthy

503
00:27:48,780 --> 00:27:53,580
120-year-olds in the
population in larger numbers

504
00:27:53,580 --> 00:27:55,790
than we do now.

505
00:27:55,790 --> 00:27:57,400
OK.

506
00:27:57,400 --> 00:27:59,200
Social quality of life.

507
00:27:59,200 --> 00:28:01,102
That's a tough one.

508
00:28:01,102 --> 00:28:02,320
All right.

509
00:28:02,320 --> 00:28:05,310
So here's a naive theory.

510
00:28:05,310 --> 00:28:08,235
We take the quality of life
of every person on the planet

511
00:28:08,235 --> 00:28:10,500
and we sum them up.

512
00:28:10,500 --> 00:28:13,240
And we say, OK, that's the
social quality of life.

513
00:28:16,050 --> 00:28:19,090
Is that a good idea?

514
00:28:19,090 --> 00:28:19,980
Probably not.

515
00:28:19,980 --> 00:28:20,670
Right.

516
00:28:20,670 --> 00:28:22,440
For one thing, it
would say that we

517
00:28:22,440 --> 00:28:26,730
ought to have as much population
explosion as possible,

518
00:28:26,730 --> 00:28:30,340
because then we'd have more
people to integrate over,

519
00:28:30,340 --> 00:28:34,140
which doesn't seem sensible.

520
00:28:34,140 --> 00:28:34,700
Right.

521
00:28:34,700 --> 00:28:39,060
Now obviously if we start
packing the world so that it's

522
00:28:39,060 --> 00:28:41,910
super crowded, then
the quality of life

523
00:28:41,910 --> 00:28:45,750
will go down eventually
enough that adding more people

524
00:28:45,750 --> 00:28:48,120
is probably not optimal.

525
00:28:48,120 --> 00:28:54,240
But nevertheless, that doesn't
seem like a real satisfying

526
00:28:54,240 --> 00:28:56,800
solution.

527
00:28:56,800 --> 00:28:58,370
How about less?

528
00:28:58,370 --> 00:29:00,840
It was popular
about 10 years ago

529
00:29:00,840 --> 00:29:03,900
for people to write these
sort of speculative books

530
00:29:03,900 --> 00:29:08,170
about how would the world
look if half the people died.

531
00:29:08,170 --> 00:29:09,690
Right.

532
00:29:09,690 --> 00:29:11,250
Other than the
trauma of the half

533
00:29:11,250 --> 00:29:14,430
the people dying,
you know, they were

534
00:29:14,430 --> 00:29:20,250
proposing that this would
be some wonderful sylvan

535
00:29:20,250 --> 00:29:25,440
sort of ideal old
fashioned kind of world.

536
00:29:25,440 --> 00:29:27,060
I didn't buy that.

537
00:29:27,060 --> 00:29:29,220
And of course, we don't
know of a good way

538
00:29:29,220 --> 00:29:32,160
to get there,
although it is true

539
00:29:32,160 --> 00:29:34,560
that in at least
developed countries,

540
00:29:34,560 --> 00:29:39,150
birth rates keep falling to
the point where populations are

541
00:29:39,150 --> 00:29:41,910
worried about underpopulation.

542
00:29:41,910 --> 00:29:46,020
The Japanese, for example,
have very strict immigration

543
00:29:46,020 --> 00:29:48,840
policies so you
can't become Japanese

544
00:29:48,840 --> 00:29:51,270
if you're not born Japanese.

545
00:29:51,270 --> 00:29:53,820
And Japanese people
aren't having enough kids

546
00:29:53,820 --> 00:29:55,630
to replace themselves.

547
00:29:55,630 --> 00:30:01,170
And so the natural population
of Japan is falling.

548
00:30:01,170 --> 00:30:05,010
Italy is in the same
quandary except that Italy

549
00:30:05,010 --> 00:30:08,640
has all these immigrants
coming in and trying

550
00:30:08,640 --> 00:30:09,900
to become Italians.

551
00:30:09,900 --> 00:30:13,920
And of course, this leads to big
political fights about who gets

552
00:30:13,920 --> 00:30:18,080
to be an Italian
and who doesn't.

553
00:30:18,080 --> 00:30:19,880
And then, of course,
there's the question

554
00:30:19,880 --> 00:30:23,390
of money, which as I say
we'll return to later.

555
00:30:23,390 --> 00:30:26,830
Now one other important
thing to consider

556
00:30:26,830 --> 00:30:32,860
is that because of the
increase in life expectancy,

557
00:30:32,860 --> 00:30:37,180
there has been a big change
in timescale in the way

558
00:30:37,180 --> 00:30:40,120
people think about medical care.

559
00:30:40,120 --> 00:30:46,060
So it used to be long,
long ago in the shaman era,

560
00:30:46,060 --> 00:30:49,600
you wouldn't go to a shaman
to say keep me healthy.

561
00:30:49,600 --> 00:30:53,170
You would go to a shaman saying,
you know, I broke my arm,

562
00:30:53,170 --> 00:30:58,400
or I have this pain in my
leg, or fix me somehow.

563
00:30:58,400 --> 00:31:03,140
And so things were focused
on the notion of cure.

564
00:31:03,140 --> 00:31:06,960
And that was applicable
to acute illnesses.

565
00:31:06,960 --> 00:31:09,750
But as we've gotten
better at treating

566
00:31:09,750 --> 00:31:11,610
acute illnesses--
which, by the way,

567
00:31:11,610 --> 00:31:13,950
didn't happen all that long ago.

568
00:31:13,950 --> 00:31:16,410
I mean antibiotics
were only invented

569
00:31:16,410 --> 00:31:18,480
in the early 20th century.

570
00:31:18,480 --> 00:31:22,050
And that made a huge
difference in stopping people

571
00:31:22,050 --> 00:31:24,780
from dying of infections.

572
00:31:24,780 --> 00:31:28,140
So then it became more a
matter of managing long term

573
00:31:28,140 --> 00:31:29,970
chronic illnesses.

574
00:31:29,970 --> 00:31:32,070
And that's pretty
much where we are now.

575
00:31:32,070 --> 00:31:36,360
The medical world at the
moment, most of the action

576
00:31:36,360 --> 00:31:39,210
is in trying to understand
things like diabetes,

577
00:31:39,210 --> 00:31:41,850
and heart disease,
and cancer, and things

578
00:31:41,850 --> 00:31:44,700
like that that develop
over a long time.

579
00:31:44,700 --> 00:31:46,500
And they don't
kill you instantly

580
00:31:46,500 --> 00:31:49,720
like infectious diseases did.

581
00:31:49,720 --> 00:31:53,370
But they produce a real burden.

582
00:31:53,370 --> 00:31:56,820
And then, of course, the next
step that everybody expects

583
00:31:56,820 --> 00:32:00,210
is, well, how do
we prevent disease?

584
00:32:00,210 --> 00:32:03,720
So how can we change
your exposures?

585
00:32:03,720 --> 00:32:05,590
How can we change
your motivation?

586
00:32:05,590 --> 00:32:06,930
How can we change your diet?

587
00:32:06,930 --> 00:32:10,650
How can we change whatever
it is that we need to change?

588
00:32:10,650 --> 00:32:13,650
How can we change your
genes to prevent you

589
00:32:13,650 --> 00:32:16,930
from developing these
diseases in the first place?

590
00:32:16,930 --> 00:32:18,435
And that's sort of the future.

591
00:32:19,500 --> 00:32:21,090
OK.

592
00:32:21,090 --> 00:32:25,440
So that's about what
medicine tries to do.

593
00:32:25,440 --> 00:32:26,820
But how does it do it?

594
00:32:26,820 --> 00:32:28,830
And so we're going
to talk a little bit

595
00:32:28,830 --> 00:32:32,940
about the traditional tasks that
are attributed to health care

596
00:32:32,940 --> 00:32:34,050
practice.

597
00:32:34,050 --> 00:32:38,320
So traditionally, people talk
about diagnosis, prognosis,

598
00:32:38,320 --> 00:32:39,660
and therapy.

599
00:32:39,660 --> 00:32:42,390
So diagnosis-- I
go to my doctor.

600
00:32:42,390 --> 00:32:45,210
I say, doc, I've got
this horrible headache.

601
00:32:45,210 --> 00:32:47,460
I've had it for two weeks.

602
00:32:47,460 --> 00:32:50,340
What's wrong with me?

603
00:32:50,340 --> 00:32:56,446
And his job-- in my case,
it happens to be a "he."

604
00:32:56,446 --> 00:33:00,545
His job is to come
up with an answer.

605
00:33:00,545 --> 00:33:01,420
What's wrong with me?

606
00:33:04,340 --> 00:33:08,330
Prognosis is he's
supposed to predict

607
00:33:08,330 --> 00:33:11,660
what's going to happen to
me, at least if he doesn't

608
00:33:11,660 --> 00:33:14,130
do anything.

609
00:33:14,130 --> 00:33:16,770
So is this headache
going to go away

610
00:33:16,770 --> 00:33:20,400
or is it going to turn out
to be a brain tumor that

611
00:33:20,400 --> 00:33:25,350
will kill me, or is it going
to be some amoeba that's living

612
00:33:25,350 --> 00:33:30,270
in my brain and eat my neurons?

613
00:33:30,270 --> 00:33:34,630
All kinds of horrible
things are possible.

614
00:33:34,630 --> 00:33:37,470
So it's the prospect of
recovery as anticipated

615
00:33:37,470 --> 00:33:42,450
from the usual course of disease
or peculiarities of the case.

616
00:33:42,450 --> 00:33:46,570
And then therapy, of course,
is what do you do about it.

617
00:33:46,570 --> 00:33:53,420
And prognosis is definitely
informed by diagnosis,

618
00:33:53,420 --> 00:33:56,570
because if you don't know
what's wrong with me,

619
00:33:56,570 --> 00:34:00,500
then it's much harder to predict
what's going to happen to me.

620
00:34:00,500 --> 00:34:03,650
And if you can't predict
what's going to happen to me,

621
00:34:03,650 --> 00:34:05,690
then it's much
harder to figure out

622
00:34:05,690 --> 00:34:08,449
what to do to prevent
that from happening

623
00:34:08,449 --> 00:34:11,670
or to encourage that
to happen, right?

624
00:34:11,670 --> 00:34:15,570
So this is kind of
a serial process.

625
00:34:15,570 --> 00:34:18,540
And the way I look
at it is that there's

626
00:34:18,540 --> 00:34:23,159
a kind of cyclic
process of care.

627
00:34:23,159 --> 00:34:27,360
And the process starts with
an initial presentation.

628
00:34:27,360 --> 00:34:29,520
So I show up at
my doctor's office

629
00:34:29,520 --> 00:34:31,949
and I complain about something.

630
00:34:31,949 --> 00:34:36,989
And if you ever listen
to a doctor interacting

631
00:34:36,989 --> 00:34:39,940
with a patient, the first
time the patient comes in,

632
00:34:39,940 --> 00:34:43,530
the first question is always,
what brought you here?

633
00:34:43,530 --> 00:34:44,340
Right?

634
00:34:44,340 --> 00:34:47,400
And that's called the
presenting complaint.

635
00:34:47,400 --> 00:34:51,659
So if I say, you
know, my ankle hurts

636
00:34:51,659 --> 00:34:55,170
like hell, that's very
different than if I say,

637
00:34:55,170 --> 00:34:58,350
I stopped being able to
hear in my right ear,

638
00:34:58,350 --> 00:35:02,410
or I have this horrible skin
rash on my arm, or whatever.

639
00:35:02,410 --> 00:35:05,990
And that's going to take me
in very different directions.

640
00:35:05,990 --> 00:35:10,030
So then what happens is
that the doctor examines you

641
00:35:10,030 --> 00:35:12,590
and generates a bunch of data.

642
00:35:12,590 --> 00:35:13,750
So these are measurements.

643
00:35:13,750 --> 00:35:16,810
And of course, it used to be
that those measurements were

644
00:35:16,810 --> 00:35:18,850
based on observation.

645
00:35:18,850 --> 00:35:22,480
So there are very famous
doctors from 100 years ago

646
00:35:22,480 --> 00:35:26,980
who were spectacularly good at
being able to look at a patient

647
00:35:26,980 --> 00:35:28,910
and figure out what's
wrong with them

648
00:35:28,910 --> 00:35:32,050
by being very astute observers--

649
00:35:32,050 --> 00:35:36,070
the Sherlock Holmes
kind of subtle,

650
00:35:36,070 --> 00:35:39,820
oh, I see that you have a cut
on the inside of your shoe,

651
00:35:39,820 --> 00:35:43,030
which means you must have
been going through brambles,

652
00:35:43,030 --> 00:35:44,620
and you know, whatever.

653
00:35:44,620 --> 00:35:48,040
I'm making up a Sherlock
Holmes story here.

654
00:35:48,040 --> 00:35:50,290
So that generates data.

655
00:35:50,290 --> 00:35:53,110
And then we interpret
that data to generate

656
00:35:53,110 --> 00:35:56,740
some kind of information
or interpreted data

657
00:35:56,740 --> 00:35:58,960
about the patient.

658
00:35:58,960 --> 00:36:02,810
And based on that, we
determine a diagnosis.

659
00:36:02,810 --> 00:36:05,020
Now, do we determine
a diagnosis?

660
00:36:05,020 --> 00:36:05,800
Maybe not.

661
00:36:05,800 --> 00:36:08,870
Maybe we guess a diagnosis.

662
00:36:08,870 --> 00:36:12,550
One of the things I learned
early on working in this field

663
00:36:12,550 --> 00:36:17,010
is that doctors are actually
quite willing to make guesses,

664
00:36:17,010 --> 00:36:20,580
because it's so useful to
believe that you understand

665
00:36:20,580 --> 00:36:22,530
what's going on.

666
00:36:22,530 --> 00:36:27,300
If you say, well, there's
some probability distribution

667
00:36:27,300 --> 00:36:30,720
over a vast number of
possible things, that

668
00:36:30,720 --> 00:36:34,470
doesn't give you very good
guidance on what to do next.

669
00:36:34,470 --> 00:36:36,660
Whereas if you can
say, oh, I think

670
00:36:36,660 --> 00:36:40,530
this patient is developing
type 2 diabetes,

671
00:36:40,530 --> 00:36:44,640
then you're locked
into a set of questions

672
00:36:44,640 --> 00:36:48,400
and a set of approaches
that you might try.

673
00:36:48,400 --> 00:36:52,090
Now, when we come back to
looking at machine learning,

674
00:36:52,090 --> 00:36:56,060
machines don't have the
same limitations as people.

675
00:36:56,060 --> 00:36:59,590
And so for a machine to
integrate over a vast number of

676
00:36:59,590 --> 00:37:04,960
possibilities is not difficult.
But for a human cognition to do

677
00:37:04,960 --> 00:37:06,890
that is very hard.

678
00:37:06,890 --> 00:37:10,120
And so this is actually an
important characteristic

679
00:37:10,120 --> 00:37:14,660
of the way doctors think
about diagnostic reasoning.

680
00:37:14,660 --> 00:37:18,340
So then, having made a
diagnosis or made a guess,

681
00:37:18,340 --> 00:37:20,680
they plan some kind of therapy.

682
00:37:20,680 --> 00:37:24,220
They apply that therapy,
and then they wait awhile

683
00:37:24,220 --> 00:37:26,410
and they see what happened.

684
00:37:26,410 --> 00:37:31,210
So if your diagnosis led
you to a choice of therapy,

685
00:37:31,210 --> 00:37:33,910
you gave that therapy to
the patient and the patient

686
00:37:33,910 --> 00:37:36,340
got better, then you
say, well, it must

687
00:37:36,340 --> 00:37:38,980
have been the right diagnosis.

688
00:37:38,980 --> 00:37:41,140
If the patient
didn't get better,

689
00:37:41,140 --> 00:37:44,640
then you say, well, how did
what happened to the patient

690
00:37:44,640 --> 00:37:49,570
differ from what I expected
to happen to the patient?

691
00:37:49,570 --> 00:37:54,070
And that drives your revision
of this whole process.

692
00:37:54,070 --> 00:37:57,090
So we again examine
what happened

693
00:37:57,090 --> 00:37:58,950
as a result of the therapy.

694
00:37:58,950 --> 00:38:00,090
We gather more data.

695
00:38:00,090 --> 00:38:01,020
We interpret it.

696
00:38:01,020 --> 00:38:04,530
We come up with the revised
diagnostic hypothesis.

697
00:38:04,530 --> 00:38:07,350
We come up with a
revised therapeutic plan,

698
00:38:07,350 --> 00:38:11,040
and we keep going
around the cycle.

699
00:38:11,040 --> 00:38:14,910
Now, that cycle
happens very quickly

700
00:38:14,910 --> 00:38:18,360
if you're a hospitalized
patient, because you're there

701
00:38:18,360 --> 00:38:19,110
all the time.

702
00:38:19,110 --> 00:38:20,070
You're available.

703
00:38:20,070 --> 00:38:23,170
They're trying to do
things to you constantly.

704
00:38:23,170 --> 00:38:28,600
And so this cycle happens on
the order of hours or a day,

705
00:38:28,600 --> 00:38:30,570
whereas if you were
an outpatient, then

706
00:38:30,570 --> 00:38:32,940
you're not dealing with
some urgent problem.

707
00:38:32,940 --> 00:38:36,060
It may happen over a
much slower period.

708
00:38:36,060 --> 00:38:39,190
It may be that your
doctor says, well,

709
00:38:39,190 --> 00:38:40,860
we're going to
adjust your drug dose

710
00:38:40,860 --> 00:38:44,400
and see if that helps bring
down your cholesterol,

711
00:38:44,400 --> 00:38:48,750
or manage your pain, or whatever
it is that he's trying to do.

712
00:38:48,750 --> 00:38:51,060
Or worse yet, we're
going to try to convince

713
00:38:51,060 --> 00:38:55,050
you to eat more healthy food,
and six months later we'll

714
00:38:55,050 --> 00:38:58,500
see if your hemoglobin
A1C came down, that you're

715
00:38:58,500 --> 00:39:02,310
less close to being diabetic.

716
00:39:02,310 --> 00:39:04,560
So the time scale
is very different.

717
00:39:04,560 --> 00:39:11,220
But that process of continually
reinterpreting things

718
00:39:11,220 --> 00:39:15,280
is a really critical feature, I
think, of all of medical care.

719
00:39:15,280 --> 00:39:20,640
And if you look back,
Alan Turing actually

720
00:39:20,640 --> 00:39:25,260
talked, in the early
1950s, about health care

721
00:39:25,260 --> 00:39:28,440
as being one of the
interesting application areas

722
00:39:28,440 --> 00:39:31,980
of artificial intelligence.

723
00:39:31,980 --> 00:39:32,550
Why?

724
00:39:32,550 --> 00:39:35,130
Well, because it was
an important topic.

725
00:39:35,130 --> 00:39:37,530
And he had the vision
that says, as we

726
00:39:37,530 --> 00:39:40,510
start getting more
data about health care,

727
00:39:40,510 --> 00:39:42,990
we're going to be able to
build the kinds of models

728
00:39:42,990 --> 00:39:47,670
that we're going to be
talking about in this class.

729
00:39:47,670 --> 00:39:53,803
But a lot of the early work took
a kind of one-shot approach.

730
00:39:53,803 --> 00:39:55,470
So they said, well,
we're going to solve

731
00:39:55,470 --> 00:39:57,520
the diagnostic problem.

732
00:39:57,520 --> 00:40:00,570
So we're going to take a
snapshot of a patient, all

733
00:40:00,570 --> 00:40:02,340
their data at a
particular moment.

734
00:40:02,340 --> 00:40:04,530
We're going to feed
it into an algorithm.

735
00:40:04,530 --> 00:40:06,660
It'll come up with a diagnosis.

736
00:40:06,660 --> 00:40:09,490
We're done.

737
00:40:09,490 --> 00:40:13,060
And that wasn't very
useful, because it

738
00:40:13,060 --> 00:40:17,650
didn't obey the cyclic nature of
the process of providing health

739
00:40:17,650 --> 00:40:18,730
care.

740
00:40:18,730 --> 00:40:22,480
And so this was a revolution
that started, really,

741
00:40:22,480 --> 00:40:26,440
around the 1980s when people
realized that you have

742
00:40:26,440 --> 00:40:29,620
to be in it for the
long-run and not for sort

743
00:40:29,620 --> 00:40:31,550
of single interactions.

744
00:40:31,550 --> 00:40:37,640
OK, well, this is
just some definitions

745
00:40:37,640 --> 00:40:40,260
of these care processes.

746
00:40:40,260 --> 00:40:46,580
So here I've listed some ideas
that came from a 1976 paper

747
00:40:46,580 --> 00:40:50,780
by several of my
colleagues, who said, well,

748
00:40:50,780 --> 00:40:53,750
here's a cognitive
theory of diagnosis.

749
00:40:53,750 --> 00:40:59,240
From the initial complaints,
guess a suitable hypothesis.

750
00:40:59,240 --> 00:41:01,850
Use the current
active hypotheses

751
00:41:01,850 --> 00:41:03,710
to guide questioning--

752
00:41:03,710 --> 00:41:08,430
so to order more tests, to
ask questions of the patient.

753
00:41:08,430 --> 00:41:11,460
And it's the failure
to satisfy expectations

754
00:41:11,460 --> 00:41:15,750
that's the strongest clue to how
to develop a better hypothesis.

755
00:41:18,540 --> 00:41:22,140
And then the hypotheses
could be in an activated,

756
00:41:22,140 --> 00:41:25,740
deactivated, confirmed,
or rejected state.

757
00:41:25,740 --> 00:41:27,930
They actually built
a computer program

758
00:41:27,930 --> 00:41:32,460
that implemented this theory
of diagnostic reasoning.

759
00:41:32,460 --> 00:41:38,540
And these rules, essentially,
about whether to activate,

760
00:41:38,540 --> 00:41:41,370
deactivate, confirm,
or reject something

761
00:41:41,370 --> 00:41:44,340
could be based both
on logical criteria

762
00:41:44,340 --> 00:41:49,210
and on a kind of very
bad probabilistic model.

763
00:41:49,210 --> 00:41:52,230
So it was very bad,
because what they really

764
00:41:52,230 --> 00:41:54,510
needed was Bayesian networks.

765
00:41:54,510 --> 00:41:59,280
And those were about a decade
in the future at that point.

766
00:41:59,280 --> 00:42:04,920
So they and every other
system built in the 1970s

767
00:42:04,920 --> 00:42:07,320
had really horrible
probabilistic models,

768
00:42:07,320 --> 00:42:10,570
because we didn't understand
how to do it correctly.

769
00:42:10,570 --> 00:42:13,080
Now, what's interesting
is somebody noticed

770
00:42:13,080 --> 00:42:16,110
that if you strip away
the medicine from this,

771
00:42:16,110 --> 00:42:20,760
this is kind of like the
scientific method, right?

772
00:42:20,760 --> 00:42:23,340
If you're trying to
understand something,

773
00:42:23,340 --> 00:42:24,810
you form a hypothesis.

774
00:42:24,810 --> 00:42:26,670
You perform an experiment.

775
00:42:26,670 --> 00:42:30,330
If the experiment is consistent
with your expectations,

776
00:42:30,330 --> 00:42:33,420
then you go on and you've gotten
a little bit more confident

777
00:42:33,420 --> 00:42:35,010
in your hypothesis.

778
00:42:35,010 --> 00:42:36,960
If your experiment
is inconsistent

779
00:42:36,960 --> 00:42:40,740
with your expectations, then
you have to change your theory,

780
00:42:40,740 --> 00:42:42,510
change your hypothesis.

781
00:42:42,510 --> 00:42:46,140
You go back and gather more
data, and then keep doing this

782
00:42:46,140 --> 00:42:48,180
until you're satisfied
that you've come up

783
00:42:48,180 --> 00:42:50,290
with an adequate theory.

784
00:42:50,290 --> 00:42:52,890
So this was a
surprise to doctors,

785
00:42:52,890 --> 00:42:55,410
because they thought
of themselves more

786
00:42:55,410 --> 00:42:58,090
as artists than as scientists.

787
00:42:58,090 --> 00:43:00,480
But in a way, they
act like scientists,

788
00:43:00,480 --> 00:43:02,620
which is kind of cool.

789
00:43:02,620 --> 00:43:06,390
All right, this doesn't
stop with caring

790
00:43:06,390 --> 00:43:08,310
for a single patient.

791
00:43:08,310 --> 00:43:12,510
So we have all these meta-level
processes about the acquisition

792
00:43:12,510 --> 00:43:15,840
and the application of
knowledge about education,

793
00:43:15,840 --> 00:43:20,280
quality control and process
improvement, cost containment,

794
00:43:20,280 --> 00:43:22,260
and developing references.

795
00:43:22,260 --> 00:43:25,320
So this is a picture
from David Margulies,

796
00:43:25,320 --> 00:43:28,950
who was chief information
officer at Children's Hospital

797
00:43:28,950 --> 00:43:29,820
here.

798
00:43:29,820 --> 00:43:34,500
And the cycle that I described
is the one right here.

799
00:43:34,500 --> 00:43:38,160
This is the care team
taking care of a patient.

800
00:43:38,160 --> 00:43:42,420
But of course at some point,
that patient is discharged.

801
00:43:42,420 --> 00:43:46,080
And then they're in
community care and self-care,

802
00:43:46,080 --> 00:43:49,230
and then maybe they're in some
sort of active health status

803
00:43:49,230 --> 00:43:51,480
management.

804
00:43:51,480 --> 00:43:54,390
And then if that goes
badly, then there's

805
00:43:54,390 --> 00:43:58,080
some episode where they
reconnect to the health care

806
00:43:58,080 --> 00:43:59,170
system.

807
00:43:59,170 --> 00:44:01,890
They get authorized
to come back.

808
00:44:01,890 --> 00:44:06,310
They get scheduled for a visit,
and they're back in the cycle.

809
00:44:06,310 --> 00:44:10,590
And so the process
of care involves

810
00:44:10,590 --> 00:44:13,350
people going to the
hospital, getting taken

811
00:44:13,350 --> 00:44:17,040
care of, they get better,
they get discharged,

812
00:44:17,040 --> 00:44:19,350
they live their
lives for a while.

813
00:44:19,350 --> 00:44:21,060
Maybe they get sick again.

814
00:44:21,060 --> 00:44:21,960
They come back.

815
00:44:21,960 --> 00:44:26,910
And so there's this larger
cycle around that issue.

816
00:44:26,910 --> 00:44:31,590
And then around that are all
kinds of things about health

817
00:44:31,590 --> 00:44:36,660
plan design and membership
and what coverage

818
00:44:36,660 --> 00:44:38,400
you have and so on.

819
00:44:38,400 --> 00:44:41,880
And then I would add
one more idea, which

820
00:44:41,880 --> 00:44:45,690
is that if you have
a system like this,

821
00:44:45,690 --> 00:44:50,340
you actually want to study,
at the next meta-level,

822
00:44:50,340 --> 00:44:53,980
that system, make
observations about it,

823
00:44:53,980 --> 00:44:57,360
analyze it, model it,
plan some improvements,

824
00:44:57,360 --> 00:45:02,310
and then intervene in the system
and observe how it's working

825
00:45:02,310 --> 00:45:05,340
and try to make it better.

826
00:45:05,340 --> 00:45:11,340
So in terms of tasks that are
important for us in this class,

827
00:45:11,340 --> 00:45:17,490
this class of tasks is
central, because one

828
00:45:17,490 --> 00:45:20,100
of the things we're
trying to do is

829
00:45:20,100 --> 00:45:22,020
to look at the way
health care works

830
00:45:22,020 --> 00:45:24,240
and to figure out
how to make it better

831
00:45:24,240 --> 00:45:26,520
by examining its operation.

832
00:45:26,520 --> 00:45:29,310
And that can be done at
any of these three levels.

833
00:45:29,310 --> 00:45:32,940
It can be done in the
more acute phase, where

834
00:45:32,940 --> 00:45:34,440
we're dealing with
somebody who's

835
00:45:34,440 --> 00:45:36,090
in the middle of an illness.

836
00:45:36,090 --> 00:45:39,840
It can be done at the larger
phase of somebody who's

837
00:45:39,840 --> 00:45:42,630
going through the cycle
of being well for a while

838
00:45:42,630 --> 00:45:46,080
and then being sick, and then
being well again and being sick

839
00:45:46,080 --> 00:45:46,950
again.

840
00:45:46,950 --> 00:45:50,980
And it can be in terms
of the system itself,

841
00:45:50,980 --> 00:45:54,570
of how do you design a health
care system that works better

842
00:45:54,570 --> 00:45:56,290
for the population?

843
00:45:56,290 --> 00:45:59,100
So this notion of a
learning health care system

844
00:45:59,100 --> 00:46:01,290
is now a journal.

845
00:46:01,290 --> 00:46:05,790
So in 2017, Chuck Friedman
at the University of Michigan

846
00:46:05,790 --> 00:46:08,130
started this new journal.

847
00:46:08,130 --> 00:46:13,350
And it's full of articles
about this third level

848
00:46:13,350 --> 00:46:17,790
of external cycle.

849
00:46:17,790 --> 00:46:20,960
So how does the health
care system learn?

850
00:46:20,960 --> 00:46:23,900
Well, I'll tell you an anecdote.

851
00:46:23,900 --> 00:46:30,900
In the mid-1980s, I was teaching
an AI expert systems course.

852
00:46:30,900 --> 00:46:33,440
And I had just come
back from a conference

853
00:46:33,440 --> 00:46:35,960
of medical informatics
people, where

854
00:46:35,960 --> 00:46:39,200
they were talking about
this great new idea called

855
00:46:39,200 --> 00:46:42,090
evidence-based medicine.

856
00:46:42,090 --> 00:46:45,600
And I remember mentioning this
to a bunch of MIT engineering

857
00:46:45,600 --> 00:46:46,260
students.

858
00:46:46,260 --> 00:46:50,900
And one of them raised his hand
and said, as opposed to what?

859
00:46:50,900 --> 00:46:52,160
Right?

860
00:46:52,160 --> 00:46:54,540
I mean, to an
engineer, it's obvious

861
00:46:54,540 --> 00:46:58,260
that evidence is the
basis on which you analyze

862
00:46:58,260 --> 00:47:00,460
things and make things better.

863
00:47:00,460 --> 00:47:03,120
But it wasn't
obvious to doctors.

864
00:47:03,120 --> 00:47:06,820
And so this was almost
a revolutionary change.

865
00:47:06,820 --> 00:47:09,120
And the idea that
they fostered was

866
00:47:09,120 --> 00:47:13,510
the idea of the randomized
controlled clinical trial.

867
00:47:13,510 --> 00:47:17,070
So I'm going to sketch
what that looks like.

868
00:47:17,070 --> 00:47:18,750
Of course, there
are many variations.

869
00:47:18,750 --> 00:47:22,530
But suppose that I'm one of the
drug companies around Kendall

870
00:47:22,530 --> 00:47:25,830
Square, and I come
up with a new drug

871
00:47:25,830 --> 00:47:28,770
and I want to prove that it's
more effective for condition

872
00:47:28,770 --> 00:47:32,540
X than some existing drug B.

873
00:47:32,540 --> 00:47:34,130
So what do I do?

874
00:47:34,130 --> 00:47:38,060
I find some patients
who are suffering from X

875
00:47:38,060 --> 00:47:41,000
and I try very hard to
find patients who are not

876
00:47:41,000 --> 00:47:43,220
suffering from anything else.

877
00:47:43,220 --> 00:47:46,290
I want the purest case possible.

878
00:47:46,290 --> 00:47:49,190
I then go to my
statisticians and I

879
00:47:49,190 --> 00:47:52,310
say, let's design
an experiment where

880
00:47:52,310 --> 00:47:55,550
we're going to collect
a standard set of data

881
00:47:55,550 --> 00:47:57,880
about all these patients.

882
00:47:57,880 --> 00:48:02,500
And then we're going to give
some of Drug A and some of them

883
00:48:02,500 --> 00:48:06,280
Drug B, and we'll see
which of them do better.

884
00:48:06,280 --> 00:48:10,180
And we'll pre-define what
we mean by do better.

885
00:48:10,180 --> 00:48:13,210
So like, not dying is
considered doing better,

886
00:48:13,210 --> 00:48:17,970
or not suffering some bad thing
that people are suffering from

887
00:48:17,970 --> 00:48:21,830
is considered doing better.

888
00:48:21,830 --> 00:48:25,240
And then the
statisticians also will

889
00:48:25,240 --> 00:48:30,370
tell me, given that you
expect Drug A to be,

890
00:48:30,370 --> 00:48:33,820
let's say, 10%
better than Drug B,

891
00:48:33,820 --> 00:48:37,480
how many patients do you
have to enroll in this trial

892
00:48:37,480 --> 00:48:40,990
in order to be likely to get
a statistically significant

893
00:48:40,990 --> 00:48:43,690
answer to that question?

894
00:48:43,690 --> 00:48:45,370
And then they do it.

895
00:48:45,370 --> 00:48:48,250
The statisticians
analyze the data.

896
00:48:48,250 --> 00:48:51,610
Hopefully you've gotten
p less than 0.05.

897
00:48:51,610 --> 00:48:53,680
You go to the Food and
Drug Administration

898
00:48:53,680 --> 00:48:57,670
and say, give me permission to
market this drug as the hottest

899
00:48:57,670 --> 00:49:01,150
new cure for something
or other, and then you

900
00:49:01,150 --> 00:49:02,990
make billions of dollars.

901
00:49:02,990 --> 00:49:03,490
Right?

902
00:49:03,490 --> 00:49:07,490
This is the standard
way that pharma works.

903
00:49:07,490 --> 00:49:09,770
Now, there are some problems.

904
00:49:09,770 --> 00:49:16,080
So most of the cases
to which the results

905
00:49:16,080 --> 00:49:19,620
of a trial like this are
applied wouldn't have

906
00:49:19,620 --> 00:49:23,530
qualified to be in the trial.

907
00:49:23,530 --> 00:49:29,150
For example, we talked
about morbidities, about

908
00:49:29,150 --> 00:49:31,860
the chronic problems
that people have.

909
00:49:31,860 --> 00:49:34,140
Well, if you're dealing
with one disease,

910
00:49:34,140 --> 00:49:37,620
you want to make sure that those
populations you're dealing with

911
00:49:37,620 --> 00:49:39,880
don't have any of
these other diseases.

912
00:49:39,880 --> 00:49:42,430
But in the real
world, people do.

913
00:49:42,430 --> 00:49:44,430
And so we've never
actually measured

914
00:49:44,430 --> 00:49:47,820
what happens to those people
if you give them this drug

915
00:49:47,820 --> 00:49:52,020
and they have these
comorbidities.

916
00:49:52,020 --> 00:49:54,420
The other problem is
that the drug company

917
00:49:54,420 --> 00:49:57,450
wants to start making
these billions of dollars

918
00:49:57,450 --> 00:49:59,650
as soon as possible.

919
00:49:59,650 --> 00:50:03,780
And so they want the trial
to be as short as possible.

920
00:50:03,780 --> 00:50:06,900
And they want it to be
as small a sample as they

921
00:50:06,900 --> 00:50:11,320
need to get that 0.05
statistical significance.

922
00:50:11,320 --> 00:50:17,410
So these are all problematic,
and they lead to real problems.

923
00:50:17,410 --> 00:50:19,560
So I didn't bring any
examples, but there

924
00:50:19,560 --> 00:50:23,400
are plenty of stories
where the FDA has approved

925
00:50:23,400 --> 00:50:25,770
some drug on this
basis, and then

926
00:50:25,770 --> 00:50:30,450
later they discovered that
although the drug works well

927
00:50:30,450 --> 00:50:32,910
in the short-term,
it has horrible side

928
00:50:32,910 --> 00:50:36,600
effects in the long-term,
or that it has interactions

929
00:50:36,600 --> 00:50:39,960
with other diseases where
it doesn't work effectively

930
00:50:39,960 --> 00:50:45,600
for people except for these pure
cases that it was tested on.

931
00:50:45,600 --> 00:50:50,520
So the other idea, the
competing idea is to say, let's

932
00:50:50,520 --> 00:50:54,120
build this learning health
care system in which progress

933
00:50:54,120 --> 00:50:55,650
in science, informatics--

934
00:50:55,650 --> 00:50:58,680
whatever-- generates
new knowledge

935
00:50:58,680 --> 00:51:02,820
as an ongoing natural byproduct
of the care experience

936
00:51:02,820 --> 00:51:07,200
and we seamlessly refine
and deliver best practices

937
00:51:07,200 --> 00:51:10,350
for continuous improvement
in health and health care.

938
00:51:10,350 --> 00:51:13,170
Wonderful words from the
Institute of Medicine,

939
00:51:13,170 --> 00:51:17,760
now called the National
Academy of Medicine.

940
00:51:17,760 --> 00:51:19,870
But it's hard to do this.

941
00:51:19,870 --> 00:51:21,660
And the reason it's
hard to do this

942
00:51:21,660 --> 00:51:26,880
is mainly for a very
profound underlying reason,

943
00:51:26,880 --> 00:51:29,700
which is that people
are not treated

944
00:51:29,700 --> 00:51:32,280
by experimental protocols.

945
00:51:32,280 --> 00:51:35,520
So it's very important
in that sketch

946
00:51:35,520 --> 00:51:39,120
that I gave you of the
Drug A versus Drug B

947
00:51:39,120 --> 00:51:43,950
that there is a randomization
step where I flip a coin

948
00:51:43,950 --> 00:51:47,430
to decide which drug any
particular individual is going

949
00:51:47,430 --> 00:51:49,230
to get.

950
00:51:49,230 --> 00:51:55,140
If I allow that decision to
be biased by my expectations

951
00:51:55,140 --> 00:51:57,990
or by something else I
know about the patient,

952
00:51:57,990 --> 00:52:01,320
then I'm no longer
doing a fair trial.

953
00:52:01,320 --> 00:52:03,600
And of course,
when I collect data

954
00:52:03,600 --> 00:52:06,840
about how actual patients
are being treated,

955
00:52:06,840 --> 00:52:10,020
they're being treated according
to what their doctors think

956
00:52:10,020 --> 00:52:14,360
is best for them, and so
there is no randomization.

957
00:52:14,360 --> 00:52:17,310
I mean, if I went to Mass
General and said, could you

958
00:52:17,310 --> 00:52:19,470
guys please treat
everybody randomly

959
00:52:19,470 --> 00:52:23,010
so that we collect
really good data,

960
00:52:23,010 --> 00:52:27,310
they would throw
me out properly.

961
00:52:27,310 --> 00:52:32,520
OK, so we also need a whole lot
of technical infrastructure.

962
00:52:32,520 --> 00:52:35,910
We need to capture all kinds of
novel data sources, which we'll

963
00:52:35,910 --> 00:52:38,830
talk about in the next lecture.

964
00:52:38,830 --> 00:52:40,920
And then we need a
technical infrastructure

965
00:52:40,920 --> 00:52:42,780
for truly big data.

966
00:52:42,780 --> 00:52:51,230
So just for example, Dana Farber
started about five years ago--

967
00:52:51,230 --> 00:52:52,370
it's a cancer hospital.

968
00:52:52,370 --> 00:52:54,980
And so for every
solid tumor, they

969
00:52:54,980 --> 00:52:58,670
would take samples of the
tumor and genotype it--

970
00:52:58,670 --> 00:53:02,940
multiple samples, because
tumors are not uniform.

971
00:53:02,940 --> 00:53:06,230
So just storing that stuff
is a technical challenge,

972
00:53:06,230 --> 00:53:08,960
and being able to
come up with it.

973
00:53:08,960 --> 00:53:16,250
You've got three gigabases, so
about over a gigabyte of data--

974
00:53:16,250 --> 00:53:19,060
from each sample,
from each tumor,

975
00:53:19,060 --> 00:53:23,420
times all the people who
come in and have this test.

976
00:53:23,420 --> 00:53:26,720
So you buy some big
disk drives or you farm

977
00:53:26,720 --> 00:53:28,620
it out to Google or something.

978
00:53:28,620 --> 00:53:30,830
But then you need to
organize it in some way

979
00:53:30,830 --> 00:53:36,170
so that it's usefully
easy to find that data.

980
00:53:36,170 --> 00:53:40,520
So today's technique,
today's prejudice

981
00:53:40,520 --> 00:53:44,870
is what I call the meat
grinder story, which

982
00:53:44,870 --> 00:53:47,220
is you take medical
records, genetic data,

983
00:53:47,220 --> 00:53:50,180
environmental data,
data from wearables,

984
00:53:50,180 --> 00:53:53,210
you put them into an
old-fashioned meat grinder,

985
00:53:53,210 --> 00:53:56,780
and out come bits, which
you store on a disk.

986
00:53:56,780 --> 00:53:58,880
And then you have all
the data from which

987
00:53:58,880 --> 00:54:00,570
you can build models.

988
00:54:00,570 --> 00:54:02,030
And that's what we do.

989
00:54:02,030 --> 00:54:05,990
You're going to see a lot
of that in this course.

990
00:54:05,990 --> 00:54:11,070
OK, the other thing that
medicine tries to do

991
00:54:11,070 --> 00:54:14,430
is not to cure people
but to keep them healthy.

992
00:54:14,430 --> 00:54:19,200
And this has been pretty much
the domain of the public health

993
00:54:19,200 --> 00:54:20,730
infrastructure.

994
00:54:20,730 --> 00:54:24,980
So if you go across the river
to the Harvard Medical area,

995
00:54:24,980 --> 00:54:26,730
there are a couple of
big buildings, which

996
00:54:26,730 --> 00:54:28,560
is the Harvard School
of Public Health,

997
00:54:28,560 --> 00:54:31,720
and this is what
they're all about.

998
00:54:31,720 --> 00:54:35,490
So they do things like
tracking disease prevalence

999
00:54:35,490 --> 00:54:37,860
and tracking
infections, and worrying

1000
00:54:37,860 --> 00:54:40,110
about quarantining people.

1001
00:54:40,110 --> 00:54:42,330
They also do a lot
of the kind of work

1002
00:54:42,330 --> 00:54:45,570
we're going to talk about in
this class, which is modeling

1003
00:54:45,570 --> 00:54:50,130
in order to try to understand
what's going on in individual's

1004
00:54:50,130 --> 00:54:52,620
health, in the health
of a population,

1005
00:54:52,620 --> 00:54:55,620
in the operations of
a health care system.

1006
00:54:55,620 --> 00:54:57,810
So they're very much into this.

1007
00:54:57,810 --> 00:55:03,320
Now historically, I looked
back and it turns out

1008
00:55:03,320 --> 00:55:07,160
there's something called the
London Bills of Mortality

1009
00:55:07,160 --> 00:55:12,890
in the 17th century, started by
a gentleman named John Graunt.

1010
00:55:12,890 --> 00:55:17,210
And he was interested
just in figuring out,

1011
00:55:17,210 --> 00:55:20,090
how long do people live?

1012
00:55:20,090 --> 00:55:24,020
And so he came up with
these bills of mortality,

1013
00:55:24,020 --> 00:55:26,930
where he went around to
different parts of London,

1014
00:55:26,930 --> 00:55:29,780
talked to undertakers
and hospitals

1015
00:55:29,780 --> 00:55:34,130
and whatever health care
providers existed at the time,

1016
00:55:34,130 --> 00:55:38,210
and collected data
on what people died

1017
00:55:38,210 --> 00:55:40,890
and how many people were
living in that area.

1018
00:55:40,890 --> 00:55:46,550
And so for example, he estimated
that the mortality before age

1019
00:55:46,550 --> 00:55:52,220
six in the 17th century-- this
was a long time ago, 1600s--

1020
00:55:52,220 --> 00:55:54,750
was about 36%.

1021
00:55:54,750 --> 00:56:00,060
So if you were a kid, your
chances of making it to age six

1022
00:56:00,060 --> 00:56:06,720
were only about 64%,
so less than 2/3.

1023
00:56:06,720 --> 00:56:08,850
Kind of shocking.

1024
00:56:08,850 --> 00:56:12,030
In the 18th century,
people you've never heard

1025
00:56:12,030 --> 00:56:14,850
of-- and Linnaeus, who
you probably have heard

1026
00:56:14,850 --> 00:56:17,790
of, because he was one
of the early taxonomists

1027
00:56:17,790 --> 00:56:22,710
for biological and animal
species and so on--

1028
00:56:22,710 --> 00:56:26,580
made the first attempts at
systemic classification.

1029
00:56:26,580 --> 00:56:31,110
In the mid-1850s,
mid-1800s, there

1030
00:56:31,110 --> 00:56:34,890
was a Congress of the First
International Statistical

1031
00:56:34,890 --> 00:56:36,270
Congress.

1032
00:56:36,270 --> 00:56:39,990
And a gentleman named
William Farr came up

1033
00:56:39,990 --> 00:56:44,110
with an interesting
categorization that said, well,

1034
00:56:44,110 --> 00:56:46,980
if we're going to
taxonomize the diseases,

1035
00:56:46,980 --> 00:56:50,010
we should separate
epidemic diseases

1036
00:56:50,010 --> 00:56:54,480
from constitutional diseases
from local diseases,

1037
00:56:54,480 --> 00:56:57,060
whereby "local"
he means affecting

1038
00:56:57,060 --> 00:57:02,810
a particular part of the body
from developmental diseases.

1039
00:57:02,810 --> 00:57:06,070
So this is things
like stunted growth

1040
00:57:06,070 --> 00:57:10,930
or failure of mental development
or speech development,

1041
00:57:10,930 --> 00:57:14,000
and then diseases that are
the direct result of violence.

1042
00:57:14,000 --> 00:57:18,880
So this is things like broken
bones and bar fight results

1043
00:57:18,880 --> 00:57:21,110
and stuff like that.

1044
00:57:21,110 --> 00:57:24,400
So that was the
first classification

1045
00:57:24,400 --> 00:57:28,780
of disease in about 1853.

1046
00:57:28,780 --> 00:57:32,170
Note, by the way, that this
is before Louis Pasteur

1047
00:57:32,170 --> 00:57:36,260
and his theory of the
germ cause of disease.

1048
00:57:36,260 --> 00:57:38,500
And so this was a
pretty early attempt,

1049
00:57:38,500 --> 00:57:42,400
and obviously could
have benefited from what

1050
00:57:42,400 --> 00:57:45,010
Pasteur later discovered.

1051
00:57:45,010 --> 00:57:49,210
So by the 1890s,
which is post-Pasteur,

1052
00:57:49,210 --> 00:57:51,730
they came up with
a classification

1053
00:57:51,730 --> 00:57:54,730
that was a hierarchic
classification, 44

1054
00:57:54,730 --> 00:58:00,910
top-level hierarchies broken up
into 99 lower-level categories

1055
00:58:00,910 --> 00:58:04,390
and 161 particular titles.

1056
00:58:04,390 --> 00:58:07,990
And they adopted this
as a way of getting,

1057
00:58:07,990 --> 00:58:10,900
typically, mortality
data of what was it

1058
00:58:10,900 --> 00:58:12,820
that people were dying of.

1059
00:58:12,820 --> 00:58:17,740
And by the 1920s,
you've heard of ICD--

1060
00:58:17,740 --> 00:58:20,090
ICD-9, ICD-10.

1061
00:58:20,090 --> 00:58:23,980
So this is currently used as
a way of classifying diseases

1062
00:58:23,980 --> 00:58:26,530
and disorders.

1063
00:58:26,530 --> 00:58:30,370
The International List
of the Causes of Death

1064
00:58:30,370 --> 00:58:35,410
was the first ICD back
in, I think, the 1920s.

1065
00:58:35,410 --> 00:58:39,820
And then it kept developing
through multiple versions.

1066
00:58:39,820 --> 00:58:43,210
In 1975, ICD-9 was adopted.

1067
00:58:43,210 --> 00:58:46,660
In 2015, ICD-10.

1068
00:58:46,660 --> 00:58:48,880
And these are under the
control of the World Health

1069
00:58:48,880 --> 00:58:52,720
Organization, which is now a
UN agency, although I think

1070
00:58:52,720 --> 00:58:56,290
it predates the UN actually.

1071
00:58:56,290 --> 00:59:00,630
And then we have ICD-9
CM and ICD-10 CM,

1072
00:59:00,630 --> 00:59:04,150
are US Clinical
Modifications that

1073
00:59:04,150 --> 00:59:08,390
are an extension of the
ICD-9 and 10 coding.

1074
00:59:08,390 --> 00:59:12,130
And they are primarily
used for billing.

1075
00:59:12,130 --> 00:59:15,490
But they're also used for
epidemiological research.

1076
00:59:15,490 --> 00:59:21,040
And if you look at the Centers
for Disease Control, CDC,

1077
00:59:21,040 --> 00:59:24,280
they collect death
certificates like this

1078
00:59:24,280 --> 00:59:26,180
from all over the country.

1079
00:59:26,180 --> 00:59:30,970
And so this is a person who died
of a cerebral hemorrhage which

1080
00:59:30,970 --> 00:59:35,140
was due to nephritis, which was
due to cirrhosis of the liver.

1081
00:59:35,140 --> 00:59:38,170
And so you can use this
kind of data to say,

1082
00:59:38,170 --> 00:59:40,820
well, here's the
immediate cause of death,

1083
00:59:40,820 --> 00:59:43,240
here's sort of the
proximate cause of death,

1084
00:59:43,240 --> 00:59:47,010
and here's the underlying
cause of death.

1085
00:59:47,010 --> 00:59:50,310
And so this is the sort
of statistical data

1086
00:59:50,310 --> 00:59:52,800
that we now have available.

1087
00:59:52,800 --> 00:59:59,352
Now, do any of you watch
the PBS show Victoria?

1088
00:59:59,352 --> 01:00:01,070
Nobody?

1089
01:00:01,070 --> 01:00:02,720
You're not television watchers.

1090
01:00:02,720 --> 01:00:06,260
All right, that's pretty cool.

1091
01:00:06,260 --> 01:00:12,410
I was stunned, because as I
was preparing this lecture

1092
01:00:12,410 --> 01:00:15,500
and I had the next
slide, it turns out

1093
01:00:15,500 --> 01:00:17,840
this plays a role in
one of the episodes

1094
01:00:17,840 --> 01:00:20,150
that was broadcast about
a week and a half ago.

1095
01:00:23,210 --> 01:00:30,050
So in the 1850s, there was a big
outbreak of cholera in London.

1096
01:00:30,050 --> 01:00:34,460
And John Snow was
a doctor who did

1097
01:00:34,460 --> 01:00:38,780
this amazing epidemiological
study to try to figure out

1098
01:00:38,780 --> 01:00:40,820
what was causing cholera.

1099
01:00:40,820 --> 01:00:44,600
The accepted opinion was that
cholera was caused by miasma.

1100
01:00:47,420 --> 01:00:49,652
What's a miasma?

1101
01:00:49,652 --> 01:00:51,110
STUDENT: Bad air.

1102
01:00:51,110 --> 01:00:53,360
PETER SZOLOVITS: Bad air, OK?

1103
01:00:53,360 --> 01:00:54,620
So it's bad air.

1104
01:00:54,620 --> 01:01:00,710
Somehow, bad air was causing
people to get sick and die.

1105
01:01:00,710 --> 01:01:04,040
And several hundred people died.

1106
01:01:04,040 --> 01:01:09,140
Whereas Snow started plotting
on a map of the area in London

1107
01:01:09,140 --> 01:01:13,660
where these were concentrated
where everybody lived.

1108
01:01:13,660 --> 01:01:16,690
And what he discovered,
interestingly enough,

1109
01:01:16,690 --> 01:01:19,660
is that right in the
middle of Broad Street,

1110
01:01:19,660 --> 01:01:23,560
which is pretty much at the
epicenter of all these people

1111
01:01:23,560 --> 01:01:28,530
dying, was a water pump that
everybody in the neighborhood

1112
01:01:28,530 --> 01:01:29,040
used.

1113
01:01:29,040 --> 01:01:35,170
And that water pump, its supply
had become infected by cholera.

1114
01:01:35,170 --> 01:01:37,140
And so people were
pumping water,

1115
01:01:37,140 --> 01:01:40,230
taking it home, drinking
it, and then dying,

1116
01:01:40,230 --> 01:01:42,630
or at least getting very sick.

1117
01:01:42,630 --> 01:01:45,900
And he looked at this
and he said, well,

1118
01:01:45,900 --> 01:01:52,620
if we turn off that pump,
the epidemic will stop.

1119
01:01:52,620 --> 01:01:55,890
And he actually went to
the queen, Queen Victoria--

1120
01:01:55,890 --> 01:01:59,250
hence the tie-in to
the television show--

1121
01:01:59,250 --> 01:02:03,540
and convinced her that this
was worth trying, because they

1122
01:02:03,540 --> 01:02:05,670
didn't have any better ideas.

1123
01:02:05,670 --> 01:02:08,010
And they took the pump
handle off the pump,

1124
01:02:08,010 --> 01:02:12,030
and sure enough the
cholera epidemic abated.

1125
01:02:12,030 --> 01:02:17,320
Now, of course the underlying
problem was sanitation.

1126
01:02:17,320 --> 01:02:18,610
And they didn't fix that.

1127
01:02:18,610 --> 01:02:21,520
That took longer.

1128
01:02:21,520 --> 01:02:24,070
But what's
interesting is here is

1129
01:02:24,070 --> 01:02:30,590
a 2003 study of the
spread of West Nile virus.

1130
01:02:30,590 --> 01:02:35,710
So this is mosquitoes that are
biting people and infecting

1131
01:02:35,710 --> 01:02:38,650
them with this nasty disease.

1132
01:02:38,650 --> 01:02:41,920
And they actually used
very similar techniques

1133
01:02:41,920 --> 01:02:45,070
to figure out that
maybe this was coming in

1134
01:02:45,070 --> 01:02:48,310
on airplanes through JFK.

1135
01:02:48,310 --> 01:02:53,470
So mosquitoes were hitching
a ride on an airplane

1136
01:02:53,470 --> 01:02:55,780
and coming into the US.

1137
01:02:55,780 --> 01:03:01,675
We need to build a border
wall against mosquitoes.

1138
01:03:04,470 --> 01:03:08,120
There is also a very
controversial practice

1139
01:03:08,120 --> 01:03:11,870
that used to be used a lot by
public health officials, which

1140
01:03:11,870 --> 01:03:14,400
was to quarantine people.

1141
01:03:14,400 --> 01:03:18,110
And so there are
lots of examples.

1142
01:03:18,110 --> 01:03:22,200
Anybody's relatives come
through Ellis Island?

1143
01:03:22,200 --> 01:03:23,610
Must be a few.

1144
01:03:23,610 --> 01:03:27,240
OK, so they were
subject to quarantine.

1145
01:03:27,240 --> 01:03:30,480
If you were sick when you
arrived at Ellis Island

1146
01:03:30,480 --> 01:03:33,630
and they didn't know
exactly how sick you were,

1147
01:03:33,630 --> 01:03:36,840
they would put you in a
building and wait a month

1148
01:03:36,840 --> 01:03:39,540
and see if you got better
or if you got worse,

1149
01:03:39,540 --> 01:03:42,870
and then decide whether to
send you back or to let you in.

1150
01:03:42,870 --> 01:03:45,060
So that was a pretty
common practice.

1151
01:03:45,060 --> 01:03:47,940
There's a famous
story of Typhoid Mary,

1152
01:03:47,940 --> 01:03:50,610
who was a carrier
of typhoid fever

1153
01:03:50,610 --> 01:03:53,280
but was not herself affected.

1154
01:03:53,280 --> 01:03:55,460
Unfortunately, she was a cook.

1155
01:03:55,460 --> 01:04:00,210
And wherever she was employed,
people got really sick.

1156
01:04:00,210 --> 01:04:03,690
And eventually, the New
York Department of Health

1157
01:04:03,690 --> 01:04:09,810
forcibly essentially jailed
her in some sanatorium

1158
01:04:09,810 --> 01:04:11,910
in order to keep
her from continuing

1159
01:04:11,910 --> 01:04:15,120
to infect people-- it was a
very controversial case, as you

1160
01:04:15,120 --> 01:04:17,160
might imagine.

1161
01:04:17,160 --> 01:04:18,870
You don't have to
go that far back.

1162
01:04:18,870 --> 01:04:25,170
Here's a 1987 article from
UPI from The Chicago Tribune.

1163
01:04:25,170 --> 01:04:28,200
So Jesse Helms,
who was a senator,

1164
01:04:28,200 --> 01:04:33,550
was calling for everybody who
has AIDS to be quarantined.

1165
01:04:33,550 --> 01:04:36,220
So fortunately,
that didn't happen.

1166
01:04:36,220 --> 01:04:40,780
But the idea is still
around, to say, well,

1167
01:04:40,780 --> 01:04:43,990
we're going to stop this
infection by quarantining

1168
01:04:43,990 --> 01:04:45,280
people.

1169
01:04:45,280 --> 01:04:51,740
And then here's a recent
report about the Ebola response

1170
01:04:51,740 --> 01:04:55,090
in Africa over the
past few years,

1171
01:04:55,090 --> 01:04:59,230
when Ebola was ravaging
parts of that continent.

1172
01:04:59,230 --> 01:05:03,160
And their conclusions
are it's controversial,

1173
01:05:03,160 --> 01:05:07,210
a debated issue, significant
risks related to human rights.

1174
01:05:07,210 --> 01:05:09,970
Quarantine should be
used as a last resort.

1175
01:05:09,970 --> 01:05:13,570
Quarantines in urban
areas are really hard.

1176
01:05:13,570 --> 01:05:15,900
Mobile populations make it hard.

1177
01:05:15,900 --> 01:05:20,920
And this is the most
technical conclusion,

1178
01:05:20,920 --> 01:05:23,320
that if you're going to
quarantine a bunch of people,

1179
01:05:23,320 --> 01:05:26,720
you have a huge waste disposal
problem on your hands.

1180
01:05:26,720 --> 01:05:29,680
Because if you have people
who might have Ebola,

1181
01:05:29,680 --> 01:05:31,900
you can't just
take their garbage

1182
01:05:31,900 --> 01:05:33,730
and throw it out somewhere.

1183
01:05:33,730 --> 01:05:36,700
You have to dispose
of it properly.

1184
01:05:36,700 --> 01:05:39,910
OK, so the last thing I
wanted to talk about--

1185
01:05:39,910 --> 01:05:45,260
hopefully mercifully short--
will be paying for health care.

1186
01:05:45,260 --> 01:05:49,780
So I remember reading
about 20 years ago

1187
01:05:49,780 --> 01:05:53,260
that if you bought a
Chevy from General Motors,

1188
01:05:53,260 --> 01:05:56,740
they spent more money on
health insurance and health

1189
01:05:56,740 --> 01:06:00,580
care for their employees than
they did on the steel that

1190
01:06:00,580 --> 01:06:02,498
went into your car.

1191
01:06:02,498 --> 01:06:03,415
That's pretty amazing.

1192
01:06:06,600 --> 01:06:07,570
So why is this?

1193
01:06:07,570 --> 01:06:10,440
Well, essentially, there's
an insatiable demand

1194
01:06:10,440 --> 01:06:12,780
for health care, right?

1195
01:06:12,780 --> 01:06:17,970
Nobody wants to die, except
for the suicidal people.

1196
01:06:17,970 --> 01:06:22,890
And so if I'm sick, I want
the best care possible,

1197
01:06:22,890 --> 01:06:26,340
and I want as much
of it as possible.

1198
01:06:26,340 --> 01:06:29,940
Because you know, what
is more important in life

1199
01:06:29,940 --> 01:06:33,040
than continuing to live?

1200
01:06:33,040 --> 01:06:39,330
So we also have gotten better at
making drugs and better tests,

1201
01:06:39,330 --> 01:06:40,410
and so on.

1202
01:06:40,410 --> 01:06:43,410
I remember one MRI
machines became

1203
01:06:43,410 --> 01:06:47,040
popular about 30 years ago.

1204
01:06:47,040 --> 01:06:49,120
The state of
Massachusetts, for example,

1205
01:06:49,120 --> 01:06:51,750
had a commission that
you had to convince

1206
01:06:51,750 --> 01:06:57,360
them to be allowed to buy an
MRI machine for your hospital,

1207
01:06:57,360 --> 01:07:00,570
because MRI machines
were very expensive

1208
01:07:00,570 --> 01:07:04,260
and MRIs were, at that
time, hugely expensive.

1209
01:07:04,260 --> 01:07:07,380
And so they wanted to contain
the cost of health care

1210
01:07:07,380 --> 01:07:10,020
by limiting the number
of such machines.

1211
01:07:10,020 --> 01:07:12,070
Well, eventually
the costs came down.

1212
01:07:12,070 --> 01:07:13,600
And so we're doing better.

1213
01:07:13,600 --> 01:07:15,090
But if you read
the newspapers, you

1214
01:07:15,090 --> 01:07:18,550
see that drug therapy
is very expensive.

1215
01:07:18,550 --> 01:07:24,060
We have these wonder drugs
for rare diseases or cancers

1216
01:07:24,060 --> 01:07:29,490
that cost $1 million a year
to pay for your dosage.

1217
01:07:29,490 --> 01:07:33,090
And so there is a
high human motivation

1218
01:07:33,090 --> 01:07:36,450
to do this, and
not much pushback--

1219
01:07:36,450 --> 01:07:38,970
except from insurance companies.

1220
01:07:38,970 --> 01:07:43,410
But they just pass the cost
on in insurance contracts.

1221
01:07:43,410 --> 01:07:45,630
There's also waste.

1222
01:07:45,630 --> 01:07:48,690
So there are lots of
stories about half

1223
01:07:48,690 --> 01:07:51,120
of health care
expenses are spent

1224
01:07:51,120 --> 01:07:54,090
in the last year
of somebody's life.

1225
01:07:54,090 --> 01:07:57,390
Although, Ingelfinger, who was
the editor of The New England

1226
01:07:57,390 --> 01:08:00,820
Journal, gave a talk
here about 25 years ago.

1227
01:08:00,820 --> 01:08:03,420
And he said, you know, when
I was a practicing doctor,

1228
01:08:03,420 --> 01:08:06,210
no patient ever came into
my office saying, doc,

1229
01:08:06,210 --> 01:08:09,120
I'm in the last year of my life.

1230
01:08:09,120 --> 01:08:12,900
And so that was a
difficult criterion

1231
01:08:12,900 --> 01:08:15,660
to try to operationalize.

1232
01:08:15,660 --> 01:08:18,359
There are marginally
useful procedures.

1233
01:08:18,359 --> 01:08:20,609
The IOM estimated
that there are sort

1234
01:08:20,609 --> 01:08:24,750
of 40,000 to 100,000 quote
unquote unnecessary deaths

1235
01:08:24,750 --> 01:08:26,649
per year--

1236
01:08:26,649 --> 01:08:28,410
in other words,
deaths that could

1237
01:08:28,410 --> 01:08:31,140
be avoided by just being
more careful and a little bit

1238
01:08:31,140 --> 01:08:32,490
smarter.

1239
01:08:32,490 --> 01:08:36,899
Well, so the result of this is
that if you look at health care

1240
01:08:36,899 --> 01:08:41,250
spending as a percentage
of gross domestic product

1241
01:08:41,250 --> 01:08:47,069
from 1970 to I think 2017,
I believe, on this graph,

1242
01:08:47,069 --> 01:08:50,279
what you see is
one real outlier.

1243
01:08:50,279 --> 01:08:54,500
And that's the
United States on top.

1244
01:08:54,500 --> 01:08:58,939
So I've selected a few
of these just to look at.

1245
01:08:58,939 --> 01:09:01,000
There's the US on top.

1246
01:09:01,000 --> 01:09:05,109
France, Germany, a lot
of the European countries

1247
01:09:05,109 --> 01:09:11,109
are roughly at that
highest of the crowd level.

1248
01:09:11,109 --> 01:09:12,460
Canada is down there.

1249
01:09:12,460 --> 01:09:14,020
The UK is a little lower.

1250
01:09:14,020 --> 01:09:15,760
Spain is a little lower.

1251
01:09:15,760 --> 01:09:17,140
Israel is a little lower.

1252
01:09:17,140 --> 01:09:20,620
Turkey is the lowest
of the OECD countries

1253
01:09:20,620 --> 01:09:25,000
in terms of percentage of GDP
that they spend on health care.

1254
01:09:25,000 --> 01:09:26,590
So well, that's OK.

1255
01:09:26,590 --> 01:09:30,130
But maybe we're getting
more for our money

1256
01:09:30,130 --> 01:09:32,359
than these other countries.

1257
01:09:32,359 --> 01:09:34,600
And so there are
a lot of analyses

1258
01:09:34,600 --> 01:09:36,160
that look at stuff like this.

1259
01:09:36,160 --> 01:09:39,310
They say, well, if
we spend so much

1260
01:09:39,310 --> 01:09:43,990
money per patient
per year, what do

1261
01:09:43,990 --> 01:09:46,090
we get in terms of
the simplest thing

1262
01:09:46,090 --> 01:09:48,729
to measure, which
is life expectancy?

1263
01:09:48,729 --> 01:09:50,859
How long do people live?

1264
01:09:50,859 --> 01:09:53,950
And what we discover is
that in the United States,

1265
01:09:53,950 --> 01:09:59,440
we're spending $9,000
a year on patients

1266
01:09:59,440 --> 01:10:05,170
and we're getting a life span
of somewhere in the high 70s.

1267
01:10:05,170 --> 01:10:08,290
So this is 2015 data.

1268
01:10:08,290 --> 01:10:11,800
Whereas in Switzerland,
they're spending about $6,000--

1269
01:10:11,800 --> 01:10:17,800
so about 2/3-- and they're
getting 83 years or something

1270
01:10:17,800 --> 01:10:22,150
of life for the
same price, and so

1271
01:10:22,150 --> 01:10:24,610
on for all these
different countries.

1272
01:10:24,610 --> 01:10:27,880
By the way, this is
all from Gapminder,

1273
01:10:27,880 --> 01:10:31,023
which is a wonderful
data visualization tool.

1274
01:10:31,023 --> 01:10:32,440
And I don't have
time to show you,

1275
01:10:32,440 --> 01:10:34,990
but you can click
on individual lines

1276
01:10:34,990 --> 01:10:40,280
there and slide the slider about
what euro you're talking about,

1277
01:10:40,280 --> 01:10:41,800
and the data moves.

1278
01:10:41,800 --> 01:10:44,290
And it's beautiful, and
it's a wonderful way

1279
01:10:44,290 --> 01:10:46,640
of understanding it.

1280
01:10:46,640 --> 01:10:52,230
So there's an important
thing to remember,

1281
01:10:52,230 --> 01:10:54,750
which was taught to
me by my friend Chris

1282
01:10:54,750 --> 01:10:57,420
Dede at the Harvard
Education School.

1283
01:10:57,420 --> 01:11:00,480
And that is that it's not even
good enough to stand still.

1284
01:11:00,480 --> 01:11:04,620
So he suggested the
following scenario.

1285
01:11:04,620 --> 01:11:07,350
If you look at the
growth of productivity

1286
01:11:07,350 --> 01:11:12,240
over this period of
10 years by industry,

1287
01:11:12,240 --> 01:11:14,520
you discover that
productivity went up

1288
01:11:14,520 --> 01:11:18,990
by seven point something
percent for durable goods

1289
01:11:18,990 --> 01:11:23,420
and went down by something
like 2% for mining.

1290
01:11:23,420 --> 01:11:26,240
About 1% for construction.

1291
01:11:26,240 --> 01:11:31,440
Information technologies grew
by 5 and 1/2% or something.

1292
01:11:31,440 --> 01:11:34,430
So if you ask the
question, what happens

1293
01:11:34,430 --> 01:11:37,280
if the demand for
these goods remains

1294
01:11:37,280 --> 01:11:41,030
constant over a period of time?

1295
01:11:41,030 --> 01:11:45,710
And what you discover is that
because the more productive

1296
01:11:45,710 --> 01:11:48,590
things become
cheaper, they wind up

1297
01:11:48,590 --> 01:11:52,610
occupying a smaller fraction
of the total amount of money

1298
01:11:52,610 --> 01:11:55,370
that's being spent.

1299
01:11:55,370 --> 01:11:59,500
So your computer, your
laptop, is a lot cheaper today

1300
01:11:59,500 --> 01:12:02,210
than it was 30 years ago.

1301
01:12:02,210 --> 01:12:04,300
And so that means
that the amount

1302
01:12:04,300 --> 01:12:08,350
of spending that people do
on things like information

1303
01:12:08,350 --> 01:12:13,180
technology, at least
per item, is much lower

1304
01:12:13,180 --> 01:12:14,890
than it used to be.

1305
01:12:14,890 --> 01:12:18,160
And if in the aggregate it's
also lower than it used to be,

1306
01:12:18,160 --> 01:12:22,020
that means that something
else must be higher, right?

1307
01:12:22,020 --> 01:12:24,640
Because it sums to 100%.

1308
01:12:24,640 --> 01:12:27,780
And so what this
shows is that if you

1309
01:12:27,780 --> 01:12:31,830
spend 30 years at the same
rates of productivity growth,

1310
01:12:31,830 --> 01:12:37,080
mining grows from whatever
fraction of the economy

1311
01:12:37,080 --> 01:12:42,650
it was to something that's about
three times as big a fraction.

1312
01:12:42,650 --> 01:12:44,450
Right?

1313
01:12:44,450 --> 01:12:48,290
And if productivity
growth is better,

1314
01:12:48,290 --> 01:12:52,770
then that sector of
the economy shrinks.

1315
01:12:52,770 --> 01:12:55,680
So I think something
like this is

1316
01:12:55,680 --> 01:12:57,930
happening in health
care, which is

1317
01:12:57,930 --> 01:13:00,240
that there's infinite demand.

1318
01:13:00,240 --> 01:13:02,940
And health care is not
terribly productive.

1319
01:13:02,940 --> 01:13:06,210
We're not getting better
as fast as electronics

1320
01:13:06,210 --> 01:13:09,890
is getting better, for example.

1321
01:13:09,890 --> 01:13:13,250
So people have tried
doing various things.

1322
01:13:13,250 --> 01:13:18,038
"Managed care" was the
buzzword of the 1980s and '90s.

1323
01:13:18,038 --> 01:13:19,580
And they said, well,
what we're going

1324
01:13:19,580 --> 01:13:22,490
to do is to prevent
people from overusing

1325
01:13:22,490 --> 01:13:27,410
medical services by requiring
pre-admission review, continued

1326
01:13:27,410 --> 01:13:31,160
stay review, second
surgical opinion.

1327
01:13:31,160 --> 01:13:34,250
We're going to have
post-care management, where

1328
01:13:34,250 --> 01:13:36,140
if you're released
from the hospital

1329
01:13:36,140 --> 01:13:38,840
and you're in that
second of the cycles,

1330
01:13:38,840 --> 01:13:41,960
people will call you at home
and try to help you out and make

1331
01:13:41,960 --> 01:13:45,200
sure that you're doing whatever
is best to keep you out

1332
01:13:45,200 --> 01:13:46,770
of the hospital.

1333
01:13:46,770 --> 01:13:47,270
And

1334
01:13:47,270 --> 01:13:49,880
We're going to try
various experiments,

1335
01:13:49,880 --> 01:13:53,150
like institutional
arrangements, that say, well,

1336
01:13:53,150 --> 01:14:00,620
if I as a doctor agree to refer
all my patients to Mass General

1337
01:14:00,620 --> 01:14:05,360
rather than to the Brigham or
the BI, they'll pay me extra.

1338
01:14:05,360 --> 01:14:10,380
And they'll get some kind of
efficiencies from aggregation.

1339
01:14:10,380 --> 01:14:14,030
And so maybe that's one
way of controlling costs.

1340
01:14:14,030 --> 01:14:17,990
So leakage is the idea of
keeping people in-system.

1341
01:14:17,990 --> 01:14:20,270
And capitation is
an interesting idea,

1342
01:14:20,270 --> 01:14:23,000
which says that
instead of paying

1343
01:14:23,000 --> 01:14:25,760
for what the
hospital does for me

1344
01:14:25,760 --> 01:14:28,400
or what the doctor
does for me, we simply

1345
01:14:28,400 --> 01:14:34,290
pay him a flat fee for the
year to take care of me.

1346
01:14:34,290 --> 01:14:36,480
And that takes away
the incentive for him

1347
01:14:36,480 --> 01:14:39,572
to do more and more and
more to get paid more.

1348
01:14:39,572 --> 01:14:41,280
But of course, it
gives them an incentive

1349
01:14:41,280 --> 01:14:44,940
to do less and less and
less so that he doesn't

1350
01:14:44,940 --> 01:14:46,380
have to spend as much money.

1351
01:14:46,380 --> 01:14:48,450
And so it's sort
of a knife balance

1352
01:14:48,450 --> 01:14:50,250
to figure out how to do this.

1353
01:14:50,250 --> 01:14:53,170
But that's an
important component.

1354
01:14:53,170 --> 01:14:56,130
So if you look at the
evaluation of managed care

1355
01:14:56,130 --> 01:14:59,370
a long time ago,
what they said was it

1356
01:14:59,370 --> 01:15:05,100
helped reduce inpatient costs
by increasing outpatient costs.

1357
01:15:05,100 --> 01:15:07,200
So what it's done is
it's pushed people

1358
01:15:07,200 --> 01:15:11,220
from going to the hospital into
going to their doctor's office.

1359
01:15:11,220 --> 01:15:14,370
But it's been pretty much a wash
in terms of overall spending.

1360
01:15:17,680 --> 01:15:20,320
Doctors also hated managed care.

1361
01:15:20,320 --> 01:15:22,900
I was sitting with
one of my colleagues

1362
01:15:22,900 --> 01:15:26,830
at a Boston hospital, and
an insurance company clerk

1363
01:15:26,830 --> 01:15:32,500
called him to dun him for
having ordered a certain test

1364
01:15:32,500 --> 01:15:34,030
on a heart patient.

1365
01:15:34,030 --> 01:15:36,550
And so he was furious.

1366
01:15:36,550 --> 01:15:39,640
And he turns to her and
says, so which medical school

1367
01:15:39,640 --> 01:15:41,740
do you have your diploma from?

1368
01:15:41,740 --> 01:15:44,340
And of course, she doesn't
have a medical degree.

1369
01:15:44,340 --> 01:15:47,050
She's following some
rules on a sheet

1370
01:15:47,050 --> 01:15:51,730
about how to harass doctors
not to order expensive tests.

1371
01:15:51,730 --> 01:15:55,900
And so we have Edward Annis,
the past president of the AMA,

1372
01:15:55,900 --> 01:15:58,510
who says, well,
in the glory days

1373
01:15:58,510 --> 01:16:01,780
there was no bureaucratic
regimentation, no forms,

1374
01:16:01,780 --> 01:16:03,370
no blah, blah, blah.

1375
01:16:03,370 --> 01:16:05,980
And all my patients were
happy, and I was happy,

1376
01:16:05,980 --> 01:16:08,380
and things were ideal.

1377
01:16:08,380 --> 01:16:10,900
If you actually look
back at those days,

1378
01:16:10,900 --> 01:16:15,170
it wasn't as good as
the cracks it up to be.

1379
01:16:15,170 --> 01:16:17,230
And some of those
issues of disparity

1380
01:16:17,230 --> 01:16:19,720
that we were talking
about were horrible.

1381
01:16:19,720 --> 01:16:22,180
So for his patients
who were rich

1382
01:16:22,180 --> 01:16:26,500
and who could afford to see
him, life was pretty good.

1383
01:16:26,500 --> 01:16:31,840
But not so much for
underserved populations.

1384
01:16:31,840 --> 01:16:37,750
So you've seen ObamaCare come
and partly go, and continues

1385
01:16:37,750 --> 01:16:39,670
to be controversial.

1386
01:16:39,670 --> 01:16:46,450
But it's trying to foster
better information technology

1387
01:16:46,450 --> 01:16:51,080
as a basis for getting doctors
to make better decisions.

1388
01:16:51,080 --> 01:16:54,560
It's trying to foster these
accountable care organizations,

1389
01:16:54,560 --> 01:16:56,800
which is a version
of capitation,

1390
01:16:56,800 --> 01:17:00,790
to put the pressure on to reduce
the amount of health services

1391
01:17:00,790 --> 01:17:03,400
that people are asking for.

1392
01:17:03,400 --> 01:17:07,960
There is a hospital readmission
reduction program now

1393
01:17:07,960 --> 01:17:14,050
that says if you are a
Medicare patient, for example,

1394
01:17:14,050 --> 01:17:16,210
and you're discharged
from the hospital

1395
01:17:16,210 --> 01:17:21,090
and you're readmitted within
30 days of your discharge,

1396
01:17:21,090 --> 01:17:24,120
then they're going to
dun you and not pay you

1397
01:17:24,120 --> 01:17:25,920
for that readmission,
or not pay you

1398
01:17:25,920 --> 01:17:27,870
for part of that readmission.

1399
01:17:27,870 --> 01:17:31,530
But if you look at the
statistics, which I just did,

1400
01:17:31,530 --> 01:17:35,400
that's the distribution of the
payment adjustment factors.

1401
01:17:35,400 --> 01:17:39,066
So it turns out the
lowest number is 97%.

1402
01:17:39,066 --> 01:17:41,760
So a 3% decrease
in reimbursement

1403
01:17:41,760 --> 01:17:44,610
is something that the
CFO of your hospital

1404
01:17:44,610 --> 01:17:45,940
would really care about.

1405
01:17:45,940 --> 01:17:50,310
But it's not like a 25%
reduction in reimbursements.

1406
01:17:50,310 --> 01:17:53,235
So this has had a
fairly minor effect.

1407
01:17:56,780 --> 01:18:01,790
Let me just finish by saying
that money determines much.

1408
01:18:01,790 --> 01:18:05,210
From our point of view,
one of the problems we face

1409
01:18:05,210 --> 01:18:09,410
is that IT traditionally
gets about 1% to 2%

1410
01:18:09,410 --> 01:18:14,720
of spending in medical centers,
whereas it gets about 6% or 7%

1411
01:18:14,720 --> 01:18:19,850
in business overall and
about 10% to 12% for banking.

1412
01:18:19,850 --> 01:18:24,230
And a lot of these systems
are managed by accountants,

1413
01:18:24,230 --> 01:18:26,390
although that is
slowly changing.

1414
01:18:26,390 --> 01:18:31,010
So in the 1990s,
HST with Harvard

1415
01:18:31,010 --> 01:18:35,360
started a training program
for medical doctors

1416
01:18:35,360 --> 01:18:39,260
to become medical
informaticians-- so practicing

1417
01:18:39,260 --> 01:18:40,970
this sort of witchcraft.

1418
01:18:40,970 --> 01:18:43,820
And our first two
graduates, one of them

1419
01:18:43,820 --> 01:18:47,300
is now CIO at the
Beth Israel Deaconess.

1420
01:18:47,300 --> 01:18:50,330
The second one is CIO
at Children's Hospital.

1421
01:18:50,330 --> 01:18:53,540
And so one of my big
successes personally

1422
01:18:53,540 --> 01:18:56,210
has been to displace
some accountants

1423
01:18:56,210 --> 01:18:58,610
by doctors who
actually understand

1424
01:18:58,610 --> 01:19:01,580
this technology to some extent.

1425
01:19:01,580 --> 01:19:05,130
OK, I think that's
all I'm going to say.

1426
01:19:05,130 --> 01:19:08,780
There's a funny last
slide here, which

1427
01:19:08,780 --> 01:19:12,290
has a pointer that I would
want you to remember.

1428
01:19:12,290 --> 01:19:15,530
The slides will be
up on our website.

1429
01:19:15,530 --> 01:19:17,330
You can follow it.

1430
01:19:17,330 --> 01:19:19,460
MIT has a program called GEMS.

1431
01:19:19,460 --> 01:19:23,840
It's the General Education
in Medical Sciences, intended

1432
01:19:23,840 --> 01:19:27,170
as a minor program for
people in the PhD program

1433
01:19:27,170 --> 01:19:29,090
in some other field.

1434
01:19:29,090 --> 01:19:33,650
And if you're serious about
really concentrating on health

1435
01:19:33,650 --> 01:19:37,640
care, developing at least
the kind of understanding

1436
01:19:37,640 --> 01:19:41,250
of the health care process that
I've tried to give you today

1437
01:19:41,250 --> 01:19:44,690
and that would allow you to
play a doctor on television

1438
01:19:44,690 --> 01:19:46,340
is really important.

1439
01:19:46,340 --> 01:19:49,550
And there is a program that
helps you achieve that,

1440
01:19:49,550 --> 01:19:51,170
which I commend to people.

1441
01:19:51,170 --> 01:19:54,130
OK, see you next week.