PETER SZOLOVITS: As David said, I've
been at this for a long time.
I am not a medical doctor.
But I've probably learned enough medicine
to be able to play one on television over the years.
And actually, that's relevant to today's lecture
because today's lecture is really
trying to set the scene for you to say, well,
what are the kinds of problems that doctors
are interested in by looking at what is it that they do.
OK.
So that's our goal for today.
So what we're going to do today is
to talk about, in a very general way, what
are the goals of health care.
So how many of you are doctors?
A couple.
Great.
OK.
So fix me when I blow it.
All right.
Please feel free to interrupt.
So that's going to be my first task.
And then the second one is going to be
what are the things that people actually do in order
to try to achieve these goals.
What is the practice of medicine like?
What is the process that generates the data
that we're going to be using to learn from?
And then I can't resist talking a little bit
about paying for health care at the end of the lecture
because a lot of the problems that come up
and a lot of the interest that people show
in doing the kind of analysis we're talking about in fact
is motivated by money.
They want to be able to save money, or spend less
money, or something like that.
So it's important to know that.
OK.
Medicine's been around for a long time.
I think from probably the earliest of recorded history,
there are discussions of people wondering
what the cause of disease is, how to cure it.
They came up with some fairly cockamamie theories
because they didn't have a lot of scientific, modern
approaches to it.
But for example, this is a photo on the left
of a shaman I think from a Canadian Indian tribe who's
working on the boy lying there who's sick.
And this shaman would use his knowledge of experience
that he'd had with other patients.
They did know a lot about medicinal plants.
They knew something about how to care for injuries and things
like that.
And so this was an effective form of health care.
Not much record keeping.
You don't see electronic medical records system in that scene.
On the right is a modern shaman practicing at one
of the New York area hospitals.
And so there are traditional cultures
in which that sort of hands on interaction with the healer
is considered a very important part
of the practice of medicine.
And if you listen to futurist doctors talking about what
medicine is likely to be like, they emphasize the fact
that the role of a healer is not just
to be a good automaton who figures out the right things
but is to persuade a patient to trust
them to do the things that he or she is suggesting
to the patient.
And there are a lot of placebo effects
that we know from lots and lots of experiments
that say that if you think you're going to get better,
you are going to get better, on average.
No guarantees.
OK.
Now modern medicine actually looks more like this.
So this is an intensive care unit.
And what you see is a patient who
has got all kinds of electrical leads, and tubes,
and things going into them and is surrounded
by tons and tons of equipment which
is monitoring him and perhaps keeping him alive.
And so this is the high tech medicine
that we think of as the contemporary version
of clinical care.
Well, you might say, OK, what does it mean to be healthy?
Right.
If the goal of medicine is to make people or keep
people healthy, what is health?
So we turn to the World Health Organization.
And they have this lovely, very comprehensive notion
of the definition of health.
A state of complete physical, mental, and social well-being,
not merely the absence of disease or infirmity.
And then they categorize.
This and they say, well, there's physical health,
there's mental health, and there's social health.
Social health is especially hard to measure.
And I'll come back to that in a little while.
So what's easiest to measure is how long people live.
And so we've had data on survival analysis
for a long time.
And this is kind of shocking.
If you look here, this lower curve is from around 1800.
And what it shows you is that if you lived in India around 1800,
your life expectancy was about 25 years.
It's not very good.
And if you lived in the richest countries, which in those days
were typically European, in Belgium,
your life expectancy was way up there at 40 years.
Right.
How many of you knew that?
OK.
Good.
I didn't until I started looking at this.
Now by 1950, which is not that long ago, it
was like 69 years ago, in Norway your expectation
was that you'd live into your early 70s,
in the US that you would live into your late 60s on average.
There was still a huge cliff where,
if you lived in Bhutan, or Somalia, or something,
you were still down around 30.
Today, well in 2012, we're doing a lot better.
And the thing that's striking is not only
that the people who were doing well have gotten better
but that a lot of the people who were doing very poorly
have also gotten better.
And so we're now up--
India, remember, was at 25 years of life expectancy
and now it's in the high 60s.
Now of course, these are averages.
And so individuals vary a lot.
But it's kind of interesting.
So if you look at the numbers, you
see that even on a shorter term, there are big changes.
So for example, if you're a male living
in Rwanda, which is among the worst places in terms of life
expectancy, your life expectancy,
if you were born today, is about 62 and 1/2 years.
Right.
If you were born in 2001, it was only 38 years.
Now what was going on in Rwanda in 2001?
Genocide.
Yeah.
They were killing each other.
So that's sort of an exceptional situation.
And that's gotten much better because they've
stopped killing each other as genocidal attacks
between the Hutu and Tutsi, I think, if I remember right.
What about South Africa?
What was going on in South Africa in 2001.
I'm not sure I heard you.
AUDIENCE: Failure to address the HIV crisis.
PETER SZOLOVITS: Yes.
The government at the time was claiming
that HIV was not the cause of AIDS
and therefore there was no point in controlling HIV infections
because AIDS was caused by something else.
Pretty crazy.
So that was terrible.
And they've gotten much better at it.
So that's what you tend to see in a lot of African countries.
And what you also see is that there has really been
improvement everywhere.
So in the US, we went from males expected
to live 74 years to almost 78 years, so about a four year
increase in life expectancy over a period of just 17 years.
You women, by the way, are going to outlive us men, on average.
There's some biological thing that seems to work that way.
OK.
So a typical way that people look
at the survival of a population is
to say, well, given a cohort of people
born at some instant zero, what fraction of them
are still alive after a certain period of time?
And what you see is that, of course,
2031 we haven't reached yet.
And so these are projections based
on sort of theoretical extrapolations of actual data.
But the older data is real.
And what you see is that from 1851 to, you know,
2011 let's say, these numbers have gone way up.
Now where have they gone up the most?
Well, it used to be that childhood mortality was
enormous.
And so if you look at 1851, by age 10 about 30% of children
had died.
And so we've gotten a lot better at stopping that
from happening.
People also look at curves like this.
So this is a distribution of death rates by age.
This happens to be for Japan a few years ago.
And again, females do better than males.
The gold curve in the middle is the average of the two.
And this is very typical of almost any country
that you look at.
The shape of this curve is pretty universal.
So what does this say?
It says that when you are born, there is a relatively high risk
that you're going to die.
So these are kids who have congenital abnormalities,
have prenatal problems, have all kinds of difficulties.
And they don't make it.
So there's a fairly high death rate at birth.
But once you make it to, I think, about two years old,
the death rate is down to about one in 10,000 per year.
Right.
And then it stays quite low until you become a teenager.
Now why might the death rate go up when you become a teenager?
Well, suicide is the extreme example of that.
But teenagers tend to be risk seeking
rather than risk averse.
You know, they start driving cars.
They go skiing, skydiving, whatever
it is that they're doing.
And they start dying.
But then if you make it to about 20,
then there is a relatively flat region where by then
you've developed enough sense to know what risks are worth
taking and which ones aren't.
And so it's relatively flat until about age 35 or 40
at which point it starts inexorably rising.
And of course, as you get older and older,
the probability that you're going to die in the next year
becomes higher and higher.
Right.
This is uncomfortable for somebody
with my amount of gray hair.
Now there is a peculiarity in Japan which people
puzzled over for a while.
And that is that weird dip up at age 106.
So first of all, that's a very small number of people
that that represents.
And it turned out that it was fraud.
So there were families who failed
to report the death of their ancient grandmother
or great-grandmother because they wanted
to continue collecting social security
payments from the government.
So that's an artifact.
OK.
Now this is a serious problem which
we're going to return to in a more technical way later
in the semester, which is this problem of disparities.
So if you look at, for example, the difference
between white and black female life expectancy
in the United States, you see that everybody's life
expectancy, as we've shown, is going up gradually
in this case from 1975 to 2015.
But there continues to be a gap between black and white females
and between black and white males
where black patients are more likely to die
or less likely to survive longer given the disparities that
exist socioeconomically.
Maybe medically.
We don't know exactly.
And then if you look at Hispanics,
however, they do pretty well.
So in 2015, you're actually a little bit
better off to be Hispanic, either male or female,
than you are to be either white or black.
But it's still worse to be black than to be white or Hispanic.
Right.
So these are the kinds of facts that
drive some of the issues in what we do in medical care.
Now what do people die of?
Well, about a quarter of them die of heart disease.
And a little over a fifth of them die of cancer.
This is USA data from 2014 so it's not completely up to date
but hasn't changed that much.
And then there's a decreasing number
of deaths from various other causes.
So heart disease, cancer, or chronic
lower respiratory disease.
So this is like COPD that's caused
by smoking, stuff like that.
Accidents account for about 5% of deaths.
Stroke, cerebral vascular events, Alzheimer's disease,
diabetes, influenza, pneumonia, kidney disease, suicide,
and then everything else is about another quarter.
OK.
Now take a look at those.
What kind of diseases are these, the biggies?
Well.
They're chronic.
Most of them are chronic.
They're also not infectious.
But except for influenza and pneumonia,
nothing else there is infectious as far as we know.
I mean, yeah.
That asterisk you should put after every statement
about current medicine.
So that's interesting, because if you wrote the same table
back in 1850, you would find that a lot of people
were dying of infections.
And they weren't typically living
long enough to develop these lovely chronic diseases
of the aging.
So there have been big changes there.
Now the other thing that's worth looking at
is, in addition to the reasons that people die,
they start getting sicker.
And they are getting sort of less value out of life
because they're developing all these other conditions.
So if you look at people over 65, about half of them
have some form of arthritis.
And about 40%-- yeah.
About 40% have hypertension.
By the way, if you have trouble with any
of the medicalese words, just interrupt.
Hypertension is high blood pressure.
Hearing impairment.
Me, I'm wearing a hearing aid on one side
because my ears are going bad.
Heart disease, about a quarter.
Orthostatic impairment-- that means people who wobble
because their sense of balance is not so good--
16%.
Cataracts, chronic sinusitis, visual impairment,
genitourinary problems, diabetes, et cetera.
So these are all growing.
Here's the list of the next 10.
And varicose veins, hernia, hemorrhoids, psoriasis,
hardening of the arteries, tinnitus, corns, calluses,
constipation, hay fever, and cerebral vascular problems.
All right.
So people develop these by the time they're over 65.
So one question we might ask is, well,
what is the quality of life?
So for example, a lot of the doctors
that I started working with in the 1970s
were great advocates of the application
of decision analysis decision theory
to making medical decisions.
And so the problem is how do you evaluate an outcome?
And they said, well, the way we evaluate an outcome is
we look at your longevity.
Obviously, the longer you live, the better typically.
But we also look at your quality of life during that time.
And we say that if you're confined to a wheelchair,
let's say, your quality of life might not
be as good as if you were able to run around,
or if you're suffering from chronic pain,
your quality of life might not be as good as
if you were pain free.
And so we came up with this model that says,
well, the value of your life is essentially an integral
from time zero to however long you're
going to live of a function Q that
says this is a measure of how good your quality of life
is at that particular point in time and then some discount
factor.
Right.
So what's the role of the discount factor?
Well, it's just like in economics.
If I offer you some horribly painful thing
today versus 10 years from now, which are you going to choose?
Most of us will say later.
So that's what the discount factor does.
Now who knows what the right discount rate is?
So in some of their work, they were doing crazy things
like taking the financial discount factors about bank
interest rates and things like that
and applying them to these health things
just because they didn't have any better numbers to do.
That seems a little suspicious.
But nevertheless, methodologically, it's
a way of doing it.
OK.
So how do you measure the quality of life?
Well, there is this notion of the activities of daily living.
So can you bathe and shower?
Can you brush your teeth and groom your hair?
Can you get dressed?
Can you go to the toilet, clean yourself up?
Are you able to walk, get in and out of bed,
get in and out of a chair?
Can you feed yourself?
And then there are a bunch of instrumental factors, things
like are you able to clean your house
and can you manage your money and so on.
So these are typically for older people.
But they are ways of trying to quantify that quality of life
by saying how many of these things are you able to do.
And there are a lot of federal regulations,
for example, that take advantage of quantification like this.
So if you're asking to be put on some sort of disability
where the government sends you a check to keep you alive,
you have to demonstrate that you are
at a certain point on the scale that's
derived from these capabilities in order
to justifiably get that.
So occupational therapy is one of the things
that people try to teach the elderly.
My parents died in their late 80s and my dad was 90.
And I remember when he would have some medical problem,
then he would be put into the clutches
of an occupational therapist who would try to make sure
that he was able to communicate, and get around, and not
fall for tricks where people wanted to get him to send all
his money to them, or meal preparation
and stuff like that.
So these are sort of the--
occupational is a funny term for it,
because this typically applies to people
who are retired so it's not really an occupation.
But it's the sort of things that you
need to do in order to be able to have a decent life.
Well, now there's an interesting valuation question.
So if you look at the top right model,
we actually don't have very good data on anything other
than mortality.
So mortality is who dies.
So the blue curve there is the curve
that you've seen before, which is, of a cohort,
how many people are still alive after a certain number
of years?
The red curve is a morbidity curve
which says how many of those people are still alive and have
no sort of problematic chronic diseases.
So they're not in constant pain.
And they're not immobilized.
And they're not unable to do the things that I just
listed on the previous slides.
And then disability is when you really
become incapable of taking care of yourself.
And it typically involves moving into an assisted living
facility, or a nursing home, or something like that, which
is kind of a nightmare for many people
and also very expensive for society.
So as I said, the blue curve there
is based on actual data for American females in 1980.
The red curve is a hypothetical curve
where I just assumed that the rate of developing a morbidity
is about twice the rate of dying.
And the green curve I assumed that the rate
of developing a disability is--
I can't remember-- I think three times as high as the rate
of dying, something like that.
So that's why those curves are lower.
And they look approximately right.
But we don't have good data on those.
Now the question that you have to ask
is, how do you want to change this?
So for example, suppose that we kept the same situation.
We reduced mortality to 20% of its actual rate.
But we kept the disability and morbidity rates the same
as they are on the top left.
So what would that do?
Well, what that would do is create a huge number
of people who are disabled because they're
going to live longer beyond the point
where they're able to function fully.
So this is-- yeah.
AUDIENCE: Can I just ask, why does green not just
mean healthy?
To me, it just seems--
PETER SZOLOVITS: Green just means healthy.
AUDIENCE: OK.
It does.
PETER SZOLOVITS: Yeah.
So beyond green is what I meant is the morbidity curve.
And beyond red is the disability curve.
OK.
I may have misspoken.
So green is healthy.
Red means suffering from some morbidity.
And blue means disabled.
So if we just extend life but we don't make it any better,
then that's not a very attractive picture.
So other possibilities are compression of morbidity.
So for example, if we reduce the rate at which people get sick
and die but we increase it--
we decrease it initially and then increase it
so that, on average, people live about the same length of time
that they now do, then we're going to have fewer people who
are suffering from morbidity or who
are disabled because you last doing well and then you die.
So this is the wonderful one horse shay
where everything falls apart at once, which, you know, frankly,
as somebody who's a little closer to the end
than you guys are, I wouldn't mind that kind of exit.
Right.
I don't want to be disabled for 20 years.
I'd rather live a long time healthy
and then drop dead at some point.
OK.
My dad used to say that he wanted to die
by being hit by a meteor.
Right.
He wouldn't know it's coming.
It's instant.
No suffering, no pain.
Perfect.
He almost got it but not quite.
OK.
And then the final story is lifespan extension,
which is where we simply lower the mortality rate and all
the other rates in proportion.
And what happens is that you start
having healthy 107-year-olds and not so healthy
120-year-olds in the population in larger numbers
than we do now.
OK.
Social quality of life.
That's a tough one.
All right.
So here's a naive theory.
We take the quality of life of every person on the planet
and we sum them up.
And we say, OK, that's the social quality of life.
Is that a good idea?
Probably not.
Right.
For one thing, it would say that we
ought to have as much population explosion as possible,
because then we'd have more people to integrate over,
which doesn't seem sensible.
Right.
Now obviously if we start packing the world so that it's
super crowded, then the quality of life
will go down eventually enough that adding more people
is probably not optimal.
But nevertheless, that doesn't seem like a real satisfying
solution.
How about less?
It was popular about 10 years ago
for people to write these sort of speculative books
about how would the world look if half the people died.
Right.
Other than the trauma of the half
the people dying, you know, they were
proposing that this would be some wonderful sylvan
sort of ideal old fashioned kind of world.
I didn't buy that.
And of course, we don't know of a good way
to get there, although it is true
that in at least developed countries,
birth rates keep falling to the point where populations are
worried about underpopulation.
The Japanese, for example, have very strict immigration
policies so you can't become Japanese
if you're not born Japanese.
And Japanese people aren't having enough kids
to replace themselves.
And so the natural population of Japan is falling.
Italy is in the same quandary except that Italy
has all these immigrants coming in and trying
to become Italians.
And of course, this leads to big political fights about who gets
to be an Italian and who doesn't.
And then, of course, there's the question
of money, which as I say we'll return to later.
Now one other important thing to consider
is that because of the increase in life expectancy,
there has been a big change in timescale in the way
people think about medical care.
So it used to be long, long ago in the shaman era,
you wouldn't go to a shaman to say keep me healthy.
You would go to a shaman saying, you know, I broke my arm,
or I have this pain in my leg, or fix me somehow.
And so things were focused on the notion of cure.
And that was applicable to acute illnesses.
But as we've gotten better at treating
acute illnesses-- which, by the way,
didn't happen all that long ago.
I mean antibiotics were only invented
in the early 20th century.
And that made a huge difference in stopping people
from dying of infections.
So then it became more a matter of managing long term
chronic illnesses.
And that's pretty much where we are now.
The medical world at the moment, most of the action
is in trying to understand things like diabetes,
and heart disease, and cancer, and things
like that that develop over a long time.
And they don't kill you instantly
like infectious diseases did.
But they produce a real burden.
And then, of course, the next step that everybody expects
is, well, how do we prevent disease?
So how can we change your exposures?
How can we change your motivation?
How can we change your diet?
How can we change whatever it is that we need to change?
How can we change your genes to prevent you
from developing these diseases in the first place?
And that's sort of the future.
OK.
So that's about what medicine tries to do.
But how does it do it?
And so we're going to talk a little bit
about the traditional tasks that are attributed to health care
practice.
So traditionally, people talk about diagnosis, prognosis,
and therapy.
So diagnosis-- I go to my doctor.
I say, doc, I've got this horrible headache.
I've had it for two weeks.
What's wrong with me?
And his job-- in my case, it happens to be a "he."
His job is to come up with an answer.
What's wrong with me?
Prognosis is he's supposed to predict
what's going to happen to me, at least if he doesn't
do anything.
So is this headache going to go away
or is it going to turn out to be a brain tumor that
will kill me, or is it going to be some amoeba that's living
in my brain and eat my neurons?
All kinds of horrible things are possible.
So it's the prospect of recovery as anticipated
from the usual course of disease or peculiarities of the case.
And then therapy, of course, is what do you do about it.
And prognosis is definitely informed by diagnosis,
because if you don't know what's wrong with me,
then it's much harder to predict what's going to happen to me.
And if you can't predict what's going to happen to me,
then it's much harder to figure out
what to do to prevent that from happening
or to encourage that to happen, right?
So this is kind of a serial process.
And the way I look at it is that there's
a kind of cyclic process of care.
And the process starts with an initial presentation.
So I show up at my doctor's office
and I complain about something.
And if you ever listen to a doctor interacting
with a patient, the first time the patient comes in,
the first question is always, what brought you here?
Right?
And that's called the presenting complaint.
So if I say, you know, my ankle hurts
like hell, that's very different than if I say,
I stopped being able to hear in my right ear,
or I have this horrible skin rash on my arm, or whatever.
And that's going to take me in very different directions.
So then what happens is that the doctor examines you
and generates a bunch of data.
So these are measurements.
And of course, it used to be that those measurements were
based on observation.
So there are very famous doctors from 100 years ago
who were spectacularly good at being able to look at a patient
and figure out what's wrong with them
by being very astute observers--
the Sherlock Holmes kind of subtle,
oh, I see that you have a cut on the inside of your shoe,
which means you must have been going through brambles,
and you know, whatever.
I'm making up a Sherlock Holmes story here.
So that generates data.
And then we interpret that data to generate
some kind of information or interpreted data
about the patient.
And based on that, we determine a diagnosis.
Now, do we determine a diagnosis?
Maybe not.
Maybe we guess a diagnosis.
One of the things I learned early on working in this field
is that doctors are actually quite willing to make guesses,
because it's so useful to believe that you understand
what's going on.
If you say, well, there's some probability distribution
over a vast number of possible things, that
doesn't give you very good guidance on what to do next.
Whereas if you can say, oh, I think
this patient is developing type 2 diabetes,
then you're locked into a set of questions
and a set of approaches that you might try.
Now, when we come back to looking at machine learning,
machines don't have the same limitations as people.
And so for a machine to integrate over a vast number of
possibilities is not difficult. But for a human cognition to do
that is very hard.
And so this is actually an important characteristic
of the way doctors think about diagnostic reasoning.
So then, having made a diagnosis or made a guess,
they plan some kind of therapy.
They apply that therapy, and then they wait awhile
and they see what happened.
So if your diagnosis led you to a choice of therapy,
you gave that therapy to the patient and the patient
got better, then you say, well, it must
have been the right diagnosis.
If the patient didn't get better,
then you say, well, how did what happened to the patient
differ from what I expected to happen to the patient?
And that drives your revision of this whole process.
So we again examine what happened
as a result of the therapy.
We gather more data.
We interpret it.
We come up with the revised diagnostic hypothesis.
We come up with a revised therapeutic plan,
and we keep going around the cycle.
Now, that cycle happens very quickly
if you're a hospitalized patient, because you're there
all the time.
You're available.
They're trying to do things to you constantly.
And so this cycle happens on the order of hours or a day,
whereas if you were an outpatient, then
you're not dealing with some urgent problem.
It may happen over a much slower period.
It may be that your doctor says, well,
we're going to adjust your drug dose
and see if that helps bring down your cholesterol,
or manage your pain, or whatever it is that he's trying to do.
Or worse yet, we're going to try to convince
you to eat more healthy food, and six months later we'll
see if your hemoglobin A1C came down, that you're
less close to being diabetic.
So the time scale is very different.
But that process of continually reinterpreting things
is a really critical feature, I think, of all of medical care.
And if you look back, Alan Turing actually
talked, in the early 1950s, about health care
as being one of the interesting application areas
of artificial intelligence.
Why?
Well, because it was an important topic.
And he had the vision that says, as we
start getting more data about health care,
we're going to be able to build the kinds of models
that we're going to be talking about in this class.
But a lot of the early work took a kind of one-shot approach.
So they said, well, we're going to solve
the diagnostic problem.
So we're going to take a snapshot of a patient, all
their data at a particular moment.
We're going to feed it into an algorithm.
It'll come up with a diagnosis.
We're done.
And that wasn't very useful, because it
didn't obey the cyclic nature of the process of providing health
care.
And so this was a revolution that started, really,
around the 1980s when people realized that you have
to be in it for the long-run and not for sort
of single interactions.
OK, well, this is just some definitions
of these care processes.
So here I've listed some ideas that came from a 1976 paper
by several of my colleagues, who said, well,
here's a cognitive theory of diagnosis.
From the initial complaints, guess a suitable hypothesis.
Use the current active hypotheses
to guide questioning--
so to order more tests, to ask questions of the patient.
And it's the failure to satisfy expectations
that's the strongest clue to how to develop a better hypothesis.
And then the hypotheses could be in an activated,
deactivated, confirmed, or rejected state.
They actually built a computer program
that implemented this theory of diagnostic reasoning.
And these rules, essentially, about whether to activate,
deactivate, confirm, or reject something
could be based both on logical criteria
and on a kind of very bad probabilistic model.
So it was very bad, because what they really
needed was Bayesian networks.
And those were about a decade in the future at that point.
So they and every other system built in the 1970s
had really horrible probabilistic models,
because we didn't understand how to do it correctly.
Now, what's interesting is somebody noticed
that if you strip away the medicine from this,
this is kind of like the scientific method, right?
If you're trying to understand something,
you form a hypothesis.
You perform an experiment.
If the experiment is consistent with your expectations,
then you go on and you've gotten a little bit more confident
in your hypothesis.
If your experiment is inconsistent
with your expectations, then you have to change your theory,
change your hypothesis.
You go back and gather more data, and then keep doing this
until you're satisfied that you've come up
with an adequate theory.
So this was a surprise to doctors,
because they thought of themselves more
as artists than as scientists.
But in a way, they act like scientists,
which is kind of cool.
All right, this doesn't stop with caring
for a single patient.
So we have all these meta-level processes about the acquisition
and the application of knowledge about education,
quality control and process improvement, cost containment,
and developing references.
So this is a picture from David Margulies,
who was chief information officer at Children's Hospital
here.
And the cycle that I described is the one right here.
This is the care team taking care of a patient.
But of course at some point, that patient is discharged.
And then they're in community care and self-care,
and then maybe they're in some sort of active health status
management.
And then if that goes badly, then there's
some episode where they reconnect to the health care
system.
They get authorized to come back.
They get scheduled for a visit, and they're back in the cycle.
And so the process of care involves
people going to the hospital, getting taken
care of, they get better, they get discharged,
they live their lives for a while.
Maybe they get sick again.
They come back.
And so there's this larger cycle around that issue.
And then around that are all kinds of things about health
plan design and membership and what coverage
you have and so on.
And then I would add one more idea, which
is that if you have a system like this,
you actually want to study, at the next meta-level,
that system, make observations about it,
analyze it, model it, plan some improvements,
and then intervene in the system and observe how it's working
and try to make it better.
So in terms of tasks that are important for us in this class,
this class of tasks is central, because one
of the things we're trying to do is
to look at the way health care works
and to figure out how to make it better
by examining its operation.
And that can be done at any of these three levels.
It can be done in the more acute phase, where
we're dealing with somebody who's
in the middle of an illness.
It can be done at the larger phase of somebody who's
going through the cycle of being well for a while
and then being sick, and then being well again and being sick
again.
And it can be in terms of the system itself,
of how do you design a health care system that works better
for the population?
So this notion of a learning health care system
is now a journal.
So in 2017, Chuck Friedman at the University of Michigan
started this new journal.
And it's full of articles about this third level
of external cycle.
So how does the health care system learn?
Well, I'll tell you an anecdote.
In the mid-1980s, I was teaching an AI expert systems course.
And I had just come back from a conference
of medical informatics people, where
they were talking about this great new idea called
evidence-based medicine.
And I remember mentioning this to a bunch of MIT engineering
students.
And one of them raised his hand and said, as opposed to what?
Right?
I mean, to an engineer, it's obvious
that evidence is the basis on which you analyze
things and make things better.
But it wasn't obvious to doctors.
And so this was almost a revolutionary change.
And the idea that they fostered was
the idea of the randomized controlled clinical trial.
So I'm going to sketch what that looks like.
Of course, there are many variations.
But suppose that I'm one of the drug companies around Kendall
Square, and I come up with a new drug
and I want to prove that it's more effective for condition
X than some existing drug B.
So what do I do?
I find some patients who are suffering from X
and I try very hard to find patients who are not
suffering from anything else.
I want the purest case possible.
I then go to my statisticians and I
say, let's design an experiment where
we're going to collect a standard set of data
about all these patients.
And then we're going to give some of Drug A and some of them
Drug B, and we'll see which of them do better.
And we'll pre-define what we mean by do better.
So like, not dying is considered doing better,
or not suffering some bad thing that people are suffering from
is considered doing better.
And then the statisticians also will
tell me, given that you expect Drug A to be,
let's say, 10% better than Drug B,
how many patients do you have to enroll in this trial
in order to be likely to get a statistically significant
answer to that question?
And then they do it.
The statisticians analyze the data.
Hopefully you've gotten p less than 0.05.
You go to the Food and Drug Administration
and say, give me permission to market this drug as the hottest
new cure for something or other, and then you
make billions of dollars.
Right?
This is the standard way that pharma works.
Now, there are some problems.
So most of the cases to which the results
of a trial like this are applied wouldn't have
qualified to be in the trial.
For example, we talked about morbidities, about
the chronic problems that people have.
Well, if you're dealing with one disease,
you want to make sure that those populations you're dealing with
don't have any of these other diseases.
But in the real world, people do.
And so we've never actually measured
what happens to those people if you give them this drug
and they have these comorbidities.
The other problem is that the drug company
wants to start making these billions of dollars
as soon as possible.
And so they want the trial to be as short as possible.
And they want it to be as small a sample as they
need to get that 0.05 statistical significance.
So these are all problematic, and they lead to real problems.
So I didn't bring any examples, but there
are plenty of stories where the FDA has approved
some drug on this basis, and then
later they discovered that although the drug works well
in the short-term, it has horrible side
effects in the long-term, or that it has interactions
with other diseases where it doesn't work effectively
for people except for these pure cases that it was tested on.
So the other idea, the competing idea is to say, let's
build this learning health care system in which progress
in science, informatics--
whatever-- generates new knowledge
as an ongoing natural byproduct of the care experience
and we seamlessly refine and deliver best practices
for continuous improvement in health and health care.
Wonderful words from the Institute of Medicine,
now called the National Academy of Medicine.
But it's hard to do this.
And the reason it's hard to do this
is mainly for a very profound underlying reason,
which is that people are not treated
by experimental protocols.
So it's very important in that sketch
that I gave you of the Drug A versus Drug B
that there is a randomization step where I flip a coin
to decide which drug any particular individual is going
to get.
If I allow that decision to be biased by my expectations
or by something else I know about the patient,
then I'm no longer doing a fair trial.
And of course, when I collect data
about how actual patients are being treated,
they're being treated according to what their doctors think
is best for them, and so there is no randomization.
I mean, if I went to Mass General and said, could you
guys please treat everybody randomly
so that we collect really good data,
they would throw me out properly.
OK, so we also need a whole lot of technical infrastructure.
We need to capture all kinds of novel data sources, which we'll
talk about in the next lecture.
And then we need a technical infrastructure
for truly big data.
So just for example, Dana Farber started about five years ago--
it's a cancer hospital.
And so for every solid tumor, they
would take samples of the tumor and genotype it--
multiple samples, because tumors are not uniform.
So just storing that stuff is a technical challenge,
and being able to come up with it.
You've got three gigabases, so about over a gigabyte of data--
from each sample, from each tumor,
times all the people who come in and have this test.
So you buy some big disk drives or you farm
it out to Google or something.
But then you need to organize it in some way
so that it's usefully easy to find that data.
So today's technique, today's prejudice
is what I call the meat grinder story, which
is you take medical records, genetic data,
environmental data, data from wearables,
you put them into an old-fashioned meat grinder,
and out come bits, which you store on a disk.
And then you have all the data from which
you can build models.
And that's what we do.
You're going to see a lot of that in this course.
OK, the other thing that medicine tries to do
is not to cure people but to keep them healthy.
And this has been pretty much the domain of the public health
infrastructure.
So if you go across the river to the Harvard Medical area,
there are a couple of big buildings, which
is the Harvard School of Public Health,
and this is what they're all about.
So they do things like tracking disease prevalence
and tracking infections, and worrying
about quarantining people.
They also do a lot of the kind of work
we're going to talk about in this class, which is modeling
in order to try to understand what's going on in individual's
health, in the health of a population,
in the operations of a health care system.
So they're very much into this.
Now historically, I looked back and it turns out
there's something called the London Bills of Mortality
in the 17th century, started by a gentleman named John Graunt.
And he was interested just in figuring out,
how long do people live?
And so he came up with these bills of mortality,
where he went around to different parts of London,
talked to undertakers and hospitals
and whatever health care providers existed at the time,
and collected data on what people died
and how many people were living in that area.
And so for example, he estimated that the mortality before age
six in the 17th century-- this was a long time ago, 1600s--
was about 36%.
So if you were a kid, your chances of making it to age six
were only about 64%, so less than 2/3.
Kind of shocking.
In the 18th century, people you've never heard
of-- and Linnaeus, who you probably have heard
of, because he was one of the early taxonomists
for biological and animal species and so on--
made the first attempts at systemic classification.
In the mid-1850s, mid-1800s, there
was a Congress of the First International Statistical
Congress.
And a gentleman named William Farr came up
with an interesting categorization that said, well,
if we're going to taxonomize the diseases,
we should separate epidemic diseases
from constitutional diseases from local diseases,
whereby "local" he means affecting
a particular part of the body from developmental diseases.
So this is things like stunted growth
or failure of mental development or speech development,
and then diseases that are the direct result of violence.
So this is things like broken bones and bar fight results
and stuff like that.
So that was the first classification
of disease in about 1853.
Note, by the way, that this is before Louis Pasteur
and his theory of the germ cause of disease.
And so this was a pretty early attempt,
and obviously could have benefited from what
Pasteur later discovered.
So by the 1890s, which is post-Pasteur,
they came up with a classification
that was a hierarchic classification, 44
top-level hierarchies broken up into 99 lower-level categories
and 161 particular titles.
And they adopted this as a way of getting,
typically, mortality data of what was it
that people were dying of.
And by the 1920s, you've heard of ICD--
ICD-9, ICD-10.
So this is currently used as a way of classifying diseases
and disorders.
The International List of the Causes of Death
was the first ICD back in, I think, the 1920s.
And then it kept developing through multiple versions.
In 1975, ICD-9 was adopted.
In 2015, ICD-10.
And these are under the control of the World Health
Organization, which is now a UN agency, although I think
it predates the UN actually.
And then we have ICD-9 CM and ICD-10 CM,
are US Clinical Modifications that
are an extension of the ICD-9 and 10 coding.
And they are primarily used for billing.
But they're also used for epidemiological research.
And if you look at the Centers for Disease Control, CDC,
they collect death certificates like this
from all over the country.
And so this is a person who died of a cerebral hemorrhage which
was due to nephritis, which was due to cirrhosis of the liver.
And so you can use this kind of data to say,
well, here's the immediate cause of death,
here's sort of the proximate cause of death,
and here's the underlying cause of death.
And so this is the sort of statistical data
that we now have available.
Now, do any of you watch the PBS show Victoria?
Nobody?
You're not television watchers.
All right, that's pretty cool.
I was stunned, because as I was preparing this lecture
and I had the next slide, it turns out
this plays a role in one of the episodes
that was broadcast about a week and a half ago.
So in the 1850s, there was a big outbreak of cholera in London.
And John Snow was a doctor who did
this amazing epidemiological study to try to figure out
what was causing cholera.
The accepted opinion was that cholera was caused by miasma.
What's a miasma?
STUDENT: Bad air.
PETER SZOLOVITS: Bad air, OK?
So it's bad air.
Somehow, bad air was causing people to get sick and die.
And several hundred people died.
Whereas Snow started plotting on a map of the area in London
where these were concentrated where everybody lived.
And what he discovered, interestingly enough,
is that right in the middle of Broad Street,
which is pretty much at the epicenter of all these people
dying, was a water pump that everybody in the neighborhood
used.
And that water pump, its supply had become infected by cholera.
And so people were pumping water,
taking it home, drinking it, and then dying,
or at least getting very sick.
And he looked at this and he said, well,
if we turn off that pump, the epidemic will stop.
And he actually went to the queen, Queen Victoria--
hence the tie-in to the television show--
and convinced her that this was worth trying, because they
didn't have any better ideas.
And they took the pump handle off the pump,
and sure enough the cholera epidemic abated.
Now, of course the underlying problem was sanitation.
And they didn't fix that.
That took longer.
But what's interesting is here is
a 2003 study of the spread of West Nile virus.
So this is mosquitoes that are biting people and infecting
them with this nasty disease.
And they actually used very similar techniques
to figure out that maybe this was coming in
on airplanes through JFK.
So mosquitoes were hitching a ride on an airplane
and coming into the US.
We need to build a border wall against mosquitoes.
There is also a very controversial practice
that used to be used a lot by public health officials, which
was to quarantine people.
And so there are lots of examples.
Anybody's relatives come through Ellis Island?
Must be a few.
OK, so they were subject to quarantine.
If you were sick when you arrived at Ellis Island
and they didn't know exactly how sick you were,
they would put you in a building and wait a month
and see if you got better or if you got worse,
and then decide whether to send you back or to let you in.
So that was a pretty common practice.
There's a famous story of Typhoid Mary,
who was a carrier of typhoid fever
but was not herself affected.
Unfortunately, she was a cook.
And wherever she was employed, people got really sick.
And eventually, the New York Department of Health
forcibly essentially jailed her in some sanatorium
in order to keep her from continuing
to infect people-- it was a very controversial case, as you
might imagine.
You don't have to go that far back.
Here's a 1987 article from UPI from The Chicago Tribune.
So Jesse Helms, who was a senator,
was calling for everybody who has AIDS to be quarantined.
So fortunately, that didn't happen.
But the idea is still around, to say, well,
we're going to stop this infection by quarantining
people.
And then here's a recent report about the Ebola response
in Africa over the past few years,
when Ebola was ravaging parts of that continent.
And their conclusions are it's controversial,
a debated issue, significant risks related to human rights.
Quarantine should be used as a last resort.
Quarantines in urban areas are really hard.
Mobile populations make it hard.
And this is the most technical conclusion,
that if you're going to quarantine a bunch of people,
you have a huge waste disposal problem on your hands.
Because if you have people who might have Ebola,
you can't just take their garbage
and throw it out somewhere.
You have to dispose of it properly.
OK, so the last thing I wanted to talk about--
hopefully mercifully short-- will be paying for health care.
So I remember reading about 20 years ago
that if you bought a Chevy from General Motors,
they spent more money on health insurance and health
care for their employees than they did on the steel that
went into your car.
That's pretty amazing.
So why is this?
Well, essentially, there's an insatiable demand
for health care, right?
Nobody wants to die, except for the suicidal people.
And so if I'm sick, I want the best care possible,
and I want as much of it as possible.
Because you know, what is more important in life
than continuing to live?
So we also have gotten better at making drugs and better tests,
and so on.
I remember one MRI machines became
popular about 30 years ago.
The state of Massachusetts, for example,
had a commission that you had to convince
them to be allowed to buy an MRI machine for your hospital,
because MRI machines were very expensive
and MRIs were, at that time, hugely expensive.
And so they wanted to contain the cost of health care
by limiting the number of such machines.
Well, eventually the costs came down.
And so we're doing better.
But if you read the newspapers, you
see that drug therapy is very expensive.
We have these wonder drugs for rare diseases or cancers
that cost $1 million a year to pay for your dosage.
And so there is a high human motivation
to do this, and not much pushback--
except from insurance companies.
But they just pass the cost on in insurance contracts.
There's also waste.
So there are lots of stories about half
of health care expenses are spent
in the last year of somebody's life.
Although, Ingelfinger, who was the editor of The New England
Journal, gave a talk here about 25 years ago.
And he said, you know, when I was a practicing doctor,
no patient ever came into my office saying, doc,
I'm in the last year of my life.
And so that was a difficult criterion
to try to operationalize.
There are marginally useful procedures.
The IOM estimated that there are sort
of 40,000 to 100,000 quote unquote unnecessary deaths
per year--
in other words, deaths that could
be avoided by just being more careful and a little bit
smarter.
Well, so the result of this is that if you look at health care
spending as a percentage of gross domestic product
from 1970 to I think 2017, I believe, on this graph,
what you see is one real outlier.
And that's the United States on top.
So I've selected a few of these just to look at.
There's the US on top.
France, Germany, a lot of the European countries
are roughly at that highest of the crowd level.
Canada is down there.
The UK is a little lower.
Spain is a little lower.
Israel is a little lower.
Turkey is the lowest of the OECD countries
in terms of percentage of GDP that they spend on health care.
So well, that's OK.
But maybe we're getting more for our money
than these other countries.
And so there are a lot of analyses
that look at stuff like this.
They say, well, if we spend so much
money per patient per year, what do
we get in terms of the simplest thing
to measure, which is life expectancy?
How long do people live?
And what we discover is that in the United States,
we're spending $9,000 a year on patients
and we're getting a life span of somewhere in the high 70s.
So this is 2015 data.
Whereas in Switzerland, they're spending about $6,000--
so about 2/3-- and they're getting 83 years or something
of life for the same price, and so
on for all these different countries.
By the way, this is all from Gapminder,
which is a wonderful data visualization tool.
And I don't have time to show you,
but you can click on individual lines
there and slide the slider about what euro you're talking about,
and the data moves.
And it's beautiful, and it's a wonderful way
of understanding it.
So there's an important thing to remember,
which was taught to me by my friend Chris
Dede at the Harvard Education School.
And that is that it's not even good enough to stand still.
So he suggested the following scenario.
If you look at the growth of productivity
over this period of 10 years by industry,
you discover that productivity went up
by seven point something percent for durable goods
and went down by something like 2% for mining.
About 1% for construction.
Information technologies grew by 5 and 1/2% or something.
So if you ask the question, what happens
if the demand for these goods remains
constant over a period of time?
And what you discover is that because the more productive
things become cheaper, they wind up
occupying a smaller fraction of the total amount of money
that's being spent.
So your computer, your laptop, is a lot cheaper today
than it was 30 years ago.
And so that means that the amount
of spending that people do on things like information
technology, at least per item, is much lower
than it used to be.
And if in the aggregate it's also lower than it used to be,
that means that something else must be higher, right?
Because it sums to 100%.
And so what this shows is that if you
spend 30 years at the same rates of productivity growth,
mining grows from whatever fraction of the economy
it was to something that's about three times as big a fraction.
Right?
And if productivity growth is better,
then that sector of the economy shrinks.
So I think something like this is
happening in health care, which is
that there's infinite demand.
And health care is not terribly productive.
We're not getting better as fast as electronics
is getting better, for example.
So people have tried doing various things.
"Managed care" was the buzzword of the 1980s and '90s.
And they said, well, what we're going
to do is to prevent people from overusing
medical services by requiring pre-admission review, continued
stay review, second surgical opinion.
We're going to have post-care management, where
if you're released from the hospital
and you're in that second of the cycles,
people will call you at home and try to help you out and make
sure that you're doing whatever is best to keep you out
of the hospital.
And
We're going to try various experiments,
like institutional arrangements, that say, well,
if I as a doctor agree to refer all my patients to Mass General
rather than to the Brigham or the BI, they'll pay me extra.
And they'll get some kind of efficiencies from aggregation.
And so maybe that's one way of controlling costs.
So leakage is the idea of keeping people in-system.
And capitation is an interesting idea,
which says that instead of paying
for what the hospital does for me
or what the doctor does for me, we simply
pay him a flat fee for the year to take care of me.
And that takes away the incentive for him
to do more and more and more to get paid more.
But of course, it gives them an incentive
to do less and less and less so that he doesn't
have to spend as much money.
And so it's sort of a knife balance
to figure out how to do this.
But that's an important component.
So if you look at the evaluation of managed care
a long time ago, what they said was it
helped reduce inpatient costs by increasing outpatient costs.
So what it's done is it's pushed people
from going to the hospital into going to their doctor's office.
But it's been pretty much a wash in terms of overall spending.
Doctors also hated managed care.
I was sitting with one of my colleagues
at a Boston hospital, and an insurance company clerk
called him to dun him for having ordered a certain test
on a heart patient.
And so he was furious.
And he turns to her and says, so which medical school
do you have your diploma from?
And of course, she doesn't have a medical degree.
She's following some rules on a sheet
about how to harass doctors not to order expensive tests.
And so we have Edward Annis, the past president of the AMA,
who says, well, in the glory days
there was no bureaucratic regimentation, no forms,
no blah, blah, blah.
And all my patients were happy, and I was happy,
and things were ideal.
If you actually look back at those days,
it wasn't as good as the cracks it up to be.
And some of those issues of disparity
that we were talking about were horrible.
So for his patients who were rich
and who could afford to see him, life was pretty good.
But not so much for underserved populations.
So you've seen ObamaCare come and partly go, and continues
to be controversial.
But it's trying to foster better information technology
as a basis for getting doctors to make better decisions.
It's trying to foster these accountable care organizations,
which is a version of capitation,
to put the pressure on to reduce the amount of health services
that people are asking for.
There is a hospital readmission reduction program now
that says if you are a Medicare patient, for example,
and you're discharged from the hospital
and you're readmitted within 30 days of your discharge,
then they're going to dun you and not pay you
for that readmission, or not pay you
for part of that readmission.
But if you look at the statistics, which I just did,
that's the distribution of the payment adjustment factors.
So it turns out the lowest number is 97%.
So a 3% decrease in reimbursement
is something that the CFO of your hospital
would really care about.
But it's not like a 25% reduction in reimbursements.
So this has had a fairly minor effect.
Let me just finish by saying that money determines much.
From our point of view, one of the problems we face
is that IT traditionally gets about 1% to 2%
of spending in medical centers, whereas it gets about 6% or 7%
in business overall and about 10% to 12% for banking.
And a lot of these systems are managed by accountants,
although that is slowly changing.
So in the 1990s, HST with Harvard
started a training program for medical doctors
to become medical informaticians-- so practicing
this sort of witchcraft.
And our first two graduates, one of them
is now CIO at the Beth Israel Deaconess.
The second one is CIO at Children's Hospital.
And so one of my big successes personally
has been to displace some accountants
by doctors who actually understand
this technology to some extent.
OK, I think that's all I'm going to say.
There's a funny last slide here, which
has a pointer that I would want you to remember.
The slides will be up on our website.
You can follow it.
MIT has a program called GEMS.
It's the General Education in Medical Sciences, intended
as a minor program for people in the PhD program
in some other field.
And if you're serious about really concentrating on health
care, developing at least the kind of understanding
of the health care process that I've tried to give you today
and that would allow you to play a doctor on television
is really important.
And there is a program that helps you achieve that,
which I commend to people.
OK, see you next week.