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So welcome here on Karl's West Stage
on the very first talk of the morning
at 11, huh?
And I am very happy to
introduce to you last for A..
He was previously at
and they are specialists Quantico
and is now working for the
health think tank
of the German Ministry of Health.
And he will give you a talk about access
to health care and how to improve
life with health data.
Welcome, Lars.
Thank you very much.
Good morning, everybody.
Thanks for making it out so early.
It's my first Congress and I'm
a little overwhelmed with all the things
that are happening, especially at night.
So I really appreciate you all being here
so early.
My name is Lars.
I used to be a data scientist.
I'm a data scientist by training, and
now I switched to bureaucratic
bureaucracy to policy doing
a policy stint.
My life kind of looks like this right
now. I work in the Federal
Ministry of Health in a think tank
that is advising policy,
sort of bringing in new ideas from
the outside and informing
the rest of the world about what's going
on inside the ministry.
And I lead the efforts on artificial
intelligence.
Today, I'm here to talk to you about
what to do with health care data because
I think there is a lot of talent
missing in health care that technical
people that hackers have.
And I think there's a lot of knowledge
missing in the hacker community
about what to do with this kind of data.
And so I'm trying to address five points
today and sort
of get you acquainted
with some of the stuff that is happening
to get you started.
Does anyone here, a doctor, can you raise
your hand?
Excellent. Zero is the worst listeners.
This is a pathologist.
What pathologists do is not
what you see in crime scenes.
Well, pathologists do typically
look for cancer.
So whenever a doctor suspects
cancer in a patient,
at some point in their journey, they will
cut a piece of tissue from the patient.
They will send it to a pathologist.
A pathologist will harden that with
special chemicals.
We'll cut a thin slice of it and we'll
look at it under a microscope like that
one.
And they will basically look for cancer
cells in their tissue.
The whole process seems very
scientific. You know, it smells like
chemicals as microscopes involved.
It's fairly opaque.
Even the doctor is sending in the
original tissue. It doesn't normally
quite understand what's happening.
And in the end, this pathologist will
answer one question Is it cancer?
And if yes, how many?
And then sort of the doctor
that send the original tissue will work
with that information and
data people.
We're pretty familiar with the kind of
thing because it's a black box, right?
It's input data comes in,
then black box processing
happens here in the form of this
pathologist and then some output comes
back.
And so I thought it's pretty natural as
data people to ask, Hey, what is
the accuracy of this
black box algorithm and the format of
this pathologist?
Turns out that's kind of a wild
question in health care.
And I found the
results via the answer to the question
fairly surprising.
And I want to share it with you.
This is a somewhat extreme
example is very illustrative,
but there's no way out of the ordinary.
It's a study done at a German University
Hospital in Hamburg, where
they compare the diagnosis
that came in from the outside world.
Then they had an expert panel of several
high-ranking pathologists looking at the
same tissue and comparing
What did the experts say versus
what the original pathologists say?
So we're basically comparing this kind of
confusion matrix.
If you've heard that term before,
where we're comparing the original
prediction of a real pathologist
with the closest we can get to
ground truth, which is sort of a
consensus opinion of experts.
And the results I found were relatively
shocking.
Most medical diagnosis fall on a spectrum
from a sort of oh good to oh
god.
With this example, it's about prostate
cancer.
It's anything worse than, oh, good.
So if it's sort of on the right of a
good, then your
prostate will probably be removed to sort
of keep the cancer in check.
And in the event that you have a
prostate, about half of us, do
you kind of want to keep it?
The, you know, having your prostate
removed is not something that is a very
pleasant experience.
Yeah, it's a very
not nice surgery to have
leave a lot of damage in the body.
So sort of having a wrong
diagnosis here is important.
The first thing you see is that about
two thirds of all diagnoses the
green bars in the middle are exactly
correct, which means that
one third of diagnoses are actually
not exactly correct.
And you can say, Hey, you know, it
doesn't really matter.
But what does matter is
the cases from oh good
upwards, because that's where I have a
good surgery and not get surgery.
And what this data shows is that from
these. 5400 patients
out of those that actually by consensus
opinion did not have cancer, did
not have prostate cancer or did not have
an immediate need to treat one
third of those patients had their
prostate removed without the need
to do that.
So that's a, you know,
pretty drastic outcome for patients
that you would really like
to avoid.
Now the question that I ask for myself
is if this were the
result of an algorithm of
a machine learning algorithm or some
other algorithm, would this be acceptable
performance?
Maybe not, but for humans as best we
can do. But I think
being more transparent about
how humans are just humans
and maybe not particularly well suited to
look at pictures all day, I
think would be a good thing to have more
clarity in the health care system about
what diagnoses actually mean.
And so this is my starting example.
This is called in the medical community.
This is called intra inter observer
variability.
So different observers look at it
and their diagnoses vary
depending on who looks at it.
Intra observer variability.
So you show the same picture to the same
doctor at a different time of the day.
Also exists and is not much better.
The example I showed you is
very illustrative, but as I said,
this happens in a lot of medical fields,
not only for prostate cancer, but for all
kinds of cancer, for all kinds of
illnesses.
And again, it shouldn't be surprising.
Doctors are not bad people.
Doctors are just humans like us all.
And so mistakes happen.
And sort of it's one example
of this question that I think can be
answered with data and that currently
maybe not explored enough.
Another example would be who gets
treatment when?
So who has access to doctors?
How long do they have to wait for
appointments, that kind of thing?
How unhealthy are waiting rooms?
So people are waiting at the local
doctor's office to be seen
often for hours makes too many sick
people. How unhealthy is that really?
Does specialization into
more and more specialized doctors
practices actually improve quality of
care? Or does it maybe detract from it
because people lose oversight?
Can we predict the best course of
treatment for a given individual to
personalized medicine?
Or does the probability that a given
patient receive the hip surgery so
it gets in your hip?
Does that depend on the reimbursement
the doctor gets, i.e.
the price tag associated with that
surgery?
These are all questions that you would
like to answer with data.
And the problem in health care is what
I call the great irony of health care
data, which is roughly this sort
of the more legitimate use would be,
the harder it is for those actors to get
access to that data.
And you know, the Converse holds
as a case in point
in Germany in the year 2020,
most patients still do not have
some official plan
of their medication. What meds are they
taking? This is important because you
have these cross effects and side effects
of different medication, and when they
are hospitalized, you want to know what
they're taking.
And when you prescribe a new piece of
medication, you want to compare that
to what they're taking already.
The really active, really engaged
patients in Germany write these little
papers by hand where they write down
what has been prescribed
to them at some point and just carry
that piece of paper with them.
And then if they go to a hospital and
they get out of hospital again, they
would have to update that piece of paper.
Maybe they do. Maybe they don't.
It's just not a good system.
So as a doctor who is working with
patients, every time I see a new patient
have a really hard time finding out what
they're actually taking already and what
I can prescribe to them.
Then we have this phenomenon of consumer
electronics companies increasingly going
into the health data space
in a very smart way, and I think there's
a lot of good things happening here.
But basically declaring their
health care products as lifestyle
products, thereby
circumventing a lot of those pesky
health data data privacy problems are
saying this is not actually health data.
You know, lifestyle data
and getting more access.
And you can sort of think, you know, why
does Apple need to have health
data
at the same time? I think a lot of good
things are happening here.
Patients are becoming more and more
empowered to make their own decisions.
They're learning more and more. And I
think overall, it's a good thing.
The worrying part about this is
that traditional health care providers
might actually lose track of
or sort of lose the connection to the
consumer electronics competition.
And then finally, as we heard yesterday,
health data does get lost
and it's a terrible thing.
This is probably the worst story I've
ever heard about.
Singapore lost a whole database of HIV
people with HIV and
were extorted for it.
And you really don't want that to happen.
So what are the learning from the hacker
community and what are the things that I
want to talk about here?
First of all, I want to say very clearly
keep hacking.
You know, you don't wanna lose this data.
And I think having people like the
theater to say community
being white hat hackers
and making sure that people stay on their
toes is very, very important
for the safety and security of our health
care data because I don't think we will
go back to a world where this is all on
paper.
This will be digitalized and
we will need to make sure that it's safe.
The second part I want to talk about is a
little more subtle in
data privacy under GDPR.
You have this idea of consent so patients
can donate their data.
Patients can say you can use my data
to do something.
And this conversation currently,
especially in Germany but all over
Europe, is very much driven by this idea
between narrow consent and broad
consent, where narrow
consent is. This idea that
as a data subject, I couldn't
say very specifically what can and cannot
be done with my data.
And so I need to allow every single step
of processing my data, whereas
broad consent would say, You know, hey,
I can say my data can be used
for all kinds of cancer research.
And the issue that I see
is that the idea of narrow consent is
beautiful in theory, and this is a great
legal idea, but in
reality leads to situations where,
for example, a patient is being asked
to sign the data usage form
in the waiting room of a hospital or by a
study nurse.
I don't think that the idea of freedom we
had when we first said we want their
consent because you have this power
dynamic, the patient wants
treatment that is not a free choice
of what to do with their data.
And I think there's a tradeoff here
between narrow consent and being very
specific what you can do with data
and the user experience part of it of
how you can give that consent.
And I think being more
clear about the
chances we have, we have a broader
idea of consent. But having better user
experience is something that would be
extremely helpful in health data and in
health care.
Sort of similar to this idea of the
cookie banners you get everywhere where
having one switch per tracking cookie,
at least for me, doesn't really help me
much.
I want to say cookies, yes, cookies, no.
And a very simple one click solution.
And then finally, I want to
give you some ideas of legitimate
uses of things that you can do
with health care data.
It may be kind of an open data style
project that you can work on to answer,
for example, the questions that I started
with.
So to get you started
on something I thought I would tell you
about a few data sets that I think are
cool and allow some
hacking.
The first one are these two.
On the left, you see a famous
dataset for pathology.
So what we started with this is
what human
tissue when you sort of cut it and
dry it and cut into slices of
it and look at a microscope and color, it
looks like, is these pictures on
the left? You get that with a diagnosis,
and you can train computer vision
algorithms to hopefully
improve on the quality of diagnosis
that we currently have on
the other side. Just to show you another
example. These are pictures of
birthmarks.
And the question is, is that melanoma?
That's skin cancer or not?
Both of these datasets are relatively
well studied and great places to start
if you're into computer vision.
Then this is a bit of a German specific
thing because health care is organized
nationally and so different countries
will have different data sets, but these
are two data sets that I think are
very interesting and probably
underutilized.
The one is the so-called
dog browser where
you actually intended to download
software, which then allows you
to see certain aggregate statistics about
what billing codes are used for.
Procedures are used.
So it tells you a lot about what health
care is rendered and which numbers in
Germany.
So the example here is actually
birth and under
the billing code for birth.
What procedures were done?
So what specific procedures were
happening?
You can download that software online.
Turns out that all the data that that
software displays is just in case of
files and install directory.
You don't even need to install it.
But it's very interesting data to just
get your fingers dirty on.
And the other thing is the so-called
quality Boavista is something
that all hospitals have to publish,
and somehow, I don't think this has
gotten much attention than the open data
community yet
tells you a lot of data about what types
of procedures, what type of diagnoses
hospitals are seeing.
And then some. So I don't think this is
necessarily super helpful for their
quality metrics that they're publishing
there, but it tells you a lot about what
kind of patients hospitals are
seeing and if
you pool this with other data like census
data. I think there's a lot of
interesting things you can do here.
This is another class
example to sort of get you thinking a
little bit out of the ordinary on the
left, you can see that certain cities
have published the
availability of their emergency
rooms in different hospitals.
So basically for a given hospital in a
row per time of day, you can see
how much room they have in their E.R.
for specific diagnoses.
So you can say hospital aid
is getting flooded with gynecology
cases, but they're doing okay
for bone fractures.
And you can do all kinds of things like
try and build predictive models on this.
Maybe somebody wants to archive the data
somewhere.
I think there's a lot of interesting
things that you could do with this type
of data. Of course, originally it's
intended to be used for ambulances to see
which hospital to drive to.
And then finally on the right
is a very well-studied example
from the US.
Probably one of the richest data sets
that we currently have in health care
mimic is a data set of
patients in a hospital system on the East
Coast that contains
almost everything that happens in a
hospital for lab results.
Doctors notes
medication.
All of these things that in Germany
we don't really have access to are in
this dataset for research in the
identified manner.
This dataset is not public public,
but extremely easy to get access to
for another interesting case to
dig into and see what's possible.
And there are some privacy concerns, but
given that this dataset is widely
used in research already, there's a lot
of papers about it.
I think this is a relatively innocent
example to start with
now. With all health care
data, there is a lot of issues and I want
to talk about them briefly.
I see three main issues.
The one thing is ground truth
is often lacking. It's unclear what
ground truth actually is.
And if you remember the example I started
with, you know, you have in the original
doctor's diagnoses and now you have
a consensus diagnosis, is that
consensus necessarily better?
Why is it better if five doctors
agreed on it?
You know, maybe they were wrong in the
original doctor was right.
It's very hard to really find out what
the right diagnosis would have been.
And I think a lot of research is lacking
in that regard, and this sort of
carries over to a lot of different data
points where
it's extremely hard in health care to
just trust the data that you have
then. Second,
semantics are surprisingly difficult
in health care. What this means is,
you know, somewhere in a hospital based
or lab results, for example,
and there's just no standard, whether
they use milligrams per liter
or grams per cubic
centimeter, or there's all
these different options. And this one
example sounds trivial, but this is
all over the place.
How do you store a certain diagnosis?
Is it the flu or is it influenza?
Is it influenza A?
There's just many different ways to code
this stuff and having the
semantics, which is called sort of the
mapping of what people use
to what you can actually work with a data
person is still,
I would say, in its infancy.
And then finally, and this leads me to my
last two points.
There's a little sampling bias and
resulting issues of representativeness.
And I think sort of deep down,
this comes from the fact that the health
care data is personal data.
And so, you know, you don't you can't
just go and get access to all health care
data for all patients.
So often you have these examples
where some dataset surfaces
like the mimic datasets
and then that's all you have and you work
with that. And that leads to a lot
of representativeness problems in
medical research.
But I think in general, in a lot of
health care data projects, this is
one example.
A paper published in Nature
this year, which is very impressive.
They predicted some really cool things,
but the only data they had was from
the Veterans Association in the U.S., so
a separate hospital system for U.S.
veterans.
And as you can predict there, ninety four
percent male.
So learning algorithms
on data that is 94 percent
male sort of generalization
error will be an issue.
And you would like these algorithms.
You would probably like this algorithms
to work as well on women, as on men.
At the same time, what else are you going
to do? This is the only data that's
available, so you can't really blame
them, but it's just an issue that is
around in health care and that you need
to take into account.
There's one thing that this
also implies, which is it's
very hard to certify
that a certain medical device works
as intended because the
regulatory bodies also don't have
fully representative test data.
You're basically going to have to believe
the manufacturer or the vendor of a
medical device, such as an algorithm,
that they used appropriate
test data and that they use
representative studies.
Now, traditionally in pharmaceutical
research, you run these randomized
controlled trials, i.e.
you have a status quo, you have a new
drug. You sort of randomly
give you a new drug to 50 percent of
patients.
You use the status quo and the remaining
half, and then you compare the two
groups.
Still the gold standards in evidence,
but extremely hard to do this in a
representative way because you need to
recruit patients to voluntarily take part
in a trial.
It's expensive, and sometimes it might
not be possible because, for
example, you might be interested in how
cancer drugs work in children.
We think that running
medical trials in children is unethical.
So how are we ever going to get to a
point where we can learn about the
efficacy in children?
And I think a really cool opportunity
for evidence that we have with
algorithmic data and algorithmic,
the medical devices here is
that we can collect test data
as a regulatory body.
And so I think this is something that is
currently being discussed internationally
by the World Health Organization
on the European level with the new
commission and that we are doing a lot
of work on, which is this idea
that governments and regulatory bodies
should collect test data in a
representative manner.
So what would happen is on the top
row, you sort of see the normal workflow
here for deep learning, but it could
really be anything.
There's public data that's private data.
People train their algorithms on it, and
in the end they come up with a model on
the far right and then the
regulatory bodies say on the
European level would have a test
dataset that is actually secret.
That is sort of not to be shared with
anyone that has a high quality
standards.
And this can be achieved, for example, by
mandating that you collect this data
from hospitals all over Europe and you
just say hospitals have to submit, say,
one percent of the images that they have
into the secret body.
And now I have representative data that
I'm not sharing and so they can use this
test data.
And I also can keep doing this over
time so that I can account
for population shift.
So, for example, in skin cancer,
you could say that in Germany, the
average patient in the next 10 years
might have a slightly
darker skin tone than the average patient
in the last 10 years because
of migration patterns.
And so if you're working with algorithms,
you want to account for this population
shift and you want to make sure that the
algorithm that you're certified looking
back on test data from the last 10 years
will still work on the new patients of
the next 10 years.
And so this will be a way to keep
collecting this data and recertifying
algorithms.
Which finally leads me to my last point
before we open to questions.
I think what you have here is
just accuracy in the end, but this
doesn't really answer the question of
what fairness means.
And I mentioned earlier that,
you know, you want an algorithm that was
trained on 94 percent men
to work as well on women.
But what does aswell actually mean?
You know, is two percentage points
difference. Good enough is four percent
good enough.
Sort of not super clear from
the outset what fairness means
in these things.
And so I think this is a broader issue
that we should discuss as a community
beyond health care, which is
quantitative fairness, which is the idea
that more and more decisions
are made by algorithm and that of people.
Traditionally, when you look at
discrimination and fairness, you tried
to sort of empathize
with the actor for the
judgment. In the end, the judge would
say, did he mean to discriminate?
Or did he not mean to discriminate
with machines? That doesn't really work
anymore. And so we need new ideas to
describe what fairness looks like
and what discrimination looks like.
And I think approaches to
quantify the fairness of a decision
exist. The literature is relatively
mature. This is one overview,
but somehow in Europe, we don't really
have that debate yet, and I think it's
long overdue that we start having this
debate for health care and outside.
So my talk in a nutshell,
first of all, keep hacking.
Second of all, think about
what informed consent really means for
data.
First of all, do you contribute
legitimate uses?
The data it showed you the questions I
showed you are next are the starting
point.
Reach out if I can help with anything to
connect you with data or ideas
for demand evidence
for medical devices.
And I think trying to
seize the opportunity we have
to get better evidence is a really big
chance. And the fifth promote
quantitative fairness.
Thank you very much.
Thank you, Lars, for your talk.
So we actually do have
a lot of time for questions, we have
two microphones in the hall
and the Signal Andrews, so please line
up at the microphones and
we start off with the questions from the
internet. So please signal angel
one.
And you don't want us to know if
you and your employer live and
work is being
mentioned is involved in any
kind of work involved.
Checking this, there was a book by I
to go on the checklist
manifesto and he argues quite
convincingly about improving
certain procedures, reducing animals
into the chickens.
Do you know anything about that?
Yeah, I'm familiar with the literature
is not one of our main topics because we
focus on digitalization.
So the checklist idea is pretty, pretty
convincing. Actually, the idea was that
in aviation pilots,
we're at some point required to fill out
these checklists before takeoff and
during flight and say, you know, yes, I
did check this, I did check this, I did
check this and sign it, which pilots
hated because, you know, makes
you do things that you don't really want
to do.
They were forced to do it, and it
dramatically reduced incidences in
aviation.
And now Atul Gawande argues
that the same thing should hold for
doctors because doctors also
hate this idea that they're subjected to
processes by checklists
to reduce the number of incidences
that happen.
The idea is relatively old and I think
has been taken up in medical guidelines a
lot, but we
don't specifically work on that because
we focus on digital aspects.
OK, microphone, let's call it one.
How would you legislate or
decide what kind of
consent would be useful for medical data?
Currently, it is.
I mean, there is legislation around.
It basically comes down all the way from
GDPR done for German
legislative bodies.
Currently, health data is deemed
a special interest private data,
and so you need very specific narrow
consent.
And one option that you have
for health data in particular is that
GDPR actually allows for
exceptions to this idea
of consent, where you can say if a
important social interest stands against
sort of your private interest in keeping
your data personal,
you can say this data is available for
research use. So specifically for
research, this
is something that
can be done.
But I think, you know, because
it's so sensitive, you want to give
people the opportunity to maybe
still opt in, but in a different way,
maybe in a broader way or opt
out.
And I think that's why I'm talking about
broad consent because I think Americans
and currently is too narrow
and there would be ways to go broader
and that would significantly facilitate
access to this data for health care.
OK. Microphone two.
So you mentioned at the beginning this
patient that they had care,
so it was diagnosed with cancer,
but he didn't have cancer was
operated, but I don't
think that was the fault of the doctor
because this reduces to statistics.
You have the procedure called
trade off. Like if you want
only the patients that have truly cancer,
you have high precision, but you miss
a lot of patients that
have cancer.
So or if you want all
patients with cancer, you were to have
a lot of people that
don't have cancer as well.
And the same problem you also have with
machine learning. So you have the
precision required trade
off and this reduces
to actually two problems you need better
measuring devices or better devices
that reduces this erode
the variance and of course,
more data as well.
And surface not the problem
of the doctors
as statistics.
So first of all, I agree with you, it's
not. The problem of a doctor is they're
doing a fine job and doing the best job
they can.
But sort of as data
people, if you look at this naively,
it's kind of weird that you have humans
looking at pictures all day and are
expected to diagnose 100
percent correctly.
You know, all day, every day, they just
look at pictures.
That's not what humans are good at.
So it shouldn't be surprising that
mistakes happen, but it's not their
fault. It's just they're they're human.
The second part is, yeah, you're right,
there's precision and recall.
But what I'm arguing is that humans
have poor AUC
in their predictive qualities,
and we would want to have better
performance in this type of
algorithm
and help get people respond
to questions or discussion
is OK.
Is there a follow up question to
that? Yeah.
Where do you said then the threshold?
Do we all want more precision or
do we want more? You call so
you want?
Who are you? See?
OK?
Is there a question from the internet
that second engine?
Yes, Twitter wants to know.
Do you think that using algorithms
on the existing health they don't know
prevent gender bias diagnosis?
Or isn't there enough knowledge about
how diseases so any women
or any?
Sorry, could you repeat the second half
of the question, OK?
The reprieve, given that we already
know how certain diseases
show up in women differently than men,
do you think that these data and
algorithms can help discover
more things and change
how diagnosis are
provided or how accurate they
are?
Yes, absolutely.
I think first and foremost,
the medical practice
often does not yet
sort of live in the year 2020.
So I think a lot of gender bias exists
implicitly in how treatment still
is delivered.
And so first of all, I think using
existing health care data is helpful
to show these biases and
demand them to be reduced.
And then second of all,
I mean, I can't predict the future, but I
could imagine that you find all types of
understudied groups of patients
that maybe historically because they
didn't sign up to to be part of
our cities.
Randomized controlled trials
were understudied and you're going to
find gaps in the care that
these people receive.
And women are one example, but I think
ethnic minorities will be another.
Age groups will be another.
I would expect a lot of these type of
insights to surface.
OK. Microphone, one really short
question, you mentioned this German
quality assurance they deserve.
What does it contain, except
besides admission rates, does it contain
outcomes or risk
adjustment, etc.?
You mean the the the A-G dataset,
the the dataset actually only contains
aggregate data on
the total proceed.
So the total episode, so it was
a total episode billed as.
So there's a billing code and then there
is procedures linked to that.
Average data sets exist, some of them
private, some of them public access.
But I think if you want to have more
detail on all the admission data,
you're moving into a world of currently
private data or protected
research data because it's very hard to
anonymized this data.
And so there's always a risk of really
identifying it.
OK. So but for one again,
thanks.
Thanks for your talk.
I like the idea quite a lot with
building up representative data sets,
obviously.
But for me, the question in my mind,
what you are achieving is a
representative dataset, but not
necessarily also a high quality data set,
especially if you force this data
collection up on hospitals,
then you could end up with a really messy
data set.
What are your thoughts about this?
Very good point.
I think it's not enough to just collect
this data. I think you would also need to
actually invest in the quality of this
data so you wouldn't need to hire an
expert panel to
improve the data that you're collecting,
which means it's a very expensive overall
process.
You will not be able to do this for every
type of diagnose that you're interested
in, but only for the bigger fields
that are becoming more and more mature.
But one example I think
mammography is for breast cancer
screening is
relatively mature.
The technology there is several companies
going on the market now, and I think
they'll be one. The field where you're
starting to collect international
test data will be very feasible and
probably worth it.
OK, I'm looking at the I'm looking at the
signal injuries.
There are questions from the internet
that be the subject of, you
know, OK and microphone
two, please.
So looking at this from the perspective
of the individual whose data is supposed
to be of these datasets in the end,
is there any work being done or do you
have ideas on how to sort of
say soften the blow if your data
becomes public?
This could be either unintentionally,
for example, data leaks or
also if I just agreed to have my data
be part of a public dataset.
What kind of protections to have as a as
an individual in
that situation?
Because I think that that
is obviously one big reason why
you don't have access to sensitive data
because it is sensitive and this is
sensitive often for
reasons that are maybe
something you could address with policy.
Um, yes and no.
So first of all, I think what we're
actually lacking is
in Germany that $5m supporter.
So penalize protection
of that. Even if you have certain data,
you cannot use it.
So, you know, if certain types of data
fall into your lap or if you happen to
accidentally identify anonymized
data, you're obliged
to delete, delete that data,
which does two things it makes the data
less valuable on the market.
So hacking for them becomes less
interesting.
And second of all, those
activities that are thought of in good
faith will help protect that this
data doesn't, you know, move through
the world.
But I think that that
kind of falls short of really protecting
you.
I would say one part is
people tend to be a little too
scared. I personally think about
what can be done with their health care
data. So I would be pretty
careful with genetics data because we
know there's a lot of information in
there. We don't know what data is in your
genetics data. So yeah,
maybe not.
And there are certain stigmatized data
points like sexual health,
but or or psychological health
that maybe don't want to out in the open.
But besides that, I think,
yes, there are certain issues
these issues we can address with
legislation maybe better than we are.
So quantitative fairness can
also go to health conditions
and can say, you know, you cannot
discriminate against certain health
conditions as an insurance company
that would take a lot of fears away that
people currently have.
And then finally, I think the
idea my idea is
I'm willing to share my health care data
if you're also sharing your health.
A data, because I think in a world where
everybody's data is open, a lot of the
risks are already mitigated.
Surfing, being conscious about
how to do this
and maybe not starting with sexual health
data, not starting with psychological
health data would be a
way forward.
OK.
Are you queuing for the microphone?
What phone?
Yeah.
Hi.
My question is basically that
giving all the concerns that
people have with disclosure
of data being detrimental to
their personal privacy and issues,
are we already at that point where we
really need that considering health,
if there's a toxic substance?
A lot of like health issues are caused
by needing to
analyze data on how toxic stuff
would be in certain circumstances
in the public sphere.
Like, have we done all
data disclosure that is not private,
that is not linked to an individual that
is linked to male companies
having a business model that generates
health issues for the public?
And are we already disclosing
this data first before we harvest
my data?
Like, how does
this count end?
Because in Germany, if I
recall right, there was this issue of
coffee makers and the coffee
brewing, and they investigated
that the cleaning and the mining of this
led some toxic substances
left in the first brews of the coffee,
but they wouldn't disclose this data for
saving the companies
not having a bad business model.
So the question is, is my
private data the last resort here or
is there a head way and wriggle room
for improving our health
without going harvesting my
data first?
Why kind of why does it have to be either
or?
I think asking for more
aggregate data to be publicly released is
a good idea.
I showed you some available aggregate
data sets.
You can probably think about more.
But in the end, I mean, make no mistake,
these aggregate data sets come from
pooling individual people's data.
So you need to collect it at some point
to then be able to do the analysis
on it.
So I think even current ideas say,
you know, maybe you cannot actually, even
as a researcher, get access to one
individual patients data.
But you can run queries on
a lot of patients data. And since you
can't predict the queries that are
interesting, you need to collect
individual data at some point.
OK. Microphone to
you.
Uh, thank you for the talk.
But you mentioned mammography
as a good example for
using
computerized detection
and any
improvement.
So we had two waves
of automatic mammography
in the last 30 years.
The first one was a total mess because
the techniques were not safe
enough, good enough and
that they differ from side to side.
They used analog techniques and things
like that. And the next wave was
about 10 years ago where
we used automated data
sets and all the things that we had.
And
it ended in a total mess to
course in 2015, when we
did the first revision of this
idea.
We saw that the people
that used it, either in the US
or in the European hospitals,
had the problem that
there were too much false positive ideas.
So we had to more much
to too much recalls.
And I think we lost a
lot of trust.
And so how do we
how can we avoid this and the next
time we use it?
I think what you're saying is exactly why
I'm suggesting that we need
regulated test data
to avoid making these mistakes on
patients.
I think that I would personally think
that technology currently is relatively
mature and there
is several companies going on the market
again with these types of products.
And precisely because
in the past it didn't always work as
intended. You might be interested in
ascertaining as a regulator that
you have good test data.
Yeah. And I think the
re- improvement of the test data is a
crucial point, as you mentioned.
OK, microphone one, please.
And some important
health data we have in Germany are the
registers like the Cancer Register,
but they are organized on the federal
level and we
had a lot of problems getting them
running in a good way.
And now we have the
new possibilities of the electronic
patients file
coming up and also
when we will have a change
on our regulation with organ
transplant stuff, we are going
to have some sort of
organ register like
am I'm willing to donate or
not?
And are you thinking about
is this register going to be
organized on a national level or
on a federal level?
And do you think that
the future of the electronic
patient?
Yeah, the API type patient
file will change
the way the registries
like the Cancer Register are
organized. Do you think that there
will be some new interaction
and how we use this data?
Yeah.
Personally, I really hope so.
So maybe for those that are not familiar,
Germany, I think, like other countries,
have these registries where
we collect data on individual diagnoses
to be used for research.
So we say, hey, we have a lack of
understanding of a certain cancer type
of organ transplants.
And so we collect data specifically for
this purpose under a sort of a regulated
exception.
And yeah, absolutely.
I mean, in a world where we have
a national electronic medical record,
you would hope that this registry data
can be included there,
partially also because the electronic
medical record could offer consent
management where patients could
be told to give their consent
for certain uses and having
that on national infrastructure in a
secure environment would be very
desirable.
The second part of your question?
Who knows,
you know, who knows what's going to
happen with the national AI
trying to make medical records there?
They're not really available yet, and we
don't know how people use them.
But in an ideal world, I think they
could be used as a central
information storage and sharing
opportunity not only with doctors,
but also if the patient ones that
we have registries and with
researchers that answer your
question.
Yeah, thank you. Just not the organ thing
if you think this is going to be national
or not.
But that's what my field of expertize
unfortunately can tell you things.
OK, microphone one again.
I thank you for the talk.
I'm.
I attended the the presentation
yesterday about the EPA.
So the patient file, the case file
and what was
explained there is that they
don't want to take the efforts for
actually having the option
and opening the file
for certain doctors, just for like
a or certain parts of the own
file for doctors.
It's more about all or nothing.
What do you have to choose as a patient
first when they want to implement it?
And I'm considering that private
health companies already
implement systems
for data collecting with
a automated
system itemization for
the data, which they send to
research
centers. So to say, is there
any effort by now
from the German government
or certain
institutions to
follow such an idea of systemize
itemization to collect the data
and keep the individual data
with the doctors?
And if not,
is there a
or where's the best place,
where to start for
promoting such ideas?
So the idea of
pseudonym Sudan food on
amazing and anonymizing
health care data, of course, is as widely
spread and is well understood in the
government.
I think for the German patient records,
the national EMR,
that doesn't really well.
It could have work, but we decided to
actually have central storage and
guarantee privacy by encryption,
which has certain advantages
in particular, I think.
Me personally, I'm much more comfortable
with my data being in sort of
national infrastructure than
being in doctor's offices and seeing
the I.T.
security of the typical doctor's office
in Germany.
So I think there's a lot to be said about
having that in the national
infrastructure type place.
The second part,
I think the option.
So what you currently can do is
you can share all your data
with one doctor.
You cannot choose what parts of your data
in the current specification.
I think the criticism
has been heard and it will be possible
to share only selected parts
of your data.
But again, I think what what doctors are
saying on the other side of it, this
discussion is what
good is a subset
of a patient's data. The patient might
not know what parts of their
previous diagnosis I actually need for my
work. So I think the idea of withholding
data from doctors is very unpopular with
doctors.
And I think
it might be much more of an opt out
type situation where I don't want my
sexual health information shared
unless it's explicitly needed.
But everything else is OK.
Then, as an opt in situation where it's
used every single piece of data that I
have in my patient record.
OK, Mary,
concise question.
Yes. OK, so
what?
It doesn't
really answer for me the question
about the itemization, because I
think there's a lot,
um, interest of,
yeah, gathering the data um
sodomized to do
research. So why couldn't
be this a first step?
Um, because with
the new EPA coming,
if you have the access or if
you have signed up for this
electronic case
file, then let me interrupt you for a
second. Yeah. What you're saying?
Lawsuit anonymization is totally right
in the law that went into effect,
I think two weeks ago the fog,
the telephones, almost Corvette's.
We actually installed a sensor
to collect research data from
health insurance companies, and we
have actually written into law a
mechanism by which this data is
pseudonymous.
The sort of minimization is in
the law and it is being used
sort of from a data privacy standpoint
that is just the identifying that is not
anonymizing because using external
data, you can still really identify the
data.
And so it sort of it's
it's a hygiene factor.
But it doesn't solve all these privacy
issues that we have.
And so I think, yes, we're doing it.
But at the same time, you need consent
and you maybe need, in certain
circumstances, effective anonymization
techniques to really solve this bigger
issue.
OK. OK, so this concludes
our Q&A, and thanks
very much for the talk and for the
extensive Q&A round of applause
for him. Thank you.