-: Good afternoon everybody.
I'm Elizabeth Heitman from the program in science,
ethics in science and medicine
and I'm very pleased to welcome you all
to today's ethics grand rounds,
and welcome back to Texas our speaker
who is without question one of the most creative
and prolific members of our field,
an excellent educator and probably literally
the most prolific person I know
in terms of writing both publications
for lay audience and for ethics audiences,
and most recently, as of last week, a new textbook
on health humanities research, research methods
in the health humanities.
Dr. Craig Klugman, I said welcome him back to Texas,
got his professional start in academic bioethics
at the Institute for Medical Humanities at UTMB
where he did his PhD.
His dissertation research was conducted in part
in the parking lot of the Walmart in Galveston
where he interviewed people and surveyed people
on their experiences with family members
end-of-life care and what it meant to them.
He graduated from Galveston and went
to the University of Nevada at Reno
where he was the first in foundational ethics
faculty member in the medical school there.
And then came back to Texas at
UT San Antonio Health Science Center
where he worked in the Center for Medical Humanities
and Ethics, becoming a tenured chaired professor
and working extensively on integrating medical humanities
and ethics into the curriculum there.
He was recruited to DePaul University in Chicago
where he is today, the professor and former chair
of the Department of Health Sciences.
Craig, as you will see, is an extraordinarily
good speaker who if the mic were to go down
and the lights were to go off, would still
be able to keep us entertained for the hour.
And I'm very pleased to welcome him today
to talk about the ethics of smart pills
and the dawn of surveillance medicine.
You will want to hold on to your Coke can
and any stray skin cells you may leave behind
at your seat, as he will tell you,
it's not really yours anyway.
Craig, thank you very much.
(audience applauding)
-: So, what Dr. Heitman was referring to
was a program that I gave last year
on families, familial surveillance of DNA.
I was giving this before members of the,
it was a international society
and at the end I walked out with three water bottles
'cause I didn't trust anybody not
to take my DNA off of them at that point.
If you are paranoid, that doesn't mean it's not true.
So thank you.
That's a very high level of entertainment value
I have to provide today.
So, this project comes out of work that I did
consulting with a pharmaceutical company,
which I no longer am doing.
But it started looking at the issue
how technology is being used as medicine now.
In some places to augment or replace
the healthcare provider.
And in some places to augment or replace medication.
When we think about the different types and ways
technology are becoming part of medical care,
there's a lot of different things
that have been appearing.
And you'll see that these are all sort of clippings
from the news in the last few months.
So Google has a project called Deep Mind.
It's an artificial intelligence.
And it turns out it is better at detecting eye disease
than any human being.
It can clearly find 50 different eye conditions
and is more effective than any human being has been.
This one, we can put scans, radiographic scans
of lungs through this computer system
and it is better at detecting lung cancer tumors
because it can differentiate finer than the human eye,
and point that out.
This one is very new.
The FDA has now approved a
clinical decision support software.
This is an artificial intelligence system
that makes recommendations that you feed in information
about your patients and it tells you
who is at a likelihood of having a stroke.
And it is apparently very, very effective and accurate.
If you wanna know if a flu outbreak is coming,
and it is October so it's flu shot month,
but if you wanna know when the outbreak will come
to your area, check Twitter.
It turns out that Twitter analytics can predict
flu outbreaks within six weeks
of it actually happening as it watches the way,
what people are posting and the terminology
they're using and where they're going.
If, back on the eyes, so it turns out
there is now, the FDA is permitting a new AI device,
works very similarly to your cellphone
and it can detect changes in the eye
for diabetes patients before it would normally be seen.
If you are having in vitro fertilization or ART,
sometimes you get more than one embryo.
How do you know which one to implant?
Well sometimes you're looking for preventing a disease
or preventing certain conditions.
But now we can scan them with a camera lens
that an AI is on the end at, and it'll tell you
which is the best quality, which ones have the
best likelihood of making it through gestation to birth.
Do you wanna know why it's hard
to talk to patients about death?
You don't have to anymore.
There's now an artificial intelligence system
that will do that for you.
So this one is two brothers.
One was a technology person, one was a palliative care.
And they used linguistics to analyze conversations
the doctors have had with their patients
to come up with ideal models for how to do
end-of-life care.
And so they can do this for anybody.
So we're using what's called artificial intelligence
which are not self-aware programs
but programs that have done self learning.
So you feed them in a whole bunch of data,
and they come up with the rules themselves
about how you determine the start points
and the end points.
So we are far beyond the possibility where
we are creating if-then statements.
If you see this, then that.
We don't do that.
We feed data and we let the machines learn for themselves.
The future is here.
Apparently my disclosure statement's here.
So I work with that pharmaceutical company.
Oh, it's all going backwards.
There we go.
Okay, I'm just gonna use the keyboard.
Wow, okay.
What all this is about is an area
we're calling digital medicine.
And digital medicine is when we are combining technology
with information technology, artificial intelligence,
big data, implantable devices, wearable devices
in order to improve patient outcomes,
in order to treat our patients.
So this is a whole area that we're calling digital medicine.
One of the projects I worked on was the digital pill.
This is a product known as MyCite Abilify.
And it is about $1,200 more than the pill
without the technology enhancement.
What it is is a pill that's a superencapsulated pill
that has Abilify and a sensor.
And the sensor is made by Proteus Corporation.
Both of these had separate FDA approval
and they were combined.
So it works like this.
I need my props.
Okay, so you take this pill and this is cool.
It's really cool stuff.
The battery for your pill is stomach acid.
I'm telling you, this stuff is really cool.
So you swallow this pill.
The stomach acid acts like battery acid
and the sensor sends out a signal.
You wear a patch.
It just looks like a large bandaid
and the patch records the signal.
And then when it gets near your phone,
it links through Bluetooth and it records
that you took your pill, time of day.
The phone prompts you, do you wanna put in your mood
or how well you slept last night.
This then uploads to the cloud and that information
then goes to a portal that your physician
has access to.
And you can designate up to five other people
to also have access to your portal.
So you take a pill and everybody knows about it.
Now you have to give permission.
There's a consent process.
You can turn it off at any time.
You can just stop taking the pill at any time,
take the patch off.
But the idea is to figure out adherence.
When the pill isn't working, is it because
the pill's not working or because the patient
isn't adhering to the regimen?
And so this answers that question.
But it's complicated.
There's a lot of ethical issues that come up with this.
And we'll be talking about those.
All right, big data.
This is huge.
I think I saw a banner outside talking about
being part of big data studies.
I do clinical ethics at Northwestern Memorial Hospital
and we are home to some huge big data initiatives.
We're trying to get a million people
to let us record them.
So there have been some interesting studies
using big data.
Often abusive ones in my opinion.
So this was a piece in the New York Times
about a year ago.
In the city of New York, there were three hospital systems,
major hospital systems, who did the same study.
They took their medical records, not research records,
medical records and they loaded them up
into an artificial intelligence program,
and wanted to see what diseases would run in families.
That was part of it.
But they anonymized the data.
They stripped the names out.
They stripped out patient numbers.
So they reconstructed family trees
and how they did this is there's something
that we all have on our medical records that links us,
our emergency contact.
Name, phone number, email, relationship.
So anonymous meant they took your name, address,
phone number, patient number off,
but they didn't remove the emergency contact.
And so they could reconstruct family trees.
They also all own bio banks.
And so what they could see is are there diseases
that run through families, and then they could match
to bio bank specimens and see if there was
a genetic component to these diseases.
Huge data.
Now three different institutions or systems.
They did not share medical records
between one another 'cause they didn't wanna
give up ownership of their data
and ownership of data is a key point
when we're talking about digital medicine.
And to preserve the confidentiality
of their patients, these are not subjects.
There was no consent process.
There was no notice in the newspapers.
There was nothing.
To preserve their confidentiality,
if they found a disease that ran in your family,
they didn't contact you, 'cause that
preserved your confidentiality.
So myself and a few other people had some issues
and concerns about this.
And you're welcome to read my blog on it
or the New York Times article.
So this is what big data's looking like.
Digital phenotyping.
These devices have a lot of information about us.
But in phenotyping it's not about the information on them,
it's about how you use them.
So if you type at a particular speed
and suddenly that speed changes,
it may be a sign of Parkinson's.
It's turning out that by changing your pattern
of use, that these devices can detect depression,
suicidal ideation, Parkinson's disease
and we're not sure what else.
So as you'll see from some of these clippings,
we can also look at your voice patterns.
When you are speaking on the phone,
and I know my students I have to go
and yes, you can actually talk on these.
They don't realize that.
So just from your voice patterns,
they can tell if you're depressed.
The words you use, how long it takes you
to say phrases and words.
There are patterns that can be picked up.
Let's see, the Parkinson's piece is there as well.
So if you tend to use this hourly
and suddenly you're putting it down
for a number of hours, they're gonna notice
that pattern change.
Over time that gives information that could be valuable.
So phenotyping is simply using this.
You can think of this as the genotype.
And the phenotype is how you use it.
And so this was a cartoon that I put together.
If you go to sleep and you're used
to using your phone a lot, it might think you're dead
and call 911 for you, which is a capacity
that it has.
I was talking to somebody the other day
and they have a fall sensor on theirs.
So if there's a sudden drop of the phone,
it calls 911 for them.
And apparently this was an older person
who did have a bad fall and in fact, it did call 911
and it probably saved their life.
Big brother's not only watching,
he's watching you all the time.
These are in weird order.
Okay, so how does this all work?
So this is what we're doing.
But it's interesting to take a step back
and see how this is happening.
So we have to think about this as a system
and these are all different components.
So we have devices.
It might be a defibrillator.
It might be an insulin pump.
It might be your Fit Bit or your Apple Watch.
Then we have the way we interact with these systems
through our computers, through our phones, our tablets.
But none of the data is there.
We also have the data structures.
And so we've gotta think about this as a system.
All right, so there are three types,
for the FDA there are three types
of these medical device categories.
So the first is mobile apps.
So there are apps now for mental illness,
for losing weight, for your activity tracking,
apps that claim all sorts of things.
That's a type one.
A type two is something that has limited body contact.
So something that touches your body
but can be removed.
It's not permanent.
So if you have a monitor that you put on,
that is one.
Your Fit Bit, your Apple watch, those are all things
that can be removed.
And then type three are things
that are connected into the body.
So your pacemaker, your insulin pump, your defibrillator,
electric shock pain therapy, those sorts of things.
So it gets incorporated inside the body.
That's a level three.
So this is how these things get classified
for FDA purposes.
These are really in weird order, okay.
So the portal again is your phone or your computer
or your tablet.
And what this is doing is it's receiving the signal
from the device.
Remember the patch sends a signal to the device.
The device is an app and the app I can see my information.
I can enter who can see, who else can see my information.
I can enter additional stuff such as my mood,
my sleep patterns.
But this then sends data to the servers.
So all the data is kept on server farms,
a lot of them owned by Amazon.
But they're on the server farms.
And this is just how I interact with,
between my device and the server.
But this is just the communication device.
There's actually nothing on here.
The data structure is where all the information is held.
We commonly call this the cloud.
The largest cloud provider in the United States is Amazon.
And what is on the cloud then is the data,
the information, that you took the pill,
the time that you entered your information about this.
All the medical records that are being used
for the big data programs are on there, the AI.
Then we also have the algorithms.
So you have all the raw data, which is like the hard drive
but what do you do with that?
It's just data.
Then you have algorithms.
And in artificial intelligence is a very advanced
self program algorithm.
And that is what's using the data
and processing and making sense out of it
and spitting back information.
So the processing, the data, all of the stuff
that's actually happening is on a server
in a farm somewhere.
And if you do Google Maps and you wanna know
where these data servers are, they consume
enormous amounts of energy and produce a ton of heat.
So if you look for really hot spots
on a Google Map search, that's probably a a data farm.
Either that or somebody's fracking, one of the two.
So this is where everything's happening.
Now this is important when you think about
how we deal, regulate these things.
So we've got the medical device or just the device.
We've go the intermediary, the portal
and then we've got the data structure.
So 95% of medical devices are never reviewed by the FDA.
If a medical device is similar enough
to something that already exists on the market,
it does not have to be reviewed.
When you think about pacemakers, don't change all that much.
What they do is they get smaller
and they're not internet-enabled.
So they can speak to the internet.
They can speak to the portals.
That's what's new is the technology, the linking.
But these devices haven't really changed all that much.
And so when a new one comes out, it doesn't go
under an FDA review.
They just simply say, well you know, pacemakers exist.
This is similar to ones that are already
on the market and that's it.
That's all that has to happen.
When it comes to the portals, the mobile and the laptops,
there is very limited FDA review.
What they wanna see is the human factors testing.
And human factors testing is how well
can a person use this without making mistakes?
Can they enter the app?
Can they log on?
Can they access their information?
It is how usable it is from a very practical standpoint.
It's not saying, you know, it doesn't have
to prove it works.
It doesn't have to prove it's better than anything else.
It doesn't have to prove it's safe.
It just has to prove that a person, an average person
can use it and use it correctly.
So that's the level of review that's going on
for the portals and the mobiles and laptops.
Behind the scenes, nothing.
The FDA does not get to look into the data
and does not get to look into the algorithms
or the AI.
We call this the black box.
It's a black box because this is all proprietary technology.
It is not patented because to patent something
you have to make it public.
And so we're not allowed to see how this is being used.
The FDA is not allowed to see how this is being used.
So it doesn't get reviewed.
If there is a problem, if the algorithm starts recommending
things, so IBM was in this space.
They were using their AI to look at
precision cancer treatment.
Great concept.
The problem became that what their project was suggesting
were, there were actually therapies
that were gonna kill the patients
if they were actually used.
So IBM sort of walked away from this space for a while.
But why was it making those predictions?
Why was it suggesting treatments that were
fatal to patients?
Nobody actually did it.
They didn't actually kill anybody by following it.
It was just when they looked at it.
Because it's a black box.
We can't go in and see why the algorithm
is making these decisions.
It's not available.
But in Europe they review the entire landscape.
In Europe you have to show your medical device,
the portal and the data structure.
They look at it all.
But in the US, which very recently
and I mean two weeks ago, came out with
that they wanted to look at, for artificial intelligence
systems only, they wanted to, they wanna do a review
of clinical decision support programs.
So not all AIs, but specific ones that are used
to help support the clinician in making decisions.
How this is going to actually happen, nobody knows.
This was released 10 days ago that they
even wanted to do this.
So it'll be an interesting landscape.
But what this shows is the regulations
and the law has not caught up to
what the technologies can do at this point in time.
Okay, so what does this all mean in a daily practice?
What does this all mean for the ethical aspects
of the practice of medicine and healthcare?
All right, the first thing to think about is autonomy
and informed consent.
We have a well-established notion of informed consent.
A patient comes in.
We wanna do a surgery or a detailed treatment with them.
They get a consent form or they sign a global consent form
and this lays out what are the risks,
what are the benefits, what are the alternatives,
what will be done to them.
These are documents that might be written
by the clinician or the department.
They probably are reviewed by an attorney but not always.
They're short.
They are written for the patient,
so they're written at a, not a high level of language
but a public, common level of language,
maybe fifth-grade level.
And they protect the patient.
They are there to protect the patient.
When you're dealing with intellectual property
and technology however, what rules is not informed consent
but the user agreement.
And a user agreement is a very long document.
It is always written by attorneys.
It does not exist to protect us, the end users.
It exists to protect the company, to make sure
that we understand the limitations of our license
to use the technology and that it can be taken away
at any time, but we don't actually own it.
The technology company owns it.
When you're using software, even if you go
and you buy a, I know this is gonna sound old fashioned,
a CD that has a program on it.
When you download that program, you do not own the program.
You have a license to use that program
and that license can be revoked at any time.
The documents exist to protect the companies.
The documents are also very long.
So there's a joke that the iTunes user agreement,
it's actually not a joke, it's true, is 56 pages long.
These are not short.
So how many people have opened an account,
gone to a website and it says you know, create a username,
create a password, check this box.
You've read the rules and the user agreements.
How many of you have gone through and read those
rules and user agreements, realistically?
Yeah, we don't because who has time to sit there
and try to understand dense legalese in 50s of the pages.
And the reality is if you don't agree to it,
you don't get to use the product.
So now we have a situation where we're using
technology as medicine.
In the world of medicine, we're used to informed consent.
In the world of technology, we have user agreements.
And now we have to have patients do both.
In the case of the pill, an informed consent
that they want this prescription.
They're gonna use this.
They have to get trained.
We have videos to train them.
But they, the first time they download this app to use it,
they pick the pill from the pharmacy.
They open the app and the app says,
do you agree to the user agreement?
So there's a difference in the cultures
and in the intent of these documents and these processes.
Consent's a process.
User agreement's one time and if they change it,
and you can change a user agreement at any time.
And if you disagree with the change,
then you can delete that product from your device.
That's your option.
So there are very different ways and perspectives.
And so now we've got a patient who's being given
a medical solution who has to sign both.
So that is a misunderstanding that,
potential place for misunderstanding.
You can also stop using these devices at any time.
You can delete the app.
You can stop taking the pill.
You can write, opt out of having your data used
in some cases, for research.
But what you can't do is tell them
to delete what they've already collected
'cause they own the data.
If you're like, okay I'm gonna stop using this device.
I don't want you to record its stuff,
you can go back and say, yeah, can you delete
my entire file?
Legally they don't have to because
under the terms of the user agreement they own the data.
It's theirs.
So in informed consent you have control over data
depending on the research subject
but it usually in medicine, but you don't have any control
over the information and once you stop using,
you don't have any control over what's happened
with what they've already collected.
Bias, so it turns out that our technology,
to put it bluntly, they're bigots.
They are extremely discriminatory and biased.
There was an AI that was put out by Google
onto Twitter, it was a Twitter bot.
And it could interact.
And it could learn from people.
And it would advance and would have conversations.
Google had to take this down within 24 hours
of putting it out.
Because what happened was some people were playing with it
and were feeding it incredibly racist, hateful things
and that became the bot.
And so it became racist and hateful.
And so it had to be removed.
And AI is only as good as the information we feed it.
And it turns out most of the information we have
is pretty biased to start with.
So as we can see here, one of the places this technology's
been used is in sentencing guidelines for the courts.
So there's, well it's in lots of courts.
The view was that judges are subjective
as human beings and they may give different sentences
to people depending on who they are,
whether they like them or not,
what they look like, things like that.
But if we had a computer doing it,
we would eliminate that bias.
Turns out the computer is pretty biased
because the information is really biased.
It's looking at things like, you know
your socioeconomic status.
It's looking at do you live near other people
who have committed crimes?
It's looking at is there more policing in your area?
And so the bot was giving sentences
that were harsher for people of color
than it was for people who weren't,
who were more Caucasian.
They're still using this.
This is still being sold.
Many courts still use this system.
It was also used for policing.
Where do we send our police forces?
Well, the system was looking, where are there more crimes,
that's where we send more police.
Well, when you send more police to a place
they tend to find more crime because they're looking for it.
So it creates this self-fulfilling prophecy.
This was, there is a facial recognition software
that is being used to identify people in crowds.
It's being used in the UK and China to a great extent.
Google had another little mishap
where they had an algorithm where you could upload
your picture and it would identify people.
It would automatically link them with their names.
It was very accurate for white men
who are most of the programmers
and most of the pictures on the internet.
But when you put up people of color,
it was terribly inaccurate.
It was putting up, it was matching not names,
it was saying unidentifiable or it was
matching people to animals or objects.
They had to take that down.
But it was the data fed in was biased
and that was part of the problem.
So we're like okay, maybe there's a way
you can deal with the data to make it more broad,
that we can have data that comes
from a larger cross section of society.
Turns out even if we're feeding in better data,
these algorithms, they evolve prejudices of their own.
Even if we control and we try and keep the bias out
of what we're feeding 'em, they just develop
their own presumptions about different groups and people.
So yeah, we have produced electronic bigots.
Part of this is because there's a lack of feedback
in the system.
So this is a great book, "Weapons of Math Destruction"
and it's basically the way we use algorithms
and math to reinforce stereotypes
and misunderstand them.
It's like you know, everybody says a P value of .05,
it's significant, no, it depends on a lot
of different things.
And in this book she goes over a lot
of these different case studies
and a lot of the problems that I've just mentioned.
This is, and some of these problems
aren't necessarily solvable.
So the feedback mechanism is a big deal.
If you are in the clinic and you prescribe something
to a patient, we can get feedback.
We can see how they react.
We can see did this work, not work.
We have that feedback.
The problem is the AI doesn't.
The AI doesn't necessarily get the information
of what happened.
And so there's no self-correcting mechanism.
There's nobody looking at how did it reach this decision
'cause it's inside the black box
and then did it match the predicted outcome?
So there's no, it's not a circle, it's a straight line.
It's feeding out information and so it can't correct.
It can't necessarily learn from itself.
Whereas hopefully human beings do learn from themselves
and their mistakes.
And this is why Watson was giving unsafe
and incorrect cancer treatments
that if they had been employed would have killed people.
That was the hysterical.
Okay, so one of the obvious pieces of this process
is the issue of privacy and confidentiality.
If we are taking medical records
and then using them for big data analysis
and not telling anybody and not getting consent,
and these weren't records that were collected
for the purpose of research, they were collected
for the purpose of treating patients,
we have an issue here.
So if we think about the different ways we approach this,
we've got privacy, right?
Privacy is, you have a right not to tell your secrets.
You can have this lockbox where you keep your secrets
to yourself and you tell no one.
That's privacy.
But the minute you tell one person a secret,
you've broken privacy.
We're seeing a lot of this play out
in Washington, DC right now.
This is one of the issues.
Once you tell one person, you've waived privilege.
You waive privacy.
So what we have in medicine is, in medicine
we need people to tell their secrets.
We can't diagnose.
We can't treat unless people are willing
to be open and share and truthful.
So we've developed this idea of confidentality,
that the person you share your secret with
has an obligation to protect it.
So patients can tell us their secrets,
their embarrassments because they know
it won't go any further and we will only use it
for their own benefit.
But what happens when we sweep these records up
into a research study that are identifiable
because we've reconstructed family trees?
Or let's not even reconstruct family trees.
Let's strip out this, strip out the emergency contacts.
Well there's been study after study after study,
a lot of 'em out of MIT that can take anonymized data
and using publicly available resources, reidentify
or at least reidentify within a family
in a geographic area.
So one of the challenges we have now is
making things anonymous and deidentified
has become a fiction in many ways
once the data is out there.
So this represents a strong privacy
and confidentiality issue.
This device is talking to the cloud,
to the data structure.
The data structure is sending information
to the portal that my physician is looking at
and my family member that I've given access to.
Not all of that is safe.
They're all places where confidentiality
and privacy can be breached.
So my physician has this obligation
to keep my confidentiality.
It's part of the fiduciary relationship.
My family and friends don't have that obligation to me,
when I'm sharing information to them.
And you know what?
Other people want this information too.
Pharmaceutical companies want it.
Is it the drug that's not working
or is it that the patient is non-adherent.
The pharmaceutical company wants this data and information.
The device manufacturer needs the information
so that they can do software updates,
that they can do improvements.
They need access to the information.
The software developer, I mean your iPhones update
every year with a new operating system.
There's security updates every month.
The software developers need access
to what's going on, how it's being used.
How does it interact with other programs on your phone?
They want access.
And your insurance company wants access.
They wanna know if it's working.
Why should they pay an extra $1,200 a month
for a digital solution unless it's actually working,
making a difference.
And none of these people, except for the physician
and the nurse practitioner,
have an obligation of confidentiality, none.
Cybersecurity is also an issue here.
I mean we've got devices and we've got information
flying through the wifi and the ethernet system.
And again, there's no regulation here.
There's also not a requirement
that this information is encrypted.
So if you are in a public wifi,
so we had breakfast at Le Madeline.
If I had logged on to my wifi, which is something
I actually miss.
We don't have those in Texas.
So I put, went into the public wifi there
so I could check my email.
Well, if I've got a device and the device
is information here, and it's sending out a stream,
it's easy to intercept.
Very easy to intercept.
And so they can then put malware on here.
So matter where I am, now they can track it.
One of the, well true story.
When Dick Cheney was Vice President of the United States
and he needed a pacemaker, they actually had
to put in an old version of a pacemaker for him,
that old model because the new models were internet-enabled.
And the fear was somebody would hack his pacemaker
to kill him.
There were a lot of people who were looking
for ways to do this (audience laughs)
but it was a real concern.
So they had to find a model that
didn't have internet enabled.
And this was the Secret Service who had this concern.
These devices are hackable.
If you remember a couple years ago,
there was a day where there was sort of this low-level
malware attack that kinda went out
and shut down a bunch of websites and stuff.
And nobody could figure out where the program
was coming from.
Well it turned out that program was all
on our home devices.
It was on your Nest Thermostat.
It was on your internet-enabled refrigerator.
It was on your cam.
Because they all have memory.
They're all connected to the internet
and none of them have much security on them.
And so they were all infected with this malware
that became active on the certain day and time.
All of your implantable devices have the same vulnerability.
They don't have malware protection.
They are internet-enabled and they can be hacked.
So it is theoretical and this has actually been
in some spy novels where they've killed people by hacking.
There's one where they hacked their insulin pump.
It's "Cell" by Robin Cooke.
And there was an algorithm in AI that decided
when people were about to cost a lot of money
to the healthcare system, if they died
it wouldn't cost money.
So it was a cost-saving measure.
It would go to their implantable insulin pumps
and it would just release all of the insulin at once
and kill them and save money for the institution
'cause the institution programmed the machine
to improve the bottom line.
These are all, some of this is science fiction
but it is theoretically possible to do now.
And because there's no requirement to encrypt
this information, anybody can grab it.
The cybersecurity is a threat that we are not
used to thinking about.
There's also a change to the physician
or the nurse practitioner-patient relationship.
So in a lot ways we have to trust our patients
that they're telling the truth despite the fact
that Dr. House says everybody lies.
But at some level you have to trust
that a patient's telling the truth
because they get something out of telling the truth.
It's important to them.
But now I don't have to take the patient's word.
Yeah, sure you're taking your pills.
I mean we used to do things like bring it in
and we'd count them.
But now I know if they're taking their pill.
I know if they're doing some exercise.
I know if a cardiac patient is doing exercise
because their watch or their Fit Bit
is recording their steps.
Now, they might be strapping it on to their toddler
as it runs around.
I knew somebody who was in a competition
for who could get more steps and she strapped it
on to her dog and the dog walker
could take the dog out.
So you can game the system.
And it might even be possible to cheat the pill
if you dropped it into a glass of soda
or gave it to your dog.
I mean, these things are possible.
But, you don't have to trust patients anymore
'cause you can verify.
Now this creates some interesting complications
because as a patient, I may think
now that you can monitor me, that you are monitoring me.
So if I can't remember, you know I don't have
to check my sugar levels anymore
because the pump's doing matching up with the device
and they're doing it automatically
and you're keeping an eye on it.
In the user agreement that nobody reads,
it says, do not expect that your healthcare provider
will actually be looking at this data.
Okay, so we're taking this expensive method to use
so that the data collected and available
to the physician but there's no guarantee
the physician's looking or using the data.
But I might psychologically, if I take on
like a therapeutic misconception,
I may assume that this device,
I could assume that the device itself is curing me.
I could assume that there's somebody
in my doctor's office who's actually keeping track
of what's going on.
So part of the role becomes now managing the expectations.
I'm only gonna check once a week.
I'm only gonna check once a month.
The hope is that patient have to come see their doctors less
because you will be able to do more remote monitoring.
But that means you have to do more remote monitoring.
What if somebody stopped taking their meds
and then goes out and commits a crime
and you didn't check the portal.
Are you now liable because you didn't check the portal?
Is this a new obligation, legal obligation
that you have and take on?
And there's another user agreement
for the physician as well when you sign on to the portal.
But again, it protects the company.
It doesn't protect you.
And the companies say they are staying out
of the physician-patient relationship
which means you now have to negotiate a lot of things
with your patients.
There might be blame for the medical center
for not checking the portals to make sure
if there's an adverse event, keeping track of these things.
So there's some new issues here.
The responsibilities are important
and a study in 2016 found that right now
doctors are spending two hours working
on their computers for every one hour of seeing patients.
If digital medicine really gains more of a stronghold,
that's gonna get worse.
That's gonna increase dramatically.
There's also some issues that haven't been addressed yet
in all of this, and we need to think about it.
So this all presumes that the patient consents
or signs the user agreement, the the patient volunteers
to use these.
Right now it is voluntary.
Nobody forces you to it, but it is a very small step
to where the court orders you to take
the internet-enabled medication
or to have this particular kind of monitoring.
It's a very small step.
And there are criminal justice systems
investigating this right now.
And there are companies pitching this
to court systems right now.
And so there is a very, we're in a period now
where we can do these things.
But very soon we will get to you must do these things.
So right now you can use an artificial intelligence
to help you make treatment decisions.
But it's not a large leap to where you now have
to explain why you aren't following the recommendation
of the AI.
And the presumption is the AI is right
and not the doctor.
And what happens to your liability
if you ignore the AI and you have a bad outcome?
It's a whole new world and not necessarily
an exciting one.
So the hope of this is that we are gonna have
better control of chronic conditions.
That's really the hope, that people won't get
as acutely ill because they'll be there.
It's expanding expertise.
So you don't necessarily, you could be
in a rural area or a remote area
and you don't need an expert because you have
the AI that's the expert's system
or you have the devices that are sending it
to the expert in the field.
And so in research-poor settings,
this could be a real boon.
Tedious tasks of reading scans and charts
are gonna be taken over by machines.
The human error element might be removed.
One place that AI has been very effective
is in scheduling ORs.
And so the AI, you know we think we know how long
a surgery takes, but the AI looks at the condition
of the patient.
It is much better, and this one hospital
that adopted it, was actually able
to double the number of surgeries they could do
without adding any ORs because the system
could more accurately and precisely schedule.
So that's really cool.
And it's a decision assistant.
You know, which of the cancer treatments are available
and you can use AI to help.
You just don't use IBM's 'cause it might
kill your patients.
But these things are available.
But we don't, we haven't really dealt
with the ethics and the policy
and the regulatory issues yet.
There's no oversight structure for any of this.
We've got the black box and nobody's allowed
to look inside of it.
We don't know how these decisions are being made.
There's no MMR for an AI.
It doesn't exist.
Privacy rules clearly need to be changed
and updated to deal with this.
And what happens when there is a disagreement
and it means more computers in practice.
So this is a study that came out of Stanford
where they put doctors and AIs against each other.
And the AIs outperformed the doctors.
But what out performs the AI is when AI
and doctors work together.
So, welcome to the brave new world of digital medicine.
And thank you.
(audience applauding)
So I'd like to send out an invitation
to our remote sites.
If you wanna call in and ask questions,
you can do that now.
(muffled speaking)
Hi, (muffled speaking) thank you.
(muffled speaking)
I'm sorry, I couldn't hear that.
Could you repeat it again?
(muffled speaking)
Ah, so yes.
At this point there has been no data. (muffled speaking)
To repeat it, so the question is,
at this point, the digital pill hasn't been proven
to affect patient outcomes.
And yes, at this point there is no data showing
any change in adherence levels
because that study just haven't been done.
So no, that data does not exist yet.
Audience Member: Thank you.
-: You're welcome.
Audience Member: First let me say,
I just turned off my iPhone.
(laughing)
My question comes, so the things you were list,
the last thing was the insurance question.
And a generation ago when DNA testing came out,
they actually enacted a law that protected people
from their DNA testing from insurance.
Is there any such solution ahead?
-: So the question is about insurance
and that for DNA we enacted GINA, the Genetic
Information Nondisclosure Act,
and is there something similar for digital medicine?
The short answer is no.
There's nothing for digital medicine yet.
The other thing is GINA is a very, very weak law.
And it basically says they can't tell
your health insurance company about the information.
But we can tell your life insurance company
and your auto policy company.
And we can tell your employer.
None of that's covered under GINA.
So it's not great model for what we probably need.
Yes, in the very back.
Audience Member: So there's an iPad application
where patients can do to direct applications
(muffled speaking) a lot of indirect measurements
(muffled speaking).
How can they do the five-minute task
that spits out a very easily interpretable report
that basically has their percentage likelihood
that they'll (muffled speaking).
I'm good a diagnosing Alzheimer's
when it's in front of me but not six years
before (muffled speaking).
So kind of with that clinician AI,
what's my responsibility to understand
how that algorithm is working to integrate that
(muffled speaking)?
-: So you can't know how the algorithm's working
because it's proprietary technology.
You can know there's the app, that there's the test
and you're being told that this is predictor.
I would ask questions like you know how accurate is this
in its prediction?
Have those studies been done, because we could be
coming up with a bunch of false-positives
and we don't necessarily wanna panic people either.
So that would kind of be the first place
I would think about and look at.
But yeah, you can't actually know
how it's making these determinations
'cause that's the IP, the intellectual property
and that's not reviewed.
It's a little scary.
I mean it's exciting that maybe we can do this, right?
Like this is cool stuff.
You saw how excited I got when stomach acid became battery.
Like that's just cool things.
But we have to think about how we wanna incorporate this
into practice and into our society.
And I think that we're not actually doing that
because these companies don't necessarily want us to.
The group that I worked at with the pill,
for several years there was a bioethics committee.
They took 90% of our suggestions.
They changed a lot of their system.
But other companies aren't doing this.
Google put together an ethics group for their health.
They were in place for less than a day
before they were disbanded.
Less than a day, yeah.
Yes sir, in the back.
(muffled speaking)
So I mean technically the medical record belongs
to the healthcare provider, the institution.
But the patient has access to it.
That's HIPAA.
So if you wanted to do studies on this,
I mean you'd have to go through your IRB.
And the New York studies, there were three IRBs.
They all approved it.
But the piece that I think was missing there
is you know we do emergency, we do community research.
And what we do is we inform the community.
It's not that hard to have notices in the hallway
that we're doing this research.
It's not hard to send, you know have something
on the website.
It's not that hard to put something in the newspaper,
yes I'm old enough that I actually
still read a newspaper.
And give people an opt out.
Say, if you don't wanna be part of this, opt out.
Call this number.
Send an email.
And that was one of my problems with this
is that people weren't informed at all,
and they weren't given an opportunity to opt out
their own data.
And so yes, I think it can be done.
I think it can be done ethically.
But you kinda have to think through the ways
of doing that, and that has not been necessarily done
in the studies that have been put together so far.
Yes sir, you've hand your hand up.
Audience Member: Sort of a related issue
(muffled speaking) in my office (muffled speaking).
I get these notices, there are patients taking Effexor
and they also had been taking tramadol.
And so they're at risk of serotonin syndrome
and what are you gonna do about it (muffled speaking).
And I don't know if you have a comment on that
because you know I think about
well what's the real risk of this.
I feel like the behavior (muffled speaking)
is to protect themselves I guess.
So how do we approach that there is a (muffled speaking).
-: Well I mean those notices were created because
before we had consolidated pharmacies,
you might get a pill from here, and a pill from there.
And so we didn't necessarily know all the pills
somebody was taking, nor the interactions
that would happen.
And so these are supposed to be mechanisms
that help you think about it.
It's not at this point making the decision for you.
You still have to use your best judgment.
So I would say you know, look at the standard of care.
Look at your personal experiences as a provider.
Let the patient know that this a potential side effect,
that this is a potential interaction
and then you decide with the patient.
Now, might that change in the future
where they go a way, if you ignore this
and prescribe these two meds, now you've got a problem.
We're not there yet.
I hope we're never there.
But I think it's just, it's sort of a double check.
It's like a time out in surgery.
I would look at it that way.
It's a time out.
Are we doing the right thing in the right place
for the right reasons?
But it's not telling you how to treat your patients.
It's just telling you to take a second look
if that helps.
Yes, Dr. Sadler.
Dr. Sadler: My insurance wants me to do
a couple of studies that will tell me
with X level of probability (muffled speaking)
whether I'll drop dead in such and such a time
from a MI or cerebrovascular event.
And so you haven't said much about predictive analytics
and that sort of stuff.
I mean my reaction to his proposal
was do I really want this?
Is this information (muffled speaking).
-: Yeah so, I've gotten into trouble on this one. (laughs)
When the predictor test for Alzheimer's came out,
you know the genetic test, I came out in AARP
that I was against it because there was nothing
you could do to prevent it.
And so my perspective is look, the things you can do
for preventing the heart attack are the things
that anybody should probably be doing anyway.
And so if the test isn't gonna change your behavior,
then it's not worth doing.
My fear would be, what if you took the test
and said there's a likelihood.
You need to change your diet, your exercise,
take an ACE inhibitor, and you don't.
Is your insurance company, goes oh, it was
a preexisting condition, we don't need to cover it now.
Which could happen.
So I think when there's nothing that can be done,
I wouldn't do the test personally.
And I got a lot of hate mail after I came out
that I wouldn't take the Alzheimer's test
because people were like, you know in my family
it's a curse.
Not knowing would be this great thing.
And I was like okay, for you maybe it's the right choice
but it's probably not a great default choice for people.
And what I'm not trying to tell anybody
what they should or shouldn't do.
I'm trying to get people to make informed choices.
And so I would say you have to work
with your doctor to make an informed choice.
That's all we can do.
Thank you.
(audience applauds)