#213: Artificial Intelligence and the Digital Healthcare Revolution


… And, it is a great product. So, thank you to Livestream. Today’s show, we’re talking about AI in healthcare,
and we have an amazing group of people joining us. And, let’s begin with Shari Langemak, who
is with Medscape. Shari? Hi, nice to meet you. I’m really looking forward to the discussion. Just a few words about myself: I work for
the German edition of Medscape. I’m based in Berlin, that’s why it’s a bit
darker in here. I’m basically very involved in the digital
health scene here in Europe. I advise a couple startups and investors here
in Berlin. And, digital health and especially AI is my
passion. [Laughter]
Fantastic, Shari! And next, in no particular order – I’m choosing
by the order in which they’re displayed on my screen – is David Bray, who has been on
CXOTalk a number of times. And, is the Chief Information Officer of the
FCC. David Bray, welcome back! Thanks for having me, Michael. And, it’s always a pleasure to be here. I have to admit, while in my current capacity,
I don’t do healthcare, coming from the Centers for Disease Control in the past, and being
involved with the bioterrorism perparedness response program, I’m very interested into
how we can make improvements as to how do we respond to disruptive health events both
locally as well as globally. David Bray, thanks a lot. Last, but not least, is Daniel Kraft, who
is doing too many things to count. And, Daniel Kraft, why don’t you tell us about
yourself? Hey! I’m a physician, scientist by background,
trained in […] medicine, pediatrics, hematology, oncology, and my academic role now chairing
medicine at Singularity University, where we look at where are technologies heading,
fast-moving, or exponential ones, and the ability to leverage those challenges from
an education, environment, to healthcare and beyond. And I founded a program out of singularity
university in 2011 called “Exponential Medicine,” where we look at how we might leverage things
like artificial intelligence, to low-cost genomics, to drones, to big data, and re-shaping
and reinventing health and medicine across the spectrum. Fantastic! So, Shari, you gave a talk not too long ago,
in which you describe some of the key disruptions, having to do with data and other things relating
to healthcare. So, as an overview, do you want to maybe just
share some of those thoughts with us? Absolutely! Absolutely. So, we are progressing so much in healthcare
right now. We’re white, I mean, we’re collecting lots
and lots data, more and more data and only due to studies that are conducted worldwide,
but also due to on our mobile phones. So, everybody’s using health apps, we get
a lot of information from that. And with the help of this data, we will finally
be able to treat diseases in a much better way. So, we speak about the era of precision medicine. So, a patient is not on the disease, or symptom
anymore, but he is a person with many different factors we can take into account for his own
treatment that can be the genome, his own genome, or a tumor genome, and even his microbiome
or his treatment preferences. So, with the collection of all this data,
we have, right now, we are finally able to find the right treatment for each and every
patient. But of course, this is one of the topics we’ll
probably dive a bit deeper into today: It’s very, very hard to take all these different
factors into account, because for a physician to keep up with the speed of information,
with the speed of knowledge, is very, very, very hard. So, we need some sort of algorithm, some sort
of machine learning to conduct and to help us to support our decisions. Daniel, what about this notion of algorithms
and machine learning? Well, as Shari mentioned, we’re in this exponential
age where you want to get to true precision, personalized medicine, and give the right
drug, the right therapy, the right prevention, diagnostics, and therapy. The challenge is it’s really hard to connect
all those dots right now, and any physician, pharma person, anybody who’s trying to make
sense of all this data, our brains are challenged, our brains haven’t had an upgrade in at least
a million and two years, but our wearables, our devices, our ability to compute is changing
at an exponential rate. So the challenge now for a clinician is to
integrate someone’s digital exhaustion, the wearable devices on mooring 3: the Withings
Watch, the Apple Watch, and O-Ring, to integrate […], to integrate the latest guidelines
and publications. The average physician, at least in the United
States, only reads journals 3-4 hours a month, and there’s no way, again, to integrate into
practice all that information. So we need machine learning, AI, big data,
just to synthesize some of that, to bring the right diagnostics, therapy, for that patient
at that point of care. And that’s sort of the promise of […] AI,
machine learning, and big data as for now, in an era where a lot of it is becoming available,
but how to make it actually useful, provide better outcomes at lower costs is still a
huge challenge. And David Bray, what about the policy implications,
and the intersection of healthcare, AI, and what do we do about all of this? So, wearing on my Chief Information Officer
hat, what I love about what both Shari and Daniel were saying is really about how we
can use AI almost to augmented intelligence for both the physician, as well as the patient,
because as they said, we’re in an era of exponential data, in terms of new therapies, but also
everything that’s possible that can determine your health outcomes. And so, policy-wise, I think we need to think
about, for the patient, how can we think about providing choices, so that they actually have
informed choices about both what they want to know, or what they want to have now with
their data. And some of us may want to have our data more
shared, because maybe it means better health outcomes, but others we may not want to know
everything because we’re not ready for that sort of carrying into the future in maybe
nine, maybe fifteen years from now, based on our genomics we may have this complication
in our health. And that means being an informed choice. Obviously, I’m not a physician, I am a CIO. I do think we need to think about how do we
address the data, how do we address the sensors that are collecting it, and then finally,
how do we make sure that you have a locus of control as to what’s done with your data,
and what algorithms are running with your permission and which ones aren’t. And I think that’s very key. Shari and Daniel, you are both physicians. And, what about the impact on medicine, and
the role of the physician, and how can physicians make use of this data, and where are we in
the process of having this data, and then being able to use it in machine learning,
and AI, and can you give us practical examples? I’m throwing a lot at you all at once. Well, I’ll start. I think you start with the question about
algorithms, which may be a bit different than AI. A lot of healthcare is, you know, in terms
of what we do as physicians, is look at a patient, they’re complaining of pain when
they pee, and so we check their urine: Is is positive for nitrates and bacteria? We’ll then maybe it’s a urinary tract infection. That doesn’t take going to medical school
to learn. A lot of basic common healthcare issues can
be triaged for these partially diagnosed with pretty straightforward decision tree charts;
and they’re now – not an explosion – but some examples of chatbots, or simple, early AI,
where it can ask you about your symptoms, and can tell you is that belly pain likely
to be appendicitis, or just indigestion? So I think there’s sort of the simple side
of algorithms, which in many cases, where there is not a lot of medical care; many parts
of the world don’t have access to physicians, or it’s expensive to reach them, we can do
a lot with simple, through your SMS or your smartphones, provide algorithmic-based, especially
triage, and health education. And then we get farther up the spectrum. When I’m seeing a patient with a urinary tract
infection, instead of just giving them the standard antibiotic, maybe we’ll have other
information about their renal function, their BMI, what dose you might want to give based
on their pharmacogenetics, from a 23andMe-type profile, or their full genome, which is coming
down to a thousand, or maybe a hundred dollars this year. So you can go from simple algorithms, pick
up the urinary tract infection with an algorithm, maybe even call in the prescription by a bot,
and deliver it by a drone; but then maybe get much more personalized, to pick the most
appropriate antibiotic that’s safest, that will give you the best outcome based on that
individual, the best information from the CDC, all available at the right time and at
the right place, and do that in the right functional and low-cost manner. That’s just one small example. I might add to the role of the physician. I think it will change significantly in the
next years already. So every time I’m at a medical conference
speaking, the physicians are actually afraid that they would be replaced. But, I highly doubt that this will be the
case, at least in the mid-term. It’s rather a tool for the physician to help
him to make better decisions. They want me to replace … We know from very
recent studies, that while AI in some cases is better at diagnosing, for example, a rare
disease in certain cases than the physician, the best outcomes are when we have when physician
and AI work together. So, you can think about it like you check,
or you type in the symptoms, you check with the genome, some sort of basic lab parameters,
and then you get a recommendation or from AI, and then still the physician needs to
check if that’s really the case, and if you trust this recommendation. I think this quality check is very, very crucial. We won’t get rid of it in the next years,
for sure. And I actually have a question for both Daniel
and Shari, in that for Daniel, my question is do you find that you feel like you have
a locus of control over the data that’s being collected from the different sensors you’re
wearing. And then for Shari, I guess the question is:
As a physician, what would be the best way for algorithms and/or an AI to present new
information or novel information to you in a path that you could actually absorb, and
actually integrate into your practice and care? Well I’ll start. I mean, great question. I think today, clinicians are still overwhelmed
by having to spend half their time typing electronic medical record notes, [at] double
the time they have face-time with their patients. The flow of data from wearbles or Omex, etc.
is not really integrated into the workflow of most clinicians, at least in the United
States. And we’re just starting to enter this era
from very intermittent data being very reactive. You know, waiting for disease to happen, having
broken feedback loops from blood pressure to blood sugar, to be much more continuous
with their data, and much proactive as individuals, as physicians, etc. So, right now, all of these consumer devices;
I was just at a consumer electronics show two week ago; there’s even more from tracking
mothers and pregnant women, and the baby in-utero, all the way to tracking the sunlight exposure
in your sleep. What’s just starting to happen in the last
years, is that the data can flow, in my case through my Apple Health Kit in my Apply Phone
into my electronic medical record at Stanford, where my physician can start to see that and
vet it into the EMR. But right now, he may have 2000 […] who
want to log in to look at everyone’s stats, and blood pressure, and other data. We need to have the AI/machine learning sift
through that information and present to the doctor the five patients in his practice who
you may need to call and bring in today, based on their blood pressure, their sleep data,
maybe their respiratory rate, picked up by their mattress. So today, we have a lot of sensors, this Internet
of Things is blending into the internet of medical and health things, but still the docs
aren’t really connected. There’s not a lot of interoperability, and
the core clinician doesn’t want to see more raw data. It needs to be synthesized, so it’s actually
useful in a timely way, and that the clinician is rewarded for doing this. Can they bill for doing an e-visit, for looking
at the data? As cherished [as it is], I think the role
of the physician is key; we’re not going to get replaced by AI, but it’s going to augment
our skills, and enable us hopefully to be much more proactive with our patients from
keeping them healthy, to thinking of a disease earlier, and then managing chronic diseases
in smarter, evidence-based, and feedback-loop ways. Excellent! Yes, and so your question was about how should
it be presented to physicians in a clinical context, right? I think we already see some small examples
of that. I mean, the very basic AI in medicine is basically
that every software checks if there are interactions between medication. We have that already for a long time, and
some sort of AI and machine learning as well. The other implication: physicians are already
working with AI in medical imaging. So, they get some suggestion what diagnosis
is behind a CT image or some sort. But we will see more and more AI that suggests
some diseases, and I think we have to make sure it’s less about how this data is presented,
and how the possible diagnosis is presented. But educate physicians about what their limitation
may be of AI is, and that they should always question – not maybe always – but in many
cases, question the background of this, because AI is basically based on studies on data,
and it’s only as good as the data feed. So, if we don’t have good study data, or if
the angle of research changes, also the recommendations can be fought. I’m hearing my computer science background
heart sing, because the mantra of “garbage in, garbage out,” we need to keep that in
mind when we do algorithms and AI. On that point, I think what’s exciting about
this age is that hopefully the future of healthcare that we practise medicine isn’t going to be
evidence-based, meaning we look for double-blind, procedural controlled trial of a patient just
like the one I have, which usually isn’t the average patient, but much more practice-based
medicine: mining data from all the Epics and Cerners, and NHS data , say for David, who’s
got this particular condition with this genotype, this looks like it will be the most efficacious
therapy, and that we can continue to sort of crowdsource that information. There’s still a lot of data-blocking: pharma
companies, EMRs, hospitals, academics don’t talk to each other, but if we can start to
collect information, mention crowdsourcing earlier like when we drive with Google Maps
and Waze, we’re used to sharing some private information: our speed and our location. In exchange, we get a map of the traffic,
and we can adapt around. We can use that same mindset across healthcare,
and that the information isn’t just garbage, but it’s synthesized from thousands or millions
of people, and we have our own sort of healthcare map: A GP has to guide me in my healthcare
journey, or a patient I may have, and those maps keep getting refined, and when there’s
a traffic jam, you’re saying we can learn to route around this. That’s an opportunity to improve the data
sources, because the way we collected data, did clinical trials, is set to be shifted
dramatically if we leverage some of these tools with the right regulatory reimbursement
and other mindsets. But how do you make this happen, because even
if it can be proven scientificially, medically, to work, and lead to better outcomes, there
are so many entrenched interests – political interests, physician interests, economic interests,
insurance interests. How do you … You’re talking about an overhaul
of the way not only that we practise medicine, but the way we think about it. It seems like an almost impossible task. Well, there’s a lot of misaligned incentives
in healthcare that many healthcare systems; in the US, there’s hundreds of systesm. Kaiser or Geisinger can operate differently
than a fee-for-service place. So part of it is aligning incentives as we’re
moving from fee-for-service healthcare, at least in the United States, to more value-based
care, we’re going to be rewarding technologies and systems that give us better outcomes that
we can measure, whether it’s keeping someone out of the hospital with heart failure, or
doing a smarter, earlier job of diagnosing a cancer, then using, like IBM Watson’s already
done, to help figure out the best sort of therapies for particular lung cancer patients. So, no oncologist could synthesize from all
the new molecular markers, and different combinations of drug therapy. So, does this sort of lie in the interests? It’s not going to happen everywhere in some
systems. Germany, things can happen that can’t happen
here. NHS has great leverage. The VA can do things in a smarter way. So, we need to align those interests. It’s not going to happen at once. So, there’s an alignment of … So there’s
a, say, coincidence of technology on the one hand, with all of the social, the economic,
political pressures, constraints, objectives, on the other hand. And so, where’s that intersection between
the technology of AI and the data, and these other factors? So, Shari, I’d be interested in Germany’s
perspective, because if I’m correct, and correct me if I’m wrong, Shari, you actually have
tougher privacy laws than we do in the United States, is that correct? Yeah. Actually, we struggle quite a bit here in
Germany to implement new healthcare solutions, to implement … Startups really struggle
a lot to find new solutions, because there are a lot of data protection rules, but we
have many, many laws that prohibit innovation here in Europe and especially in Germany. I have kind of mixed feelings about that,
because of course, I think especially when it comes to AI, especially when it comes to
big data, data security and high privacy laws are very, very important, but you must ensure
at the same time that they don’t prohibit innovation, because it’s so important to reduce
our cost in healthcare. As we all know, we are barely able to cover
all the rising costs of healthcare right now, especially drug prices, and the rising cost
for people with chronic diseases. So we must find a way to allow innovation
at the same time, and still protect the individual. And I think, especially AI, one way to do
that is to have transparency, basically. I think companies must show what algorithm
they use on what data the recommendations are based, so that we can still, afterwards,
check if the recommendations are valid if we might need to change the algorithm, or
all things like that, basically. Alright. And one of the things that I would say from
my own experiences as a CIO is you don’t want to be top-down when you’re dealing with many
different players. In fact, it’s exactly what Daniel said: If
you want to think about what are the incentives that will help encourage people to find their
own paths in the direction we want to go, and so if the direction we want to go is holistically
treating the patient, making sure it’s outcome-based, and actually trying to make sure that we’re
thinking about how we make sense of this data overflow, then the question for us is, “What
are the incentives both in the private sector and the public sector that will encourage
innovators to move in that direction?” I mean, I think this whole value-based approach
is going to drive the incentives. If I’m a physician, and I’m not paid to see
more patients and do more procedures, but to keep you healthier, to glean better outcomes,
I am much more likely to use the AI agent to help me pick the right drug and dose, because
I’ll hopefully get rewarded in some form, whether it’s just pending on patient outcome,
to a bonus at the end of the year for having patients with good blood pressure control,
or being picked out before they end up needing hospitalization. It may be even in a few years malpractice
not to use the AI in doing diagnosis and therapy. We all know the issues of medical errors,
they make the equivalent of a 747 crashing every week or two. A lot’s happening in a hospital setting. We still treat patients based on our old experience
around what journal article we just read, and I think as we’re incentivized to get better
outcomes and rewarded in smart ways, both financially and otherwise, it’s going to drive
the adoption of these. And it’s going to be disruptive of certain
fields. Dermatology, radiology, pathology are all
based on pattern-recognition. A lot of what a physician does, is learn,
“This is what a sick patient looks like. This is a constellation of symptoms.” But we may not catch that Zebra or we might
miss something, and the more we can leverage this and again, combine it … That won’t
replace the clinician, but using a combination can give us hopefully better outcomes, and
enable a primary care doctor in rural Rwanda, using an AI app to do skin exams, pick up
early ebola, or other things that might have global health implications as we’re all getting
more super connected and the world becomes more globalized, including issues that David
knows well: bioterrorism. Are there policy, or let me put it this way:
What are the policy implications? You know, there’s legal implications, for
example, the legal changes that will need to take place to support this; other types
of government policy as well. They have started, so I think we have to answer
a couple of questions: How do we ensure this quality control we have talked about? To what extent do we want to use AI, and am
I allowed not to want to use AI? As a patient, can I say I don’t want to have
AI be used in my treatment? So that’s a very tough question, right? Because maybe the outcome isn’t as good, and
this patient might cost a lot of money to our healthcare system. Another important thing is who’s responsible
if something goes wrong, right? If AI makes a recommendation and it’s the
wrong one, is the physician in the end responsible, or the company? So, I think the only way to answer these tough
questions, because most of them also have a very critical, ethical background, can only
be solved in a public discussion somehow, at least in a discussion where all the major
stakeholders involved in bringing their view into the discussion. Essentially, I don’t know that we can run
medicine policy by voting or debates, and folks who may not have a good picture of what
practicing medicine looks like, or where AI may be in a couple years. I mean, AI is moving pretty quickly, and we
often in this exponential age don’t appreciate what’s going to be here in a couple years,
and how powerful it might be. I agree, who’s liable for this information? Just like with self-driving cars, eventually,
someone’s going to get hit by one, and who gets sued? It is the self-driving car software? Is it the person who owns the car? There’s so much data flying out, so I’m wearing
a little patch right now trying to do the live demo that’s streaming my vital signs
to my smartphone, and I can literally be sending to David and Sheri my 24/7 EKG, which you
might see here. … Hope it looks okay in my […] out there. You look very relaxed. Yeah. Yeah. Who’s liable for looking at that data in real-time? What algorithm parses that? […] your rhythm is going on here, not just
your EKG, but your sleep data, and beyond. I think we need to be careful about that over
legislating this, and allowing it to sort of have some room to expand, but in balance,
the malpractice laws at the same time. By the way …
I was just going to say, as CIO, I would love to do experimental pilots, and so my question
for both Shari and Daniel: If you could design an experimental pilot that could be done this
year to show people what’s going to be possible in this era of patient-centric healthcare
and AI, what would be the experiment you would design? Well, what I think is coming faster than we
think is in this sort of hyperconnected age to, you know, all these wearables are becoming
commoditized. It’s how we make sense and synthesize the
data. So, I like to use the example of our modern
cars, had three or four hundred sensors. And we don’t care about any sensor, but the
AI software gives you a “check engine” light with some exhaust. Hopefully, that means you’re proactive and
you take your car to the mechanic for a blown gasket. Could we start to see some pilot systems which
look at your connected home data, through Alexa, through your smart mattress, through
your wearables, understanding your own mix, to kind of give you a 24/7 surveillance of
your particular exhaust, and your baseline information, start nudging you in the right
direction to get you on the path of health and wellness, or to manage, let’s say, expensive
patients like Type 2 diabetics which end up costing the healthcare systems, with a lot
more morbidity, and challenge, and suffering as well. That might be a little pilot. How could we take a systems medicine and a
systems biology approach, and connect these dots? We’re starting to see some companies do that,
like Arivale, founded by Lee Hood, or Longevity, Inc., or Preventure. I want to remind everybody that you are watching
CXOTalk, and we’re talking about AI and data in healthcare. And right now, there is a tweet chat going
on using the hashtag #cxotalk, and you can ask your questions directly of our truly amazing
panelists today. Daniel, I have to ask you, what product is
it that you’re using that shows your EKG in real-time? Oh, this is … I’m wearing a patch from somebody
called Vital Connect; this little sort of band-aid sized element that’s disposable. These are moving into the hospitals now to
monitor patients who should not be on monitored beds. I can wear this for about a week. And again, it streams to my smartphone EKG,
temperature, stress level, other elements. And, you know as an example, an intense security
in the level of streaming data that could come up on my body 24/7, it’s a bit of a “So
what?” unless that data can flow, let’s say, not
just into my electronic medical record, but a smart medical record system, that it can
be parsed with machine learning and AI so we can figure out what changes there might
be, that might be evident to what I like to call the “predictalytics,” predicting I might
be heading in the wrong direction, or it can nudge me, or move me back to a good direction. So, there’s several of these types of smart
band-aids coming out. So we can start to measure all sorts of things. The challenge is what we do with the data,
because a lot of it is how do we blend it, put it part of the workflow for and overwhelmed
clinician, who doesn’t want some other data flow they can’t manage, that he’s liable for. So simply having a set of products that generate
all kinds of data is not useful, unless we have that entire chain built in, involving
the right data sources, the right software, machine learning/AI software to parse through
that data in a meaningful way. We have clinicians who know how to use that
data, and we have a regulatory and practiced environment that accepts the use of this data,
and that has parsed through the risks that are involved, so that it’s legally safe for
physicians to make use of this data. Well, and actually that’s what I was going
to ask Shari, because I know Germany, again, having tougher laws. As a CIO, what would be the experiment that
you would want to design for medical practice and data outcome, and how would it be different
say in Germany, versus a different [country] because of the laws? I think the biggest challenge here in Germany
is how to prove that innovation actually has a big impact on the outcome, and to fight
reservations from our physicians here; not only physicians, to be honest, but from many,
many Germans. So, I think we need … So, the thing I hear
the most of the argument against innovation, against AI, is mostly we don’t have enough
data. So, I would like to see an experiment where
physicians, and AI, try to diagnose something here in Germany, and compare it with the physician
alone, and show what we can already do, how we can already improve the outcome for certain
types of patients to show that there’s actually a huge impact on innovation in healthcare,
and a huge possibility to reduce costs for our healthcare system. Well that’s starting to happen. I’ve been involved with the X-Prize, and designing
a new X-Prize: the Medical Tricorder X-Prize, which is to build a sort of home dignostic
device to consume, blended with AI, is an example of one medical tricorder that was
part of the X-Prize competition from Scanadu, which entered a clinical trial; I think these
are closing now. But, you know, usually you can collect this
data at home, which used to require going into a clinic, or an ER, or an intensive care
unit. Now that data can go through your phone, AI
can start to look at, “What’s Daniel’s normal baseline? How’s that changing? If I’m getting a really sensitive infection,
how can this help pick that up?”, communicate that to a medical team in a smart, proactive
way, and so that’s … And so, I think part of this future is how
and where we collect this data, how the consumer, or patient is empowered to own their own healthcare
data, share it when they feel like being a data donor, a lot of new things are going
to come through these smart sensors, and clincal lab tests as well, not just vital signs. What do we need to do, or what do the stakeholders
need to do, the public sector, and the private sector, in order to encourage all of this
innovation, and create the right type of environment in which in can flourish? [Laughter] Well I’ll take one example. I think it often comes down to financial incentives,
right? If you, as a patient, can pay less for your
insurance premium, if you agree to go through an AI chatbot before you call the triage line
or show up in the ER, that might encourage some adoption. We just saw, in San Francisco, launched yesterday,
a new company called “Foward,” funded by Kleiner-Perkins, Google Ventures, and then […], who are just
trying to make the clinical practise of the future. I think that’s under 7-8 hours to a month,
you can have unlimited access. And when you go there, they have big touchscreens
where you can display the data, they apparently are using AI, and it seems like they listen
to patients in the clinic and help provide suggestions to the clinical team. So, I think we’re seeing early evidences of
this. And you can get to that smart, concierge-type
practice, at a very low price-point, under $80 bucks a month, unlimited access. That’s going to be disruptive to regular payers,
regular hospital systems, and physicians, and they drive a lot of this adoption, especially
when you see you’re getting better, smarter care, based on your own data, your own […], your
own behavioral type, and that the user face matches you. Another AI element is these smart coaches,
because you can diagnose a patient or prescribe them a therapy or other intervention. Half the people don’t take their medical intervention. Now we’re seeing these AI chatbots and coaches
that can track you and incentivize and nudge patients, hopefully in a more personalized
way that is part of this blend of AI and machine learning, and user interface that will help
drive smarter and better outcomes as well. I can only say here in Germany, we just have
recently started to [explore] this potential in e-health and digital health. We just have introduced e-health law last
year, and it shows what we basically need. We need incentives, financial incentives,
and financial penalties to move a very old sector into the future and help them to adapt
to these changes, because we tried it for a very, very long time. Anybody who listens from Germany probably
knows how long it took us to start with the electronic health records. It’s really a shame. [laughter] So, we really need these financial
incentives, at least in here, and we really need politicians who are better informed about
technology. And I see that there’s some discussions starting;
more and more politicians try to talk to young entrepreneurs, starting to talk to companies,
go to the Valley, see what’s up there so we try to catch up now and hopefully, we’re there
soon as well. [laughter]
Maybe I’ll pitch this quick question to David. You know, we have 4G or something, and now
5G and 6G are coming, which doesn’t mean our smartphones and our wearables and our digital
can exhaust can be streamed at 100x the data stream. So hopefully, pretty soon I think. So we need the SECs of the world to help enable
this data to flow, whether it be a need for a data pass for healthcare data; all these
privacy issues are critical, how do we layer things like blockchain on top of this to make
data more shareable and safe. Where’s that heading? Right. So recognizing I’m not a commissioner of the
SEC, nor am I Congressionally-appointed. I can say you’re right; 5G and more in the
industry is around the corner, and it’s going to start releasing in stages. 5G is interesting, because you can actually
do structured data elements within the signal and the message itself, and so you can actually
say this part of the signal can be shared for these purposes, or I’m a type of first
responder, I’m a type of doctor and things like that, so we can even have ad-hoc mesh
networks. And so, it will be interesting both from a
community perspective as well as a hospital perspective. How does that include the broader ecosystem
of care that includes physicians, also includes first responders that are first at the scene
for your health. Maybe there’s a burning building, and you’re
unconscious, but your phone is still active: can they find you so that they can bring you
to the hospital and things like that. Also, we need to think about how we can use
this to have smarter transportation of people’s data, because as you know, the data’s growing
so large. If we were to port that file everywhere you
went, that would be voluminous. And so, we need to start, like you said, thinking
about how do you inform sharing? I, particularly, am interested in; and I try
to tell people, and I try to talk about public services as opposed to just government. I think a lot of the innovation is really
going to come from individuals in the public that are caring about this issue, whether
they themselves have an affected family member that they want to get better healthcare for,
or they’re just passionate about making some innovation in this area, as well as public-private
partnerships that are thinking beyond their own bottom line. I do wonder if the world is changing so quickly
that the traditional approaches to top-down addressing of these issues will not succeed,
and so the question is what is an informed approach that does protect the consumer, does
protect the industry, but at the same time, keeps up with the speed of change that is
expected to happen. And I don’t have any easy answer, other than
to say what we did here at the FCC, when we had the FCC speed test app, is we made it
open-source; and this was done in late 2013, and you can imagine given the events of late
2013 to say, “Hi, I’m with the government. Would you like to download an app that will
monitor your broadband connection?”, that probably would not have been well-received,
except we made it open-source. You can see by design, we weren’t collecting
your IP address, and by design, we didn’t know who you were in a five-mile radius. And so, maybe there are things that require
public trust. We can expose what the algorithm is doing,
or expose what is being done with the data, so you can see that we’re doing privacy by
design wherever possible, and then giving you informed choices as to maybe you do want
ot share more data because you think it will help inform the cancer clinical trials that
will make your loved one healthier, or you may choose not to do that because you value
your privacy more than whatever other outcome. So, I think we need to rethink how we’ve done
public service, in the same time we’re also thinking about how we address healthcare and
other things like this. So it’s going to be a very interesting challenge,
and that’s why I’m really glad that people […] like Daniel and Shari are leading the
way from the physician perspective, because really we got to let the experts lead the
way as to how we address these issues. We have just about five minutes left, and
we began this discussion talking about the disconnected pieces. I think Daniel and Shari, you said the dots
need to be connected. And it seems like this is the fundamental
problem, hearing you talk, because you’ve got the technology providers, you’ve got the
physicians; there’s all of these people working on it. And Daniel, you mentioned earlier that it
will ultimately be financial incentives that enable the chain to be connected, that aligns
the regulatory environment, and so forth. And so, in the last five minutes, I’d like
to ask the three of you for your advice, both for the public sector, and for the private
sector, regarding how do we create the environment where the dots can be connected, and we have
a context to enable this to go forward and be used in practice? What advice do you have; specific advice? For example, I was in Washington this summer
with Vice President Biden as part of the Cancer Moonshot summit, and a lot of the focus there
is to do ten years of progress in five years of cancer, particularly in therapeutics. A lot of that was aligning the ability to
share data, and catalyze that between pharma and academics, and hospital systems, speed
up IP, intellectual property; speed up the FDA processes for new cancer drugs. Some of the lessons do get driven by policy
and convening, getting everyone to agree to collaborate and connect the dots. The big HIMSS conference is coming up next
month, where there are still all these issues about interoperability; a lot of systems just
don’t talk to each other, they’re not incentivized to, so that can be driven by policy. And again, on the smaller scale, every individual
can start playing with little AI chatbots, and bring them to their clinician, and clinicians
are out there starting to say, “What are tools that exist today, even if it’s not paid for,
or it’s […] that I can use to start enhancing my practice, or my touchpoints with my patients;
so, not waiting for the future to arrive. Again, the future’s already here, not just
evenly distributed, a famous quote, and it’s up to us to not just predict the future, but
to create it using some of these new tools, and to catalyze that differently. In Berlin, which is an amazing startup culture,
in Silicon Valley, and in other parts of the world, which, where everyone isn’t wearing
an Apple Watch or wearing the Google Glasses about you. So we have a question that’s related to all
of this from Twitter, and Joanne Young, who is a very experienced Chief Information Officer
herself in higher-ed, is asking, “Does the promise of AI include halting, or reversing
cost escalation?” So that, obviously, is a key part of it. Anybody want to jump in on that before we
finish off with the remainder of the advice? I think I like to take that question. So, of course, there is a fear that we have
increasing drug prices, and even more personalized care through AI, because patients get very,
very specific treatment, which is very costly. But, AI can help us to reduce costs in many
ways, not only by reducing the rate of complications, but also by helping pharmaceutical companies
to reduce the time it costs them to bring a drug to the market. First of all, we already have started working
on recommendations for pharmaceutical companies to follow some sort of direction for a new
drug, so they don’t have to spend that much time on many different types of drugs to see
if it works or not, and it also helps, of course, in the end, to get an FDA approval,
for example, because we can use the data, use AI to see if a drug is safe or not. So, in the end, I strongly believe AI will
help us to decrease cost in healthcare. And when, as the incentives shift, like right
now in the United States, for Medicare, hospitals don’t get reimbursed if a heart failure patient
comes back within the first month, and there are several companies that are setting out
mass networks in the patient’s home, to look at the scale, their blood pressure, their
activity, and have that sort of early “check engine” light in red, green, or blue; red,
yellow, green; to help identify the folks that are moving from green to yellow before
they get to red. And so, we can be much more proactive using
this data, using the algorithms to pick up that digital fingerprint of someone falling
off the deep end on heart failure, or mental health issues, for emphysema, and that can
lower costs. We’re picking up someone who’s pre-diabetic,
before they become diabetic, and putting it into these programs, like one from Omada Health,
which is a digi-ceutical social network platform that can trim people around through behavior
change, […]. So, you can definitely lower cost by using the data in smart ways and leaving
early signals that can change the course of a disease path. I love this idea of the digital fingerprint,
and AI being used to interpret that data. We’re just about out of time. Shari, do you want to share your advice for
making this all come together, and then David, we’ll turn to you. Absolutely. I can only agree with Daniel, but the future
won’t wait for us, and especially when we’re talking about Germany or European countries,
I hope that we start the discussion, that we start to get informed about these new technologies,
and start implementing new laws that will allow innovation, and that prevent the risks
that come with it. It’s a topic that is not easily touched here
in Germany, we sometimes believe that by avoiding the topic, we find a solution, [but] it’s
not like we can prevent innovation. So, I really hope that the discussion starts
now. So, I will conclude with three thoughts, and
enthusiastically agree with both Shari and Daniel. First, as a CIO, when I arrive, we are spending
more than 85% of our budget just to maintain the legacy systems we had, and while we didn’t
see a budget increase, we were motivated to move to public cloud and new technologies,
because we could see an efficiency at scale. So, I’m hopeful with AI, even if there’s not
necessarily an overt financial incentive, it could be just a legacy way of doing things
as later shown it will be so expensive, you have to move to it. Two: When we made that move, and people thought
we were crazy to do it in 2013-2014, we need safe spaces to experiment. I mean, yes, there’s certain parts of medicine
that you have to keep going well, and keep the train going on time, but creating those
safe spaces will be the key to show what’s possible to bring everybody else along. And third, it’s going to take all of us. It’s going to take physicians, it’s going
to take IT professionals, it’s going to take public. This is a massive endeavor, and so I look
forward to seeing what sort of almost ecosystems of thought, and action, evolve as a result
of this. Alright. Wow, this has been quite a discussion. You have been watching Episode #213 of CXOTalk,
and we’ve been discussing healthcare, and digital data, and AI, the regulatory environment,
and a lot of other topics. Share this with your friends, because the
transcript will be up on the CXOTalk site early next week, and it’s a rich treasure
trove of material. I’d like to thank Daniel Kraft, David Bray,
and Shari Langemak, for spending time with us. And, we’ll be back next Tuesday for our next
show, and then we’ll have a show the following Friday. The next Friday as well. Thanks so much, everybody. Thanks for watching. Bye-bye!

Daniel Yohans

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