June 17, 2021 • 32 mins
Welcome back to Season 3 of the Not Mini Adults Podcast. To kick off Season 3 we have a very special guest and spend our time talking about the importance of data and AI and what it will mean for our children's wellbeing now and in the future.
This week we are talking to Dr. Anthony Chang. Dr. Chang is the Chief Intelligence and Innovation Officer as well as Medical Director of the Heart Failure Program at Children’s Hospital of Orange County. Dr Chang also founded the Medical Intelligence and Innovation Institute (MI3) at CHOC Children’s.
Dr. Chang holds an MD from Georgetown, an MPH from UCLA, an MBA from the University of Miami, holds a certification from MIT on AI, and has an MS in Biomedical Data Science from Stanford.
Anthony has been called “Dr. AI” by the Chicago Tribune having published numerous peer reviewed papers in ML and AI related to medicine. He is passionate about how AI can transform healthcare and works tirelessly in advancing the use of AI.
Dr Chang is also one of the original founders of the International Society for Paediatric Innovation and to us is the Godfather of Paediatric Innovation.
Visit our shop here to purchase a copy of the Thinking of Oscar Cookbook - Made with Love or Face Coverings. THANK YOU!
Thinking of Oscar website and contact details can be found here.
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–
copyright Lisa Fitzgibbon 2000
Written & performed by Lisa Fitzgibbon,
Violin Jane Griffiths
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Anthony (00:00):
Medicine up to now has been like a string ensemble, but
(00:03):
mainly string instruments. Nowwe have the availability of
trumpets and clarinets anddrums. It doesn't mean that the
violinist needs to drop theirviolin and learn these new
instruments. Just learn how toplay with those other musicians
so that we can have a muchbigger range of music that we
can play together. So if youequate that to, you know, the
(00:26):
very complex chronic diseasepatients, it's like expecting a
string ensemble to playBeethoven's Ninth Symphony, he
just can't do it enough justice.
And you need other instruments.
Actually, Beethoven's Ninth hassingers as well, right. So you
need as many types ofinstruments as you can to really
bring that Symphony to life.
David (00:53):
Welcome back to the not mini adults podcast pioneers for
children's health care andwellbeing. My name is David
Cole. And I'm joined by my wife,Hannah. And together we are the
co founders of UK children'scharity, Thinking of Oscar, it's
been quite a year for us, as I'msure it has for many of you. And
we're sorry, it's taken us awhile to come back with our
third season of the podcast.
We're so grateful to everybodywho has downloaded, shared and
(01:15):
enjoyed the pod. And we'rereally delighted that so many
medical students have chosen tolisten and learn more about some
of the amazing people that wehave been lucky enough to talk
to. We have what we hope youwill agree is a wonderful set of
conversations coming up over thecoming weeks. And once again, we
are humbled to be able to sharethese conversations with
everyone. We started thispodcast in 2020 with three aims,
(01:35):
firstly, to continue to educateourselves, so that when we're
asked to support a project,we're able to be in the best
position to do so. Secondly, westrongly believe that you can
change the world by sharing yourstory, and therefore, we wish to
share as many stories aspossible. And finally, we hope
that those listening will beinspired to want to help to do
more in children's health.
(01:58):
Before we get going with thisweek's episode, we want to say a
quick thank you to our commsteam who have worked throughout
the start of 2021, helping tospread the word of the podcast
and share the stories that we'vebeen lucky enough to discuss. So
thank you to Susie, Annie, andCarrie. And now without further
ado, let's discuss our guestthis week. You heard a clip from
him earlier and those that knowhim will know that we are
(02:19):
delighted to welcome Dr. AnthonyChang to the not many add ons
podcast to kick off seasonthree. Dr. Chang is the chief
intelligence and InnovationOfficer as well as medical
director of the heart failureprogramme at Children's Hospital
of Orange County, and he foundedthe medical intelligence and
Innovation Institute, MI3 atchoc children's also, Dr. Chang
holds an MD from Georgetown, anMPH from UCLA, an MBA from the
(02:44):
University of Miami, and alsoholds a certificate from MIT on
AI. And finally, he has an MS inbiomedical data science from
Stanford. Anthony has beencalled Dr. Ai by the Chicago
Tribune, having publishednumerous peer reviewed papers in
machine learning and artificialintelligence related to
medicine. He is passionate abouthow AI could transform
(03:07):
healthcare and works tirelesslyin advancing the use of
artificial intelligence. Dr.
Chang is also one of theoriginal founders of the
International Society forpaediatric innovation. And to
me, is the godfather ofpaediatric innovation. Anthony
is most importantly, a father oftwo young girls, who he also
discusses during ourconversation, we can think of no
one better to kick off our thirdseason of the not mini adults
(03:30):
podcast, and we hope you enjoyour conversation as much as we
did. Anthony, Hi, thank you somuch for joining us on not mini
adults podcast.
Anthony (03:43):
Thank you very much, David and Anna, and I just want
to publicly thank you foreverything that you do to help
children around the world. It'sphenomenal work. So thank you
David (03:52):
Thank you. So I mean, a lot of the stuff that we try and
do is actually been inspired bythe work that you have done with
I spy, and we'll talk a littlebit about that. But you know,
you and I met a few years ago atthe peace 2014 conference, which
was a massive eye opener for usin terms of understanding just
what the world is looking for,what the world is doing and the
(04:12):
importance of bringinginnovation into into paediatric
care. But to me, the thing thatI always talk about, and I used
to do use you anecdotally,Anthony, is just to talk about
how you've gone from yourclinical studies or you know,
your clinical work, but broughtwith it data science. And I
think that's always been afascinating thing. So if you
could talk maybe just to beginwith just a little bit about
(04:34):
your background and how you'vebrought the two together. I
think it's a wonderful examplefor everyone.
Anthony (04:40):
First of all, I've always been a math, computer,
chess nerd in early schoolyears. And that led to several
mentors that really emphasisemathematics to me, one was a
pathology professor at HarvardMedical School. Another one was
a heart surgeon. from myfellowship training. They
(05:01):
introduced me to complex mathand chaos theory and I was
already very enamoured withbiostatistics, which is not
common for clinician to actuallylike statistics. And I always
did my own statistics for mypapers. So I've always loved
math. And I think I told you thestory that when IBM Watson beat
(05:22):
the human contestant onJeopardy, was a really good wake
up call for me to go back toschool and learn the current
data science, which looksnothing like statistics that I
learned before. But it washelpful to have that background
going in. And after three and ahalf, long years of education
(05:42):
really gain a really valuableinsight into not just applying
data science to healthcare, butalso I think, change me
fundamentally, as a clinician,which I didn't expect, neither
did my programme director expectthat on my exit interview, I
told him that I didn't expectbeing changed as a clinician,
because as a cardiologist, andintensivist, as you can imagine,
(06:04):
I was always on system onethinking just reacting and fast
thinking. And three and a halfyears at Stanford taught me that
there's a lot of data scienceavailable now you can pump on
the brakes occasionally, if youcan to make a an even better
decision than you previouslythought. So it's been wonderful
for me in terms of thinking ofways to deploy data science and
(06:26):
AI to clinical medicine. Butalso, I think it fundamentally
changed me as a clinician.
Hannah (06:31):
So when David talks about Watson, I spent 18 years
of my career in IBM as well. SoI'm familiar with the story. But
he's always talked after thatthe early times of Jeopardy, and
kind of, which was exciting forso many people it was, it was
just a really brilliantillustration of, you know, maybe
where tech could be going. Butthen, yeah, I've heard him time
(06:53):
after time talking to colleaguesand customers about augmenting
decision making. And it wasreally easy in the earlier
years, I think more so than nowto, you know, for the press to
pick up on AI replacing ourjobs. And there's been a firm
kind of putting on the brakes ofactually, it's not about
necessarily about taking jobs,but as much as anything about
(07:15):
improving our decision makingabilities. When you said it
changed you as a clinician, isthat what you're suggesting that
he was giving you opportunity toreflect? Is that what you meant?
Or have I made that up?
Anthony (07:26):
Yes, no, you're spot on. I think most clinician,
especially seasoned clinicians,that are used to making rapid
fire decisions are alwaysworking on you know, Daniel
Kahneman system one thinkingthey're fast thinking, they're
reacting part of the brain. Insituations, I don't have to make
(07:47):
such a rapid decision, I'velearned to pump on the brakes,
and use more system to when thesituation allows that. But
wouldn't it be wonderful if evenin system one thinking
situations we are armed withsystem two already built in?
I've spent most of my career ina cardiac intensive care unit
for children, and would be sowonderful to have clinicians
(08:10):
that work in that setting, tohave the availability and to
leverage data science during thesystem one fast decision
process, so we have the best ofboth worlds, essentially. So
yeah, I think what what you gotfrom me is absolutely correct,
that I think we must try to getthis technology and methodology
into clinical medicine to makethe best decisions that we can,
(08:34):
every time, not just when we aresort of bringing our a game and
we're we're not tired. Lots oftimes clinicians are very
fatigued, or mired in asituation that they're just not
thinking creatively any longer.
So we need to relieve the burdenas much as we can. So that the
best part of the humanclinicians, which is the complex
problem solving, the creativeproblem solving can be really,
(08:55):
really unleashed as best as wecan.
David (09:00):
I think you and I've, again, discussed this
previously. And I know that thisis also a personal kind of quest
for you. But the thing from forme about AI is if I think about
when Oscar was in hospital isthat his doctors were ringing
around to colleagues on theother side of the world or
waiting for them to wake up. Soyou've got that kind of lag time
trying to see whether or notthey had seen a child with his
(09:23):
condition or any correlations inthere and the democratisation of
data has always been that kindof that driver for me in terms
of resetting my professionalcareer moving into healthcare
and thinking about AI and beingable to actually put the right
right data into the hands of theright people at the right time.
And that's kind of my drivingforce and you know, a lot of
(09:44):
what we're trying to do fromfrom thinking of Oscar
perspective, and I know thatit's very similar for you, but
why is it so important forpaediatrics in particular, and
what have you seen and how do wethink that we can start down
that journey and, and reallymake a difference?
Anthony (09:58):
Well, it is it Even more essential for paediatrics
because we have varying sizesfor patients. So many, many more
permutations of the sameproblem. We have rare diseases,
that are often undiagnosed ordiagnosed not in a timely
fashion. And we have so manymore permutations basically, of
(10:22):
even basic diseases. So wereally need democratisation of
data, information and knowledge.
So we had to first work onsharing data. However, we can
safely, however safely we needto get it done. And I think
we're getting closer to havemechanisms that will enable us
to do that, every institutionhas some hesitation to share
(10:42):
their data. But, you know,there's sort of more current
ways of thinking about that. Andone is, obviously increase the
security aspects. But also,there are mechanisms now, such
as federated learning where youactually push the models of AI
to the local data, keep thatlocal data where it is, and then
(11:03):
push the model parameters backcentrally to a meta model. So I
think that's another way we canget around the institutions not
being perfectly willing to sharetheir own institutional data.
And then I think once we solvethe the enigma of data sharing,
then I think the information andthe knowledge will come very
(11:26):
quickly, because the AImethodologies as you know,
David, is actually kind of aheadof schedule. So we're just not
leveraging that technologyenough. And it's mainly because
humans with the datarepositories are still trying to
figure it out how to share thisdata safely, in such a way that
(11:47):
we can take advantage of theamount of data. So you're
absolutely right, we need towork on the data sharing the
data governance of all theinstitutions, but in a way,
that's good, because we shouldbe doing that anyway. So the AI
is, I think, a tremendousNorthstar. For us to try to
reach to really improvehealthcare, the best reason I
can think of is to gaininsights, without having to go
(12:10):
to sort of the top children'shospitals by anyone's
definition, I have alwaysobjected to that kind of
ranking, and sort of puttingcertain children's hospitals in
the elite category. I think thatshould be democratised. I think
ranking is great for sports, butterrible for children's diseases
Hannah (12:35):
That is so interesting, the comment that you just made
about democratisation andapplying it to the hospitals and
children's access to care,because in the two series of
podcasts we've done so far, whenwe talked to people about what
would you like to fix, then thetopic that has come top of the
list every single time is aboutthe inequality of access to
(12:57):
great care, and you've justcycled back around it again. But
the question I had for you was,what was always had always
worried me was that what we weredescribing, leveraging our AI in
order to improve outcomes. And,and you talked about, you know,
unleashing the real value ofgreat clinicians and the human
side of that expertise. It justfelt like it was, you know, a
(13:19):
real dream and a vision ratherthan a reality. But what you're
describing feels more touchable.
So when do you think we are, youknow, is realistic to imagine
that there are going to bedifferent outcomes as a result
of more widespread availabilityof this type of technology and
shared insight.
Anthony (13:37):
On July 15 2028, I'm just kidding. I think it's a,
it's going to be a lifelongjourney, thinking that machine
and deep learning as good asthey are, are already showing a
little wear and tear in terms ofwhat it's capable of doing.
These are great statisticaltools. But what we really need
(13:59):
in a really big way, is all theclinicians to get involved
provide knowledge, insights,creativity into these sort of
knowledge machines. So that wecan really, really move AI
forward into what I call thecognition or the smart AI era.
(14:20):
And I think we'll see in ourlifetime, I think we'll see in a
decade or two, but it's going totake a lot of work. It's almost
like machine and deep learningis sort of the easy part of AI
is as simple as scalingcomplicated they can be, it's
still relativelystraightforward. Essentially,
these are labelers, right? Theylabel different things in
different situations. Buthopefully with the advent of
(14:41):
technologies like theTransformers that are around now
for natural language processing,and cognitive architectural
elements that can be built in tovarious aspects of deep
learning, and some considerationfor changing the way we share
data, the way we look atdatabases. I think, in this
(15:02):
decade, we can see some, Ithink, really, really big steps
forward. And looking at all ofthis as a really smart symphony
of tools. I just gave a talkjust the hour before to Ireland,
with clinician saying that weall need to learn how to code
and programme and I say, Well,no, just think that medicine up
(15:22):
to now has been like a stringensemble, but mainly string
instruments. Now we have theavailability of trumpets and
clarinets and drums, it doesn'tmean that the violinist needs to
drop their violin and learnthese new instruments, just
learn how to play with thoseother musicians so that we can
have a much bigger range ofmusic that we can play together.
(15:44):
So if you equate that to, youknow, the very complex chronic
disease patients, it's likeexpecting a string ensemble to
play Beethoven's Ninth Symphony,you just can't do it enough
justice. And you need otherinstruments. Actually,
Beethoven's Ninth has a singersas well, right. So you need as
many types of instruments as youcan to really bring that
(16:08):
Symphony to life. So I think theviolin is gonna stay in their
seats, you don't have to run outand learn how to programme and
code. Matter of fact, in someways, it's actually better that
you don't, because then you canactually not be distracted by a
new domain, but work with adomain experts, like data
scientists to actually work, youknow, very cohesively, and
(16:31):
harmoniously together to bringprojects to life.
David (16:35):
I absolutely love your analogy. And it does make me
think that the opportunity thatwe have now is actually to
capture that data, if we thinkback maybe even as 5-10 years
ago, and in some countries, theUK included, we're still not
capturing the data digitally.
So, you know, we talk aboutartificial intelligence, and
machine learning and all therest of it. But actually, unless
the data is there to beutilised, these systems can't
(16:57):
see it at all. So for ourchildren, for example, their
clinical data, as well as manyother many other aspects of
their life is going to be storedin a digital format there, you
know, what, what we call thedigital exhaust is going to be,
you know, far superior to us. Sotherefore, hopefully, there will
be that opportunity, but you'restill seeing medical
institutions not utilising thatand not being able to capture
(17:20):
that data. And, you know, a lotof the conversations I have in
my professional life is allabout signals, you know, being
able to spot signals, hopefully,before they cause any adverse,
you know, events. And that'sgoing to be so important for us
moving forward. The nextquestion, Anthony, is for
anybody listening to this, andwe get quite a few students, as
well as clinicians. What wouldyour advice be in terms of you
(17:42):
know, where to start? Whatshould they be looking to do?
Either just on an everydaybasis? And I guess you've
touched on that a little bit.
But one of the fortunate things,I think, that we have at the
minute is that we have atremendous amount of great
ideas, because we're seeing abit more of, you know, that kind
of democratisation of data andthe computer speeds necessary
(18:03):
in order to share that data. Butwhat would your advice be to you
know, paediatricians that arelooking to actually make a
difference?
Anthony (18:09):
Well, I think it's a very personal journey. And this
new era, I don't expect everyoneto just kind of become a
passionate advocate forartificial intelligence. I
think, as smart and as good adoctor as you can be, because
that's very valuable, especiallyin this coming decade, in
artificial intelligence, whenwe're looking for, you know,
sort of these cognitive elementsto build into the AI machinery,
(18:33):
it's going to really requirevery, very good doctors with
insights and wisdom that we canbasically find a way to
programme into these machinesthat you know, want to build for
decision making basically. Onthe one hand, we need better
curation and better organisationof data. On the other hand, we
(18:54):
also have to work in parallelways that we can do, accomplish
things, you know, withouthundreds of 1000s of patients of
the same disease category. Soit's always good to have both of
those strategies in place,because I think those two
strategies are very synergistic.
But I think for anyone that'slistening, I think, just follow
your passion. There's always away you can have your passion
(19:15):
and medicine and or data sciencebe used very wisely, in
particularly this coming decade.
This coming decade is going tobe I think, significantly more
challenging, because I think thelow hanging fruit was medical
image interpretation with deeplearning. And I think, the next
phase of taking on decisionmaking. So although the real
(19:39):
holy grail of clinical medicineis going to be here and i think
that challenges to solve thoseissues, and I think within a
decade or at most two decades,we won't really be calling
anything in medicine, artificialintelligence anymore, because
it's all embedded that just howwe practice. You know, this new
paradigm of clinical medicine,which is, I think, just hugely
(20:02):
exciting. I'm a little bitenvious of younger people these
days. But certainly I think thenext 25 years will probably be
the most exciting 25 years inmedicine ever. I see so many
signals and trends heading inthat direction. I think I'll
make medicine fun again, andmake medicine great again. And
also I think I'm predicting asopposed to my AI guru friends.
(20:26):
I'm predicting there'll be moredoctors and more radiologists
than ever before and not less.
Hannah (20:31):
And apart from the evolutions that you described
from a technical side. I wasalso mulling over, sort of how
do you get there. And what I'mthinking about was scale,
because AI works and assisteddecision making works, because
you have very large sets ofdata, where algorithms can be
trained, and in order that youcan derive insight from these
(20:52):
very, very large data sets. ButI'm thinking about the scale of
the data. And given that wedon't have the sharing on a
global scale, that it's going tobe most useful yet. Is this an
example where you can startsmall, because you can't start
that small? Because you're notgoing to have sufficient volumes
of data in order to deriveinsight? So for example, are
there things that you can do atan institutional level or I know
(21:16):
that I spy is one of thenetworks that you're extremely
involved in, for example? And sothen, are there ways of
collaborating across existingformal and informal networks
where somehow you get somevolumes that become valid?
Anthony (21:31):
I think all depends on the disease. But I think certain
diseases are so rare. And weneed as much data as we can for
those few patients, that youabsolutely probably going to
have to data share as much aspossible. So we have to think of
solutions with those types ofpatients, and then their
patients with diseases, they'renot so rare. And then you can, I
(21:53):
think he's still needed tocollect data that maybe not
absolutely essential that youhave, you know, 100 000's of
those patients records to learnfrom, but it's always good to
have more good quality data. Buton the other hand, as I said
before, we have to work inparallel strategies that will
(22:14):
enable us to do so in an AI typeof research. Without that many
patients and one of my areas ofinterest is how can you work
with little data. And part ofthe I think art of working with
little data is going to besomething that I've mentioned
before, which is bring incognitive elements to that data
(22:34):
set. So you're not entirelydependent on deep learning and
massive data sets, you canactually have cognitive
shortcuts that bypass lots ofdata. And I think the clinicians
will play a major role in thatthis decade in terms of
providing clinical insights anda knowledge that will really not
(22:56):
require treatment or diagnosis,with a lot of parameters and a
lot of data. So AI is going toget even more sophisticated.
It's not we're gonna head fromthe statistical era of machine
and deep learning to a cognitiveera. As you know, I'm a big fan
of IBM Watson and what it wastrying to accomplish, we just
(23:16):
need to continue that philosophyof the synergy between machine
and human intelligence is goingto be far better than either
machine or humans alone.
David (23:29):
Absolutely. During this this journey, you've started up
AI med, so people can find it.
And I'll put the link in theshow notes around it. But that
has enabled you to look at lotsand lots of different not just
paediatrics but lots ofdifferent walks within the
medical field and where AI canfit in. But my question is
during that time, or from yourown experience, can you talk
(23:49):
about learning from where we asin humans have failed, you know,
in the last few years, which hasactually meant that we can
accelerate? And we can learnfrom that
Anthony (24:00):
Great question. As I reflect back, I think maybe one
of the major takeaways is not tobe daunted by these level of
sophistication and the level ofdifficulty of artificial
intelligence. I think it'seasily distracting for a lot of
people to realise that theycan't code, they don't
(24:20):
understand IT. So they're kindof out of the loop, when in
fact, you should be in themiddle of a loop because as a
clinician, especially a seasonedclinician, that is the most
valuable resource, I think in AIin medicine. Obviously, data
scientists are not far behind.
And not to be daunted by thelevel of knowledge that you need
for AI but just take it in,learn a little bit at a time,
(24:44):
learn how this relates toclinical projects and problems
that need to be solved. And Ithink what we're missing and
this leads to the secondtakeaway from the last decade,
which is I think, where there'slots of great data science and
healthcare. But unfortunately,not nearly enough clinical
relevance that we need, I alwayssay the perfect model can have
(25:05):
zero impact. And we don't wantthat, obviously, we want maybe
good and great models with, youknow, moderate to great patient
impact. Because the perfectmodel, which I think a lot of
data scientists try to do, isalmost sometimes clinically
irrelevant. And 0.99, 0.98 onthe area under the curve doesn't
(25:28):
really impact on patient care,what we really need to focus is
on how we impact on patientcare, in terms of delivering
better outcomes. It's almostlike you need two scores, just
like you know, skatingcompetitions, you need a score
for technical and you need ascore for the art. I think we
should have two scores for AIprojects, you need a technical
(25:49):
score for the data science, andperhaps you need a clinical
relevance score. And thencombined is I think, what we
should do. As a matter of factI'm editor of a journal, maybe I
shouldn't insist on that for myown journal submissions, that we
score them on technical qualityas well as clinical relevance.
David (26:08):
I think that's a brilliant point. And actually. I
can't believe that, given theconversations that you and I've
had in the past, that it's takenus this long to talk about the
patient to a degree. And I thinkalways, certainly I know that
you always think this way. Andcertainly we do. But it's all
about the patient. It's allabout bringing the right
information, in order to makethe correct decision for the
patient, you know, rather thanjust playing with new shiny
(26:30):
tech,
Anthony (26:31):
But you may have noticed that peach 2040, as well
as AM meetings typically startswith a patient and story. That's
sort of the signature of mymeetings. Because I want
everyone to myself included, bythe way to remember that we are
talking about patients andfamilies and outcomes and life
and death. We're not talkingabout support vector machines,
(26:54):
and deep learning. We aretalking about all of these
things. And as you may remember,I became a patient myself a year
and a half ago. And, you know,pulmonary edoema is a string of
two words that you can use datascience to find patients. But I
realised that whenever we findpulmonary edoema, in a chart,
that patient suffered greatlybecause of that term, you know,
(27:16):
so it really adds a emotionaland human weight to those terms.
They're not just kind of searchterms for data science projects,
people paid greatly for thatdata and those vocabulary words
that really, really are part ofany project, but people pay the
price for those.
David (27:36):
We've spoken a little bit more, you mentioned that there
was initiative that you startedPedes 2014. So looking kind of,
you know, 20-25 years out, butjust briefly, what do you think
medicine is gonna look like bythe end of this decade? So 2030?
What do you what do you think wewill be,
Anthony (27:52):
I think we will start to see bigger dividends from
data science, we will start tosee true elements of precision
medicine. We will see moreinstitutions willing to share
data, and work towards solvingcommon problems and work on
common goals, more and more. Ithink in a way, it took a
(28:14):
pandemic for us to realise that,you know, the humans really need
to work together much closerthan we've been, especially for
children. Thankfully, thispandemic has not affected great
numbers of children. But if itdid, this aspect of sharing data
and working together will beeven more of a mandate from all
of us. So I do see very positivetrends for the future and very
(28:39):
optimistic about the future. Andyou know, what's encouraging, at
least here in the US more peopleare interested in going to
medicine at all levels than everbefore. It's almost like this
pandemic has been a clarion callfor everyone to get involved in
health care. So I do see greatpromise for healthcare and
medicine for the next 10 to 20years.
David (29:01):
Yeah, and Fingers crossed. You know, a lot more
people want to go intopaediatrics as well, as a result
of that. And interestingly, Iwas hosting a panel for the
Royal College of paediatrics inthe UK and Ian Hennessey here, I
think you know, it all the hayactually started to think about
what were the implications ofbeing if COVID had been
reversed. So actually, it waschildren that had been infected,
(29:22):
not adults. How would we havecoped with that? And how would
we have done it? And it's,obviously it's a horrific
thought. But I think askingthose questions and turning
those scenarios on their headand trying to be prepared for it
is obviously key.
Anthony (29:36):
I don't think we should have like wargames equivalent
for a pandemic that affectsmainly children. I think that
would be an amazing idea andproject and a wake up call for
all of us now, what if insteadof seeing older people dying on
ventilators, that only childrenare in those ICU's? And we
actually a few of us actuallytalked about that possibility.
(29:56):
You know, what if we ran out ofventilators for children, it was
a horrible shock for us to evenhave that for adults. But I can
imagine for children, it will bea very, very terrible world to
live live through to see thatever. So that means we need to
be smarter, smarter than theviruses, which is not easy. The
viruses are actually on the AIside of really, really good
(30:19):
complex adaptive system. Andthey're pretty. There's the
certain intelligence to thatsystem. So we need to be
smarter.
David (30:26):
Anthony thank you so much for joining us. We ask one final
question, if you don't mind,which is if you could change
anything in paediatrics, whatwould it be?
Anthony (30:34):
I would get rid of anything that puts institutional
hubris and agenda ahead of thechildren. So we'll get rid of
ranking systems, I would makesure that every hospital raises
their game, and we democratisehigh quality care as much as
possible and it may be againsthospital marketing strategies.
(30:55):
But I think we ought to putinstitutional agendas aside and
put children first and let'srank diseases rather than
hospitals. I think that will bea great change in philosophy and
strategy for us and children arethe future. So we absolutely
need to have a very differentmindset for children.
David (31:14):
Totally agree. And I think on that note, your girls
are probably getting restless.
It's about quality nine at nightfor us, and we can hear the
pitter patter of tiny feetupstairs, we better go and check
that our children are in bed.
But thank you so much, as alwaysa pleasure to speak with you.
You know, thank you foreverything that you're doing and
really fingers crossed that wecan we can meet up in the not
(31:34):
too distant future as soon aswe're able to travel again.
Yes, thank you so much David andHannah. Thank you.
Thank you so much to Dr. Changfor joining us on this week's
not mini adults podcast. We wehad such a wonderful time and we
really hope that you enjoyed theconversation to next week feels
like a doubleheader because weare delighted to say that we
(31:55):
have one of Dr. Chang's greatfriends, Timothy Chu, who will
be joining us to talk about hisproject around bringing a
paediatric health cloud tohospitals all over the world. We
really hope that you can join usthen please do subscribe to the
podcast. And if you're enjoyingit, please do leave us a review
as well. We hope you'll join usagain next week.
Medicine up to now has been like a string ensemble, but
(00:03):
mainly string instruments. Nowwe have the availability of
trumpets and clarinets anddrums. It doesn't mean that the
violinist needs to drop theirviolin and learn these new
instruments. Just learn how toplay with those other musicians
so that we can have a muchbigger range of music that we
can play together. So if youequate that to, you know, the
(00:26):
very complex chronic diseasepatients, it's like expecting a
string ensemble to playBeethoven's Ninth Symphony, he
just can't do it enough justice.
And you need other instruments.
Actually, Beethoven's Ninth hassingers as well, right. So you
need as many types ofinstruments as you can to really
bring that Symphony to life.
David (00:53):
Welcome back to the not mini adults podcast pioneers for
children's health care andwellbeing. My name is David
Cole. And I'm joined by my wife,Hannah. And together we are the
co founders of UK children'scharity, Thinking of Oscar, it's
been quite a year for us, as I'msure it has for many of you. And
we're sorry, it's taken us awhile to come back with our
third season of the podcast.
We're so grateful to everybodywho has downloaded, shared and
(01:15):
enjoyed the pod. And we'rereally delighted that so many
medical students have chosen tolisten and learn more about some
of the amazing people that wehave been lucky enough to talk
to. We have what we hope youwill agree is a wonderful set of
conversations coming up over thecoming weeks. And once again, we
are humbled to be able to sharethese conversations with
everyone. We started thispodcast in 2020 with three aims,
(01:35):
firstly, to continue to educateourselves, so that when we're
asked to support a project,we're able to be in the best
position to do so. Secondly, westrongly believe that you can
change the world by sharing yourstory, and therefore, we wish to
share as many stories aspossible. And finally, we hope
that those listening will beinspired to want to help to do
more in children's health.
(01:58):
Before we get going with thisweek's episode, we want to say a
quick thank you to our commsteam who have worked throughout
the start of 2021, helping tospread the word of the podcast
and share the stories that we'vebeen lucky enough to discuss. So
thank you to Susie, Annie, andCarrie. And now without further
ado, let's discuss our guestthis week. You heard a clip from
him earlier and those that knowhim will know that we are
(02:19):
delighted to welcome Dr. AnthonyChang to the not many add ons
podcast to kick off seasonthree. Dr. Chang is the chief
intelligence and InnovationOfficer as well as medical
director of the heart failureprogramme at Children's Hospital
of Orange County, and he foundedthe medical intelligence and
Innovation Institute, MI3 atchoc children's also, Dr. Chang
holds an MD from Georgetown, anMPH from UCLA, an MBA from the
(02:44):
University of Miami, and alsoholds a certificate from MIT on
AI. And finally, he has an MS inbiomedical data science from
Stanford. Anthony has beencalled Dr. Ai by the Chicago
Tribune, having publishednumerous peer reviewed papers in
machine learning and artificialintelligence related to
medicine. He is passionate abouthow AI could transform
(03:07):
healthcare and works tirelesslyin advancing the use of
artificial intelligence. Dr.
Chang is also one of theoriginal founders of the
International Society forpaediatric innovation. And to
me, is the godfather ofpaediatric innovation. Anthony
is most importantly, a father oftwo young girls, who he also
discusses during ourconversation, we can think of no
one better to kick off our thirdseason of the not mini adults
(03:30):
podcast, and we hope you enjoyour conversation as much as we
did. Anthony, Hi, thank you somuch for joining us on not mini
adults podcast.
Anthony (03:43):
Thank you very much, David and Anna, and I just want
to publicly thank you foreverything that you do to help
children around the world. It'sphenomenal work. So thank you
David (03:52):
Thank you. So I mean, a lot of the stuff that we try and
do is actually been inspired bythe work that you have done with
I spy, and we'll talk a littlebit about that. But you know,
you and I met a few years ago atthe peace 2014 conference, which
was a massive eye opener for usin terms of understanding just
what the world is looking for,what the world is doing and the
(04:12):
importance of bringinginnovation into into paediatric
care. But to me, the thing thatI always talk about, and I used
to do use you anecdotally,Anthony, is just to talk about
how you've gone from yourclinical studies or you know,
your clinical work, but broughtwith it data science. And I
think that's always been afascinating thing. So if you
could talk maybe just to beginwith just a little bit about
(04:34):
your background and how you'vebrought the two together. I
think it's a wonderful examplefor everyone.
Anthony (04:40):
First of all, I've always been a math, computer,
chess nerd in early schoolyears. And that led to several
mentors that really emphasisemathematics to me, one was a
pathology professor at HarvardMedical School. Another one was
a heart surgeon. from myfellowship training. They
(05:01):
introduced me to complex mathand chaos theory and I was
already very enamoured withbiostatistics, which is not
common for clinician to actuallylike statistics. And I always
did my own statistics for mypapers. So I've always loved
math. And I think I told you thestory that when IBM Watson beat
(05:22):
the human contestant onJeopardy, was a really good wake
up call for me to go back toschool and learn the current
data science, which looksnothing like statistics that I
learned before. But it washelpful to have that background
going in. And after three and ahalf, long years of education
(05:42):
really gain a really valuableinsight into not just applying
data science to healthcare, butalso I think, change me
fundamentally, as a clinician,which I didn't expect, neither
did my programme director expectthat on my exit interview, I
told him that I didn't expectbeing changed as a clinician,
because as a cardiologist, andintensivist, as you can imagine,
(06:04):
I was always on system onethinking just reacting and fast
thinking. And three and a halfyears at Stanford taught me that
there's a lot of data scienceavailable now you can pump on
the brakes occasionally, if youcan to make a an even better
decision than you previouslythought. So it's been wonderful
for me in terms of thinking ofways to deploy data science and
(06:26):
AI to clinical medicine. Butalso, I think it fundamentally
changed me as a clinician.
Hannah (06:31):
So when David talks about Watson, I spent 18 years
of my career in IBM as well. SoI'm familiar with the story. But
he's always talked after thatthe early times of Jeopardy, and
kind of, which was exciting forso many people it was, it was
just a really brilliantillustration of, you know, maybe
where tech could be going. Butthen, yeah, I've heard him time
(06:53):
after time talking to colleaguesand customers about augmenting
decision making. And it wasreally easy in the earlier
years, I think more so than nowto, you know, for the press to
pick up on AI replacing ourjobs. And there's been a firm
kind of putting on the brakes ofactually, it's not about
necessarily about taking jobs,but as much as anything about
(07:15):
improving our decision makingabilities. When you said it
changed you as a clinician, isthat what you're suggesting that
he was giving you opportunity toreflect? Is that what you meant?
Or have I made that up?
Anthony (07:26):
Yes, no, you're spot on. I think most clinician,
especially seasoned clinicians,that are used to making rapid
fire decisions are alwaysworking on you know, Daniel
Kahneman system one thinkingthey're fast thinking, they're
reacting part of the brain. Insituations, I don't have to make
(07:47):
such a rapid decision, I'velearned to pump on the brakes,
and use more system to when thesituation allows that. But
wouldn't it be wonderful if evenin system one thinking
situations we are armed withsystem two already built in?
I've spent most of my career ina cardiac intensive care unit
for children, and would be sowonderful to have clinicians
(08:10):
that work in that setting, tohave the availability and to
leverage data science during thesystem one fast decision
process, so we have the best ofboth worlds, essentially. So
yeah, I think what what you gotfrom me is absolutely correct,
that I think we must try to getthis technology and methodology
into clinical medicine to makethe best decisions that we can,
(08:34):
every time, not just when we aresort of bringing our a game and
we're we're not tired. Lots oftimes clinicians are very
fatigued, or mired in asituation that they're just not
thinking creatively any longer.
So we need to relieve the burdenas much as we can. So that the
best part of the humanclinicians, which is the complex
problem solving, the creativeproblem solving can be really,
(08:55):
really unleashed as best as wecan.
David (09:00):
I think you and I've, again, discussed this
previously. And I know that thisis also a personal kind of quest
for you. But the thing from forme about AI is if I think about
when Oscar was in hospital isthat his doctors were ringing
around to colleagues on theother side of the world or
waiting for them to wake up. Soyou've got that kind of lag time
trying to see whether or notthey had seen a child with his
(09:23):
condition or any correlations inthere and the democratisation of
data has always been that kindof that driver for me in terms
of resetting my professionalcareer moving into healthcare
and thinking about AI and beingable to actually put the right
right data into the hands of theright people at the right time.
And that's kind of my drivingforce and you know, a lot of
(09:44):
what we're trying to do fromfrom thinking of Oscar
perspective, and I know thatit's very similar for you, but
why is it so important forpaediatrics in particular, and
what have you seen and how do wethink that we can start down
that journey and, and reallymake a difference?
Anthony (09:58):
Well, it is it Even more essential for paediatrics
because we have varying sizesfor patients. So many, many more
permutations of the sameproblem. We have rare diseases,
that are often undiagnosed ordiagnosed not in a timely
fashion. And we have so manymore permutations basically, of
(10:22):
even basic diseases. So wereally need democratisation of
data, information and knowledge.
So we had to first work onsharing data. However, we can
safely, however safely we needto get it done. And I think
we're getting closer to havemechanisms that will enable us
to do that, every institutionhas some hesitation to share
(10:42):
their data. But, you know,there's sort of more current
ways of thinking about that. Andone is, obviously increase the
security aspects. But also,there are mechanisms now, such
as federated learning where youactually push the models of AI
to the local data, keep thatlocal data where it is, and then
(11:03):
push the model parameters backcentrally to a meta model. So I
think that's another way we canget around the institutions not
being perfectly willing to sharetheir own institutional data.
And then I think once we solvethe the enigma of data sharing,
then I think the information andthe knowledge will come very
(11:26):
quickly, because the AImethodologies as you know,
David, is actually kind of aheadof schedule. So we're just not
leveraging that technologyenough. And it's mainly because
humans with the datarepositories are still trying to
figure it out how to share thisdata safely, in such a way that
(11:47):
we can take advantage of theamount of data. So you're
absolutely right, we need towork on the data sharing the
data governance of all theinstitutions, but in a way,
that's good, because we shouldbe doing that anyway. So the AI
is, I think, a tremendousNorthstar. For us to try to
reach to really improvehealthcare, the best reason I
can think of is to gaininsights, without having to go
(12:10):
to sort of the top children'shospitals by anyone's
definition, I have alwaysobjected to that kind of
ranking, and sort of puttingcertain children's hospitals in
the elite category. I think thatshould be democratised. I think
ranking is great for sports, butterrible for children's diseases
Hannah (12:35):
That is so interesting, the comment that you just made
about democratisation andapplying it to the hospitals and
children's access to care,because in the two series of
podcasts we've done so far, whenwe talked to people about what
would you like to fix, then thetopic that has come top of the
list every single time is aboutthe inequality of access to
(12:57):
great care, and you've justcycled back around it again. But
the question I had for you was,what was always had always
worried me was that what we weredescribing, leveraging our AI in
order to improve outcomes. And,and you talked about, you know,
unleashing the real value ofgreat clinicians and the human
side of that expertise. It justfelt like it was, you know, a
(13:19):
real dream and a vision ratherthan a reality. But what you're
describing feels more touchable.
So when do you think we are, youknow, is realistic to imagine
that there are going to bedifferent outcomes as a result
of more widespread availabilityof this type of technology and
shared insight.
Anthony (13:37):
On July 15 2028, I'm just kidding. I think it's a,
it's going to be a lifelongjourney, thinking that machine
and deep learning as good asthey are, are already showing a
little wear and tear in terms ofwhat it's capable of doing.
These are great statisticaltools. But what we really need
(13:59):
in a really big way, is all theclinicians to get involved
provide knowledge, insights,creativity into these sort of
knowledge machines. So that wecan really, really move AI
forward into what I call thecognition or the smart AI era.
(14:20):
And I think we'll see in ourlifetime, I think we'll see in a
decade or two, but it's going totake a lot of work. It's almost
like machine and deep learningis sort of the easy part of AI
is as simple as scalingcomplicated they can be, it's
still relativelystraightforward. Essentially,
these are labelers, right? Theylabel different things in
different situations. Buthopefully with the advent of
(14:41):
technologies like theTransformers that are around now
for natural language processing,and cognitive architectural
elements that can be built in tovarious aspects of deep
learning, and some considerationfor changing the way we share
data, the way we look atdatabases. I think, in this
(15:02):
decade, we can see some, Ithink, really, really big steps
forward. And looking at all ofthis as a really smart symphony
of tools. I just gave a talkjust the hour before to Ireland,
with clinician saying that weall need to learn how to code
and programme and I say, Well,no, just think that medicine up
(15:22):
to now has been like a stringensemble, but mainly string
instruments. Now we have theavailability of trumpets and
clarinets and drums, it doesn'tmean that the violinist needs to
drop their violin and learnthese new instruments, just
learn how to play with thoseother musicians so that we can
have a much bigger range ofmusic that we can play together.
(15:44):
So if you equate that to, youknow, the very complex chronic
disease patients, it's likeexpecting a string ensemble to
play Beethoven's Ninth Symphony,you just can't do it enough
justice. And you need otherinstruments. Actually,
Beethoven's Ninth has a singersas well, right. So you need as
many types of instruments as youcan to really bring that
(16:08):
Symphony to life. So I think theviolin is gonna stay in their
seats, you don't have to run outand learn how to programme and
code. Matter of fact, in someways, it's actually better that
you don't, because then you canactually not be distracted by a
new domain, but work with adomain experts, like data
scientists to actually work, youknow, very cohesively, and
(16:31):
harmoniously together to bringprojects to life.
David (16:35):
I absolutely love your analogy. And it does make me
think that the opportunity thatwe have now is actually to
capture that data, if we thinkback maybe even as 5-10 years
ago, and in some countries, theUK included, we're still not
capturing the data digitally.
So, you know, we talk aboutartificial intelligence, and
machine learning and all therest of it. But actually, unless
the data is there to beutilised, these systems can't
(16:57):
see it at all. So for ourchildren, for example, their
clinical data, as well as manyother many other aspects of
their life is going to be storedin a digital format there, you
know, what, what we call thedigital exhaust is going to be,
you know, far superior to us. Sotherefore, hopefully, there will
be that opportunity, but you'restill seeing medical
institutions not utilising thatand not being able to capture
(17:20):
that data. And, you know, a lotof the conversations I have in
my professional life is allabout signals, you know, being
able to spot signals, hopefully,before they cause any adverse,
you know, events. And that'sgoing to be so important for us
moving forward. The nextquestion, Anthony, is for
anybody listening to this, andwe get quite a few students, as
well as clinicians. What wouldyour advice be in terms of you
(17:42):
know, where to start? Whatshould they be looking to do?
Either just on an everydaybasis? And I guess you've
touched on that a little bit.
But one of the fortunate things,I think, that we have at the
minute is that we have atremendous amount of great
ideas, because we're seeing abit more of, you know, that kind
of democratisation of data andthe computer speeds necessary
(18:03):
in order to share that data. Butwhat would your advice be to you
know, paediatricians that arelooking to actually make a
difference?
Anthony (18:09):
Well, I think it's a very personal journey. And this
new era, I don't expect everyoneto just kind of become a
passionate advocate forartificial intelligence. I
think, as smart and as good adoctor as you can be, because
that's very valuable, especiallyin this coming decade, in
artificial intelligence, whenwe're looking for, you know,
sort of these cognitive elementsto build into the AI machinery,
(18:33):
it's going to really requirevery, very good doctors with
insights and wisdom that we canbasically find a way to
programme into these machinesthat you know, want to build for
decision making basically. Onthe one hand, we need better
curation and better organisationof data. On the other hand, we
(18:54):
also have to work in parallelways that we can do, accomplish
things, you know, withouthundreds of 1000s of patients of
the same disease category. Soit's always good to have both of
those strategies in place,because I think those two
strategies are very synergistic.
But I think for anyone that'slistening, I think, just follow
your passion. There's always away you can have your passion
(19:15):
and medicine and or data sciencebe used very wisely, in
particularly this coming decade.
This coming decade is going tobe I think, significantly more
challenging, because I think thelow hanging fruit was medical
image interpretation with deeplearning. And I think, the next
phase of taking on decisionmaking. So although the real
(19:39):
holy grail of clinical medicineis going to be here and i think
that challenges to solve thoseissues, and I think within a
decade or at most two decades,we won't really be calling
anything in medicine, artificialintelligence anymore, because
it's all embedded that just howwe practice. You know, this new
paradigm of clinical medicine,which is, I think, just hugely
(20:02):
exciting. I'm a little bitenvious of younger people these
days. But certainly I think thenext 25 years will probably be
the most exciting 25 years inmedicine ever. I see so many
signals and trends heading inthat direction. I think I'll
make medicine fun again, andmake medicine great again. And
also I think I'm predicting asopposed to my AI guru friends.
(20:26):
I'm predicting there'll be moredoctors and more radiologists
than ever before and not less.
Hannah (20:31):
And apart from the evolutions that you described
from a technical side. I wasalso mulling over, sort of how
do you get there. And what I'mthinking about was scale,
because AI works and assisteddecision making works, because
you have very large sets ofdata, where algorithms can be
trained, and in order that youcan derive insight from these
(20:52):
very, very large data sets. ButI'm thinking about the scale of
the data. And given that wedon't have the sharing on a
global scale, that it's going tobe most useful yet. Is this an
example where you can startsmall, because you can't start
that small? Because you're notgoing to have sufficient volumes
of data in order to deriveinsight? So for example, are
there things that you can do atan institutional level or I know
(21:16):
that I spy is one of thenetworks that you're extremely
involved in, for example? And sothen, are there ways of
collaborating across existingformal and informal networks
where somehow you get somevolumes that become valid?
Anthony (21:31):
I think all depends on the disease. But I think certain
diseases are so rare. And weneed as much data as we can for
those few patients, that youabsolutely probably going to
have to data share as much aspossible. So we have to think of
solutions with those types ofpatients, and then their
patients with diseases, they'renot so rare. And then you can, I
(21:53):
think he's still needed tocollect data that maybe not
absolutely essential that youhave, you know, 100 000's of
those patients records to learnfrom, but it's always good to
have more good quality data. Buton the other hand, as I said
before, we have to work inparallel strategies that will
(22:14):
enable us to do so in an AI typeof research. Without that many
patients and one of my areas ofinterest is how can you work
with little data. And part ofthe I think art of working with
little data is going to besomething that I've mentioned
before, which is bring incognitive elements to that data
(22:34):
set. So you're not entirelydependent on deep learning and
massive data sets, you canactually have cognitive
shortcuts that bypass lots ofdata. And I think the clinicians
will play a major role in thatthis decade in terms of
providing clinical insights anda knowledge that will really not
(22:56):
require treatment or diagnosis,with a lot of parameters and a
lot of data. So AI is going toget even more sophisticated.
It's not we're gonna head fromthe statistical era of machine
and deep learning to a cognitiveera. As you know, I'm a big fan
of IBM Watson and what it wastrying to accomplish, we just
(23:16):
need to continue that philosophyof the synergy between machine
and human intelligence is goingto be far better than either
machine or humans alone.
David (23:29):
Absolutely. During this this journey, you've started up
AI med, so people can find it.
And I'll put the link in theshow notes around it. But that
has enabled you to look at lotsand lots of different not just
paediatrics but lots ofdifferent walks within the
medical field and where AI canfit in. But my question is
during that time, or from yourown experience, can you talk
(23:49):
about learning from where we asin humans have failed, you know,
in the last few years, which hasactually meant that we can
accelerate? And we can learnfrom that
Anthony (24:00):
Great question. As I reflect back, I think maybe one
of the major takeaways is not tobe daunted by these level of
sophistication and the level ofdifficulty of artificial
intelligence. I think it'seasily distracting for a lot of
people to realise that theycan't code, they don't
(24:20):
understand IT. So they're kindof out of the loop, when in
fact, you should be in themiddle of a loop because as a
clinician, especially a seasonedclinician, that is the most
valuable resource, I think in AIin medicine. Obviously, data
scientists are not far behind.
And not to be daunted by thelevel of knowledge that you need
for AI but just take it in,learn a little bit at a time,
(24:44):
learn how this relates toclinical projects and problems
that need to be solved. And Ithink what we're missing and
this leads to the secondtakeaway from the last decade,
which is I think, where there'slots of great data science and
healthcare. But unfortunately,not nearly enough clinical
relevance that we need, I alwayssay the perfect model can have
(25:05):
zero impact. And we don't wantthat, obviously, we want maybe
good and great models with, youknow, moderate to great patient
impact. Because the perfectmodel, which I think a lot of
data scientists try to do, isalmost sometimes clinically
irrelevant. And 0.99, 0.98 onthe area under the curve doesn't
(25:28):
really impact on patient care,what we really need to focus is
on how we impact on patientcare, in terms of delivering
better outcomes. It's almostlike you need two scores, just
like you know, skatingcompetitions, you need a score
for technical and you need ascore for the art. I think we
should have two scores for AIprojects, you need a technical
(25:49):
score for the data science, andperhaps you need a clinical
relevance score. And thencombined is I think, what we
should do. As a matter of factI'm editor of a journal, maybe I
shouldn't insist on that for myown journal submissions, that we
score them on technical qualityas well as clinical relevance.
David (26:08):
I think that's a brilliant point. And actually. I
can't believe that, given theconversations that you and I've
had in the past, that it's takenus this long to talk about the
patient to a degree. And I thinkalways, certainly I know that
you always think this way. Andcertainly we do. But it's all
about the patient. It's allabout bringing the right
information, in order to makethe correct decision for the
patient, you know, rather thanjust playing with new shiny
(26:30):
tech,
Anthony (26:31):
But you may have noticed that peach 2040, as well
as AM meetings typically startswith a patient and story. That's
sort of the signature of mymeetings. Because I want
everyone to myself included, bythe way to remember that we are
talking about patients andfamilies and outcomes and life
and death. We're not talkingabout support vector machines,
(26:54):
and deep learning. We aretalking about all of these
things. And as you may remember,I became a patient myself a year
and a half ago. And, you know,pulmonary edoema is a string of
two words that you can use datascience to find patients. But I
realised that whenever we findpulmonary edoema, in a chart,
that patient suffered greatlybecause of that term, you know,
(27:16):
so it really adds a emotionaland human weight to those terms.
They're not just kind of searchterms for data science projects,
people paid greatly for thatdata and those vocabulary words
that really, really are part ofany project, but people pay the
price for those.
David (27:36):
We've spoken a little bit more, you mentioned that there
was initiative that you startedPedes 2014. So looking kind of,
you know, 20-25 years out, butjust briefly, what do you think
medicine is gonna look like bythe end of this decade? So 2030?
What do you what do you think wewill be,
Anthony (27:52):
I think we will start to see bigger dividends from
data science, we will start tosee true elements of precision
medicine. We will see moreinstitutions willing to share
data, and work towards solvingcommon problems and work on
common goals, more and more. Ithink in a way, it took a
(28:14):
pandemic for us to realise that,you know, the humans really need
to work together much closerthan we've been, especially for
children. Thankfully, thispandemic has not affected great
numbers of children. But if itdid, this aspect of sharing data
and working together will beeven more of a mandate from all
of us. So I do see very positivetrends for the future and very
(28:39):
optimistic about the future. Andyou know, what's encouraging, at
least here in the US more peopleare interested in going to
medicine at all levels than everbefore. It's almost like this
pandemic has been a clarion callfor everyone to get involved in
health care. So I do see greatpromise for healthcare and
medicine for the next 10 to 20years.
David (29:01):
Yeah, and Fingers crossed. You know, a lot more
people want to go intopaediatrics as well, as a result
of that. And interestingly, Iwas hosting a panel for the
Royal College of paediatrics inthe UK and Ian Hennessey here, I
think you know, it all the hayactually started to think about
what were the implications ofbeing if COVID had been
reversed. So actually, it waschildren that had been infected,
(29:22):
not adults. How would we havecoped with that? And how would
we have done it? And it's,obviously it's a horrific
thought. But I think askingthose questions and turning
those scenarios on their headand trying to be prepared for it
is obviously key.
Anthony (29:36):
I don't think we should have like wargames equivalent
for a pandemic that affectsmainly children. I think that
would be an amazing idea andproject and a wake up call for
all of us now, what if insteadof seeing older people dying on
ventilators, that only childrenare in those ICU's? And we
actually a few of us actuallytalked about that possibility.
(29:56):
You know, what if we ran out ofventilators for children, it was
a horrible shock for us to evenhave that for adults. But I can
imagine for children, it will bea very, very terrible world to
live live through to see thatever. So that means we need to
be smarter, smarter than theviruses, which is not easy. The
viruses are actually on the AIside of really, really good
(30:19):
complex adaptive system. Andthey're pretty. There's the
certain intelligence to thatsystem. So we need to be
smarter.
David (30:26):
Anthony thank you so much for joining us. We ask one final
question, if you don't mind,which is if you could change
anything in paediatrics, whatwould it be?
Anthony (30:34):
I would get rid of anything that puts institutional
hubris and agenda ahead of thechildren. So we'll get rid of
ranking systems, I would makesure that every hospital raises
their game, and we democratisehigh quality care as much as
possible and it may be againsthospital marketing strategies.
(30:55):
But I think we ought to putinstitutional agendas aside and
put children first and let'srank diseases rather than
hospitals. I think that will bea great change in philosophy and
strategy for us and children arethe future. So we absolutely
need to have a very differentmindset for children.
David (31:14):
Totally agree. And I think on that note, your girls
are probably getting restless.
It's about quality nine at nightfor us, and we can hear the
pitter patter of tiny feetupstairs, we better go and check
that our children are in bed.
But thank you so much, as alwaysa pleasure to speak with you.
You know, thank you foreverything that you're doing and
really fingers crossed that wecan we can meet up in the not
(31:34):
too distant future as soon aswe're able to travel again.
Yes, thank you so much David andHannah. Thank you.
Thank you so much to Dr. Changfor joining us on this week's
not mini adults podcast. We wehad such a wonderful time and we
really hope that you enjoyed theconversation to next week feels
like a doubleheader because weare delighted to say that we
(31:55):
have one of Dr. Chang's greatfriends, Timothy Chu, who will
be joining us to talk about hisproject around bringing a
paediatric health cloud tohospitals all over the world. We
really hope that you can join usthen please do subscribe to the
podcast. And if you're enjoyingit, please do leave us a review
as well. We hope you'll join usagain next week.
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