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welcome welcome to the analytics at Warton AI at Wharton podcast series on artificial intelligence my name is Eric
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bradow professor of marketing statistics and data science here at the Wharton School I'm also the vice dean of
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analytics and I'm the one that's been hosting this podcast series today's
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episode is on an area that you know I've said it many times even when my two
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guests are not here I think that artificial intelligence and machine learning in combination with industry is
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going to solve the healthcare problems we have today Ai and Healthcare is such an important area and I can't imagine
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two colleagues better to talk with me about that topic first I have my colleague Marissa King Marissa is the
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Alis y hung president's distinguished professor at the Wharton School uh her
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research has significantly contributed to our understanding of a wide range of pressing Healthcare issues ranging from
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prescription drug abuse crisis to clinician burnout Welcome to our podcast it's a pleasure to be here and then next
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and certainly last last not last but not least is my friend and colleague Hamza bastani Hamza is associate professor
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operations information decisions as well as a colleague in the statistics and data science department her research
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focuses on developing machine learning algorithms for Learning and optimization in healthcare and as I know very well
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because I've interviewed her for other podcasts uh she's done a lot of work
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with Greece and Sierra Leon to deploy algorithms at a Countrywide scale Hamza welcome to the podcast as well thanks so
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much for having me so let me start with the beginning maybe uh Marissa I'll start with you um for those people that
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aren't familiar with the applications of AI and Healthcare what are they if you
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could just give us a broad overview of the kinds of problems in healthcare people are trying to use artificial
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intelligence to use and uh to solve and maybe it's in combination with machine
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learning and other types of algorithms yeah machine learning and artificial intelligence have touched
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almost all aspects of healthcare at this point if you think of everything from who and how you get reminders to pick up
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prescriptions uh from who's reading your Radiology reports to even how you're
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being triaged in the emergency department machine learning plays a key role in all of those facets so why don't
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maybe before I jump to Hamza here let me ask you a question so um are the reminders that we're all getting are
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those being determined using some optimal algorithm um is triage being done in a more like not entirely 100%
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human basis and um in terms of who is reading my charts um like is that being done a lot in an automated way so maybe
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just to give us a baseline there yeah and pretty much in every one of those applications machine learning and AI is
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playing a critical role so when you get those reminders it's almost certainly
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coming from an AI powered reminder the same is true if you're thinking about uh
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La reminders to pick up labs and get your blood work done so that's the point
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of care for for patients but clinicians are also starting to deploy this for a wide range of uses if you think about
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Radiology reports that's arguably the place where AI had the greatest penetration so many many of our
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Radiology reports are read Now by machines and then finally if we think about triaging in the emergency
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department that's another important area of application that's really reducing
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overall length of stay within the emergency department and we know that when length of stay is reduced that has
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important implications from everything from um complications to long-term mortality so AI is already playing a
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critical role in a lot of domains of healthcare so Hamza as someone who's I know both a statistical methodologist
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but also cares about the practical application of the work how do you think about the kinds of problems that Marissa
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just laid out like do you try to solve them in some idic setting do you try to solve them with real data do you try to
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kind of develop algorithms and then you know try to run field experiments actually launch them in the field how do
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you think about your role as similar to me as we're statistical methodologists
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how do you think about your role in helping solve these problems I think it has to start with the data so I think in
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healthcare there's a lot of variation so only recently in the last couple decades
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have we started digitizing the whole health record but even now like EKG readings for example aren't digitized in
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most health systems and so um I think the first thing we need to figure out is is this a use case where an algorithm
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given the data that is digitized is able to do better than a human or at least comparably to a human in a way that's
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Equitable and also like transports well to other Health Systems um and if that's
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the case then we start thinking about algorithms and rcts because there's lots
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of other things that come up like whether we're able to effectively integrate it into the workflow whether
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there's Buy in from stakeholders and so on but I think it has to start with the
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data so Marissa Hamza said a lot of things I want to ask you about let me let me fire these through in a rapid
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fire kind of way how much are people in Industry worried about Equity it's easy to say you're you care
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about it but how much does it impact like let's imagine I could have an an algorithm that improves the outcomes for
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some population of people but not others it's not Equitable but it's still from a
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population perspective it could still benefit Society how are people thinking about Equity yeah I mean I think it's
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certainly a point of concern in large part because some of the key issues arising around Equity with algorithms
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that have already been deployed have been made quite public um but at the same time I do think it's a second order
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consideration and that's going to I think have a really important long-term business implications because in the
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long run as businesses start to deploy these algorithms um when issues around equity come to light I think it's going
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to have really significant impact for their bottom line so I think in very short order it will go from being a
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second order consideration to a primary consideration or it should and one of the things I've been talking to all of
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our uh guests on the podcast series about is what are the hindrances to actually getting this stuff implemented
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so what do you see you know there's always whether you want to call it algorithm aversion there's like well how
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do I know this machine learning algorithm is right how what kind of barriers are you seeing or hesitancy
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you're seeing in the field especially when when it's Healthcare yeah I think as a first order
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consideration one of the biggest issues is that as Hamza mentioned data is certainly an issue but a bigger issue is
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that to deploy these algorithms well you need to actually have a very deep understanding of clinical workflows for
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them to be incorporated so even when clinicians are willing to accept them um you still have to integrate them into
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workflows and that point of integration seems to be one of the biggest challenges at the moment so that's a
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perfect segue to my question for Hamza so um I've that's probably the biggest
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problem I've had in my career and you're going to tell me now how to solve that
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where I develop algorithms but you know the fact is getting them actually in someone's workflow is really tough how
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did how do you think about that how did you deal with that when you were working
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with Greece with Sierra Leon to actually you know you have an algorithm but you can't just hand it off to someone and
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say good luck right I think um that's an excellent question and uh I'm doing my
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best over here I dep I think it depends on the complexity of the setting and how
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well a human is trained to answer that particular question uh compared to how well an algorithm might be so I think in
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a lot of public health questions uh where you're trying to forecast demand for Health Resources or you're trying to
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figure out who which population to screen and it's kind of rapidly evolving that's kind of the work we've done um it
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makes sense like even policy makers or public health experts think that algorithms are better suited to
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assessing those situations because there's large volumes of data um I mean assuming that you've built an
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interpretable system that they can look into and you know um check the reasoning
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of the algorithm I think in healthcare it's harder because for example a very
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famous example is this sepsis alarm that's in a lot of icus that's being
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deployed now uh and I think a big challenge is those algorithms they're not always aware of the private
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information that the physician has so a lot of Doctors Express frustration that when the alarm goes off they already
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knew that the patient was crashing they're working actively to you know stabilize the patient and this thing is
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just you know irritating them and so this causes something called alarm fatigue so I think algorithms need to be
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exactly as Marissa said designed in a way that is aware of what knowledge the human decision maker has and is able to
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complement it in a useful way and that's not how we do machine learning so that's
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a fascinating idea let me let me give you my example that I always like to give in sports and then I'll translate
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it to healthcare which is what is the role of Scouts in sports like can't I just measure everything and then just
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the algorithm's going to tell me who the better player is but they may have private information that the algorithm
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doesn't see and so I always talk about blending the two can you talk to me Marissa a little bit about how it's it
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shouldn't really be AI or humans it really should be Ai and humans in healthcare yeah in healthcare in
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particular this is not negotiable in many ways in large part because of Regulation so if you think about what's
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happening at the Forefront of algorithm development almost all the models at this point are thinking about a
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physician or clinician with an AI co-pilot and that model I think is going to be the one that is the one that's
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most likely to Prevail both for regulatory reasons which you in many ways can't get around with but also
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forgetting clinici and buy in which is absolutely essential and seems to be a huge hurdle at the moment so um Mera
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just mentioned a word which is you know an interesting word when we develop algorithms which are whether it's
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regulation or restrictions how do you think about that when you're kind of saying well here would be the optimal
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solution in kind of an unrestricted world with unlimited data and the ability to do whatever you want or now
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that there restrictions here's kind of whether you want to call it the loss of
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efficacy or here's the you know how do you think about restrictions when you're
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thinking about building algorithms I think it's a great question uh I think in most of these cases humans still have
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this valuable private signal so we do want them to override the algorithm uh but I've heard a lot of concerns that um
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you know people are worried about malpractice lawsuits and things like that and so they would rather if if an
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algorithm is for example FDA approved they would rather be more conservative and Ur towards the algorithm so we've
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had a lot of talk about algorithm aversion but I think it also goes the other way that sometimes there's over
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Reliance on algorithms because it clear creates a more established like uh paper
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trail but ideally we would have better training so Physicians are able to understand what are the limits and the
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capacities of these algorithms what is the correlation between the information that's bringing to the table and their
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own private signal so that they're able to more effectively combine these signals in a I guess beian way uh and I
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think that will be necessary to get actually good outcomes so you just mentioned something I was not aware of
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maybe Marissa you could educate me and our listeners here on SiriusXM and in this AI at Wharton podcast series do
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these algorithms have to be FDA approved and if the answer is yes usually the gold standard for approval is randomized
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experiments can you run a is it I mean imagine running a randomized experiment and now people are dying so
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how do you think about running like getting kind of the gold standard of evidence in cases is where you're
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testing an algorithm how is that thought about yeah all these algorithms require
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FDA approval did know they do and there have been more than 500 algorithms that have already been approved for use in
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clinical settings and so you can get a sense of just how many algorithms there are that exist and I think the lack of
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clinical integration is also highlighted by how few of those are actually used in
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practice um so certainly um regulation is a key point for this um and there's a
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lot of debate over how well they're actually being regulated so the current standard is trying to compare them to
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existing clinici and performance um but we know right that algorithms that particularly when they're exposed to new
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data will often times deteriorate so you're like the question is both how well does it work compared to clinicians
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but also on how large of data sets and those are two current criteria that are really key um the other interesting
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piece about on the regulation side is my under and I'm not a lawyer uh but my
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understanding is that ultimately responsibility still lies with a clinician so if there is going to be a
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loss suit um even if an algorithm does have FDA approval the final the responsibility finally lies with the
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clinician um so that's another Regulatory and legal challenge in terms of getting large scale deployment so
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could you give us a sense maybe Marissa um of these I think you mentioned about 500 algorithms could you give our
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listeners here on SiriusXM just a sense of like what are these algorithms like like what would be if you want to think
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about them from the most impactful and efficacious to the least What would near the top and what would be like I don't
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know like for example let's imagine you had something that could red EKGs that
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could prevent heart attacks at a much higher rate than a h that would seem to be be given the frequency of heart
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attacks that would seem to be pretty efficacious and important can you give us a sense of like or the way I like to
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describe it I'm an effect Siz person tell me the things that you think the algorithms that are having the big
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effect sizes on a large population yeah the algorithms that seem to be enjoying the greatest success in having the
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biggest impact on Healthcare and healthare outcomes do seem to be the ones that are focused on Radiology so if
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you imagine that you show up at the emergency department and that you may be having a stroke um the ability to get
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that scan and read that scan quickly is critical so time is of the essence in Saving Lives um and the deployment of AI
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to you read Radiology reports which then do set an alert that speeds up the rest
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of clinical care so a human is looking at it um but that acceleration seems to have a huge impact on both Health
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outcomes and the cost and quality of care so I would put the more Radiology focused machine red ability to read um
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various scans of whatever nature those may be or Radiology reports seem to be the area where there's been the greatest
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penetration um so I think that's where the greatest impact lies where things
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get trickier or when you think about things that um require a deeper integration into clinical care and I
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think where the they're facing the biggest uh point of resistance is actually if we think about things that
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are directly patient facing MH so uh Marissa just mentioned something that I haven't thought about for a while I used
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to spend a lot of time working on methods that I called realtime approximation methods I haven't worked
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on these in a while but as Marissa said someone comes in you know I hate to put it this way but I'll use the word basian
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um I can't run my basian mcmc sampler overnight to get some result that gives
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me something and of course the patient may have died by then how much do you think about you know um as we as AC mics
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are supposed to in theory we're supposed to come up with good answers the fact
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that they may not be real Time That's I hate to put this way but that's someone
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else's problem how much do you think about that when you're trying to come up
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with Solutions like when you were working with coid testing you know someone's coming through the skin and
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you can't say well give me a few hours why don't you just sit over here on the
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side while we decide which you know what the likelihood of you having Co is how do you think about real time nature of
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things yeah I think um sometimes that changes uh the algorithm that you use so sometimes it's better to use do
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something that's uh computationally easier uh that might be slightly less accurate um because it's actually
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practical and another big thing we do is batching like batched updates uh so you
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can't so like uh like Marissa was saying one of the issues I think the FDA is not
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monitoring is making sure that these algorithms actually evolve over time as the patient population changes and
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adapts to thing things to events like coid uh I think that should be part of the regulation but isn't yet uh but we
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do do need to monitor these algorithms as like uh the ICD system changes as uh scanning imagery uh changes and so on uh
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and I think doing batched updates on as fresh data comes in is kind of a critical part of that and that makes it
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that kind of solves a computational issue I see uh we're here on the analytics at warten AI at Warton podcast
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series we're talking about Ai and Healthcare I'm here with my colleagues
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mura King the alisy hung president's distinguished professor at the Wharton School and Hamza bastani associate
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professor of operations information decisions and statistics and data science um let me ask you a question
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question when we as academics approach these problems maybe even approach companies I can imagine
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one of two reactions like what are you doing here or number two oh thank you the academics have come to help us um so
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what is the reaction when we as I mean it's not that we we care I mean I think
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I can safely the three of this people in this room care more about our the way our research impacts practice then
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probably we're in the top desile of Wharton faculty but what's the reaction
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of Industry when academics want to get engaged here yeah I I think that the engagement
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of academics is absolutely critical and I think this is where analytics at Wharton has particularly a huge role to
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play if you think about the nature of the Health Care system in general it's
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highly fragmented fragmented with stakeholders having various um strong positions for a variety of different
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reasons so you have right insurers you have Regulators you have the people delivering care um so there's a many
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many players in this space and to solve healthcare's biggest challenges you need
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to get them all to work together collaboratively and because many times the positions from which they're arguing
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right they may have um different incentives or misaligned incentives and so having a neutral convener who can
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bring together um all those parties and Tackle them from a place um of scientific basis and a place of
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neutrality and act as a really convening organization is really really critical um so certainly algorithmic tools can be
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useful um but in order for them to have broad penetration to tackle the most pressing challenges you need
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coordination among stakeholders and I think that's where Academia can play a
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really important role so actually Marissa reminded me of something that's in neither of your bios but it's going
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to change after today I should have mentioned even more importantly that analytics at won is launching a
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healthcare analytics lab under Hamza and Marissa's leadership for those people
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interested which is I would think everybody you could go to analytics. won. up.edu Warton Healthcare analytics
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lab and see about about all the work that we're that Hamza and Marissa will be leading Us in could you talk uh Hamza
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about the role of uncertainty like an algorithm comes up with a suggestion or a recommendation but one of the things
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that's important is you know if it's 640 does the clinician have the right to
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know that this is 6040 like the model saying this is better than this but maybe it's not that much better how do
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you think about that when you're kind of whether it's a dashboard you're creating
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or you're providing recommendations you're doing some form since you're an
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oid which means you also care about optimization when you think about optimization what is the role and how do
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you think about uncertainty I think it's super important as you probably agree uh
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but I think uh so I'll talk about the humans first so when you're thinking
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about human AI collaboration I think uncertainty is one of those critical pieces of information that you have to
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convey so they know when they should override it and when they shouldn't um but I think one of the challenges has
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been that in behavioral experiments when you show uncertainty people often tend to over trust the algorithm because they
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think oh not only did it give me a point estimate but it also gave me a measure of uncertainty uh so I think this is
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part of the training thing that has to happen that uh we want people to intervene in a preferential way when the
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algorithm is uncertain for optimization it's a little bit easier we've built a
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lot of tools in in stochastic optimization that account for uncertainty uh so that we're targeting
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for example the right quantile of uncertainty rather than just using the mean or estimates because typically in
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underserved populations we'll have a lot more uncertainty and we don't want that
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to result in you know them getting uh fewer resources um the next question I'm going
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to ask Marissa is my favorite question to ask anybody and when it comes to analytics and I can't you know usually
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with me it's about some sort of food or beverage so I'm taking outside my
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personal life I'm talking about my professional life here if you think about the way that AI can impact in
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healthcare I'm going to give you one of three options okay okay you can have
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better data you can have better mathematical models or you could have better adherence by people in the field to what
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we as academics you can't pick all three you you we all want all three which one
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is the big if you'd like impedance right now to advances we're making is it lack
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of data lack of better models or is it we've got all that stuff just you know
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these damn people just won't listen to us I think it's a ladder um so if you
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think about the data challenges the data challenges still Loom large um but we have now the ability to work with large
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enough data sets that this is starting to become a solvable problem I think particularly on the electronic health
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record front it's still a challenge in the sense that most of the time we're
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going hospital by Hospital deploy these things um but it's still like the data
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is okay um the second piece is the algorithms seem right like the algorithms I don't think are the
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challenge uh if you just even look at how many your FDA approved right and I feel like h and I could sit down
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probably tomorrow and write an algorithm that would certainly improve care in many many ways um the big is I'm
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counting on that but H the biggest challenge is really implementation and integration and the same is true as was
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true with data in the sense that most of the time these things have to be rolled
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out system by System hospital by Hospital doctor's office by doctor's office um and until and even within
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those roll outs um there's a lot of clinical resistance so I as I watched these things be deployed in various
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settings right um I can't tell you like I don't even want to disclose how often
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times the algorithm is overridden um just for and medicine is particularly challenging in the sense that clinicians
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have a lot of expertise and a lot of authority and um there's a strong difference to that expertise and
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Authority as there should be but it makes um changing the way that they think um and questioning their judgment
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um particularly difficult so I think it's the latter if I could improve anything it would be adoption and
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implementation so HS I know an issue you've thought quite a bit about and are
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planning on doing a lot of work on is kind of educating people in these methods so what's the process like I my
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brother's a cardiologist I'm pretty sure if you I mean he's also a researcher so
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I'm pretty sure if you present him the ideas of confidence intervals and prediction methods he might get it um
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but many doctors that's not their job like so how do you present information
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or how do we even educate this large population on these algorithms cuz you use the word before I think
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interpretable and explainable maybe they're like how do I know this is just some black box data's coming in
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something's coming out how do you think about your role in kind of educating the
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if you'd like the distribution channel in this case the Physicians on these methods I think uh one big challenge is
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that people don't know what training data was used to train the algorithm and
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I mean giving it to them wouldn't be super useful anyway because it's not
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interpretable but I think um a lot of the reasons that uh we want humans to override these algorithms is because the
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data that they're seeing is possibly an outlier or has a different distribution
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uh than the data that it was trained on so for example maybe they didn't see
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people of this particular type um or maybe the Imaging system that they're using now is a little bit different from
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uh the one that was used in the training data uh and I think partly it's on us to
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provide these signals but I don't think they're immediately interpretable so I
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think some kind of training where we show them historical examples of when the algorithm went wrong and when they
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might have had a right prior and when the algorithm didn't go wrong and when
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they overwrote it even know even just showing them their own decisions historically and seeing when they um
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should or shouldn't have overwritten the algorithm like would help a lot because
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people need feedback and there isn't spec like healthcare is very expensive and these people's time is very
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expensive so like setting aside the time to do that training I think is important
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and costly well that's what that was going to be my next question for Marissa
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do you see whether it's Hospital groups or do you see I don't know the American
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American Medical Association or the American Association of Surgeons I'm just I don't know I think that's assume
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that's a real one I mean do you see them coming to people like yourselves and say
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help us train Us in Mass come up with video series or something so that these people that are overburdened overworked
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in general you could argue underpaid like how are we going to train them because we want to save
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lives yeah I think unfortunately or fortunately depending on how you look at it the greatest opportunity is actually
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just going to come from pain um we know that we're facing a clinical healthcare
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worker shortage so by 2033 um there's going to be a massive shortage of healthcare workers and if you already
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look at the issues around burnout um with a majority of clinicians burnt out um there's already an enormous amount of
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pain and overwork and so I think in many ways that we're most likely to see adoption coming coming from the bottom
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up where clinicians asking right and I I didn't talk about clinical notation but
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that's a huge area in which um large language models and machine learning can
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play a role by starting to do some of the work right and the tasks that clinicians don't need to be doing and by
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saving them that time right then you can allow them to reconnect with patients deliver higher quality care and so I
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think in many ways um education is certainly going to be more important but based on my experience people are really
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much more willing to adopt things when there's a a real need for them and right
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now in healthcare there's a strong need for help um to augment clinical workflows but particularly with the
00:25:23
things that clinicians don't need to be doing and so I think very soon we're
00:25:27
going to be seeing yeah I just had an example the other day where someone I'll just say with a large
00:25:31
Investment Bank told me that all of their meetings are now recorded and now the agent or the investment adviser
00:25:39
doesn't need to take notes because all of that is automatically put into the
00:25:43
system any types of decisions they made get automatically implemented because they're now in this case voice recorded
00:25:49
and automatically and so now the investment adviser can spend time on training and other forms of doing her
00:25:54
his or their job actually better which is a great really great point so maybe in the last minute or two that we have
00:26:00
let me ask each of you i' I'm trying to ask each person in this podcast series
00:26:04
about this so let's say we're sitting here 10 years from now which the invitation is open we're sitting here 10
00:26:10
years from now that'll be my 38th year at Wharton we're sitting here 10 years
00:26:13
from now what are we talking about that either you or you think the field of AI and healthc Care from an algorithmic or
00:26:21
you know data perspective what have we seen over the past 10 years what's your
00:26:26
hope and dream at Le least even if it's not going to happen I think over the
00:26:30
past 10 years we will definitely have um adoption on on notes for example or things that Physicians don't want to do
00:26:36
I think we'll have more adoption in developing countries where there isn't I
00:26:40
know we're we're short staffed even in the US but uh where there isn't as much
00:26:44
health worker staff to reach underserved communities so automation is sort of being more adopted in those locations uh
00:26:51
and I think definitely for things like Radiology I think we'll still be facing
00:26:54
challenges with you know things like alarm fatigue and adoption for places where we have clinician experts and I
00:27:00
think uh with models like gbt coming out I think it'll be easier to educate people on machine learning um and be
00:27:07
able to you know better enable this human AI collaboration but I feel like that is going to be a big challenge even
00:27:12
10 years from now and what do you think mer so what are we going to see out there whether it's in the field or we as
00:27:16
academics are doing I mean both homs and I's goal at the analytics lab is to try
00:27:21
to improve access to care and quality care for all and I think that that's hopefully where analytics will take us
00:27:27
well I'd like to thank both of you for our podcast series episode here on AI
00:27:31
and Healthcare i' like to thank again my uh colleague Marissa King and Hamza
00:27:34
basani thank you again for joining me thank you