[I. GLENN COHEN] All right I think we will get started now because we want to make sure we have enough time to hear from everybody. Welcome everyone. I'm Glenn Cohen. I'm a professor at Harvard Law School I'm a Deputy Dean here and I'm also the faculty director for the Petrie-Flom Center. My pleasure to welcome you, and thank you for attending. It's also my pleasure to acknowledge the Gordon and Betty Moore Foundation and thank them for funding the work we've been doing on diagnosis in the home of which this webcast is part of it. I also want to mention as part of the same project we have a new podcast series featuring some of people who you see here on this panel called Petrie-Dishes you can find on the Apple Podcast Network, review, rate us especially if it's good rating rate us and that's Petrie-dishes we've got a really exciting panel for us today, but I want to just do a few housekeeping points. Firstly if you want to submit a question you can do so through the Q&A function on Zoom or via Twitter, now X, @PetrieFlom.
You do not need to raise your hand or use the chats those are ways we will not be looking at those instead that's the way to do it and Chloe has helpfully put a link to the podcast I see in the chat function. We'll share the fully captioned event video with all the registrants within about one to two weeks. So if you've missed this or you want to watch it again because it was so good one to two weeks it'll be available and captioned. And lastly if you have any technical issues or even a technical problem please email us at Petrie-Flom at law.harvard.edu. Okay well with that housekeeping, it's my pleasure to turn it over to David Simon, now Professor Simon, who is a postdoc on this project and now he's an assistant professor at Northeastern. And David I'm going to turn it over to you.
[DAVID SIMON] Great thanks Glenn. As Glenn said my name is David Simon, I'm an associate professor at Northeastern University School of Law, formerly a postdoctoral fellow at the Petrie-Flom Center and it's my pleasure to introduce our three panelists today, first we have Dr. Adam Landman he's an Emergency Physician and Chief Information officer at Brigham and Women's Hospital. He's interested in Innovative application of Information Technology to improve Healthcare delivery and he led an emergency department Information Systems modernization project a three-year seven million dollar custom software development project to move clinicians from paper-based to electronic documentation among others. He's received a variety of grants and is quite interested in artificial intelligence and its application to Health Care. Our second panelist is Dr. Michael Abramoff. Dr. Abramoff is the Robert Watzke Professor of Ophthalmology and
Visual Sciences at the University of Iowa, with a joint appointment in the College of Engineering he is an IEEE fellow, did I get that right, that the Triple E, and an arvo gold fellow he's also the founder and executive chairman of digital Diagnostics and autonomous AI Diagnostics company, who is the first in any field of medicine to get FDA clearance for an autonomous AI. Our final panelist is Professor Leah Fowler, she's a research assistant professor at the health law and policy Institute at the University of Houston Loss Center, her work explorers intersection of consumer technology and health with a focus on smartphone applications and platform she's published in a variety of outlets and has done some terrific work so thank you to all of the panelists for joining us we're going to start with Professor Landman and I will hand it over to him and begin the slideshow. [ADAM LANDMAN] Great thanks so much David thanks so much for the opportunity to join everyone today, and thanks for the nice introduction. I'm gonna run through these slides really quickly, and it's sort of start with a holistic overview of Healthcare AI. Next
slide please. I just want to start by saying you know I'm a technologist, I love technology, but what we're really talking about here is using technology to improve the quintuple aim. And so our whole goal here is to find solutions AI solutions that help improve population Health that improve the patient experience that help improve the efficiency of healthcare delivery and that also help us with advancing Health Equity and very importantly help with healthcare worker burnout. And so ideally we are looking for solutions that actually help improve all of these five aims or at least many of them. Next slide. I really like this figure by Justin Norden that kind of gives a quick glimpse of what's going on in healthcare AI. And the big takeaway is that there
are AI Solutions popping up to many areas of healthcare challenges, ranging from you know life sciences and and clinical trials research to administrative challenges like prior authorization and medical coding to analytics for pop health and then even to patient-facing solutions like sensors and care navigation and finally we're seeing a lot of AI solutions to help clinicians with clinical decision support or with documentation. Next slide, not all of these use cases are equal though. We want to think about the use cases in terms of what the value add will be, but we are but I also like to think of these use cases in terms of risk. So if you're applying AI to these areas what is the risk? The risk for patient harm for instance. And so on the left hand side are our use cases that I think are lower risk and as we move to the right higher risk. And so I
think where many health systems are starting with AI strategies towards tends to be towards the left where the lower risk, and so we're seeing contact center automation or we just did an AI video. In the middle are sort of medium risk where we're using it in clinical workflows but we often have an expert such as a clinician in the loop, so human in the loop. And then finally I think where we're going to focus today's discussion is on diagnostics, when we're having AI actually do diagnosis or triage a patient and I would say that's at the highest risk. So I want to share with you some real examples of things that we're working on just to give you a sense of where AI is in healthcare delivery. Next slide. The first example is on the lower that I consider lower risk and it's in our contact center, and we're using a various forms of AI in our contact centers to help us with improving efficiency and also improving patient experience as well as employee experience. And we have a large contact center that handles
patient questions related to our electronic health record. Our patients are increasingly using the patient portal and sometimes they have questions. Well traditionally they called and our agents would answer their question, we are using a combination of interactive voice response system with natural language understanding though to have the computer listen to, recognize the questions, and then provide answers to those patients. And we've actually seen about a third of calls to our patient portal support desk are successfully handled by this IVR and AI combination. So it's
really been showing a nice efficiency improvement and Improvement in satisfaction. Next slide. Another example and this is kind of in the middle bucket of kind of medium risk and where we use a human in the loop many sites including my organization are very interested in using generative AI to help with clinical documentation as providers every time we see a patient we have to document, that's for many reasons including billing, legal, and also for clinical care continuity, and that takes quite a bit of time. And so the Holy Grail is could you have a solution where AI does the documentation? And these solutions take the form of an app a secure app running on a smartphone, the patient provides their consent to have the patient provider conversation recorded. That secure recording is then securely sent to a commercial
product in the cloud, and that commercial product then does speech recognition natural language processing, natural language understanding and then then uses large language models to summarize the note, and create a note that looks like any other provider note. We are in early stages of testing this I also want to emphasize that the AI generated note has to be reviewed by the clinician to ensure that it's accurate and correct and has all the information included, it's then edited and signed off. And so we're in early stages of assessing this solution. Next slide. Another promising example is using AI to help with in-basket messages from patients. So we're really excited that we've seen a large growth in number of patients that are using online portals one of the features that our patients really like is sending in-basket messages so sending you know secure email messages to their providers and care teams and across the country we've seen a huge growth in use of these tools particularly after covid, well this is great news and great engagement but it's been very challenging for our practices and our physicians to keep up with responding to these messages while they're still keeping their usual in-person clinic visits and other clinical load. And so we're in early stages of investigating AI, in particular generative AI that can review the messages coming in from patients, classify them, and then once they're classified we can help route them to the appropriate person to handle that message. We're also in early stages of investigating how well AI could actually draft a response to an in-basket message. Those messages would then be reviewed need to be reviewed by a clinician, edited,
and then ultimately sent to the patient by the clinician. Next slide. A final example which is on the diagnostic side I'm going to share a research example it's an exciting study that was recently published by researchers from Mass General Hospital as well as MIT and Chang Gung Memorial hospital and they developed an algorithm that can predict lung cancer risk from a single low dose CT scan of the chest. And you can really see the power of this if you look at the image in the bottom right. If you look at image a when a expert radiologist looked at image a you can see the
area in the circle they rated that as low risk of lung cancer, a lung rad score of two, when the AI algorithm developed in this study which is called Sybil evaluated image a it rated it as a high risk for cancer in the 75th percentile. And Image B is the same patient but two years later and now as a human looking at image B it's very clear there's a new speculated solid mass very concerning for cancer. And so this is an extremely exciting this is very early stage, this is a research study, but this is very exciting because this starts to show AI as a diagnostic tool that is exceeding human capabilities. Next slide. And I think as you know we all want to move towards using AI
for medical diagnosis at the point of care, and I think these are some of the characteristics and things that we need to ensure exist before we can really start using all of these tools and I think we're going to probably have much more discussion on this but most importantly we need to make sure that the algorithms are safe and that they're reliable, but we also need to have to look at the benefit and the return on investment because there are costs to these tools and we need to ensure that there's value being added. In some cases FDA approval may be necessary, and I think the rules and regulations regarding which types of algorithms need FDA approval is evolving. And so there's a there's a real opportunity here to better understand this. And then finally we need to ensure that these algorithms can be applied without bias to all patients, and if there is bias we need to be able to understand that bias and ideally remove the bias. We also need to be thinking you know there is data involved here and need to ensure that the privacy and security of the individuals data as well as the data used to create the algorithms. When possible ideally
these algorithms should also be transparent so clinicians and others can understand how the algorithms are working, that may not be possible in all cases but when it is we should try to bring transparency. And certainly transparency about the algorithm's performance. And finally in some cases we may need to be transparent in explaining to patients when we're using AI and how we're using AI. So I look forward to talking with the entire panel and diving deeper into some of these issues. [DAVID SIMON] Great thank you so much Adam I'm going to turn it over to Michael.
[MICHAEL ABRAMOFF] I'm in a hospital environment so I'm I'm so sorry I'm not wearing a tie and looking decent like everyone else. I've read continuously in the hospital because of family circumstances for a few weeks now. Very excited to be here thanks so much for inviting me and Adam laid it out very carefully very very well. Let me begin with what about me, my name is Michael
Abramoff, I'm a practicing retina specialist like David already mentioned in the beginning. I do notice we do not see my face right now so as long as that's okay I'm good. I'm also as you mentioned the creator of that first autonomous AI and that took a long time, started meeting in 2010 with FDA, Hey I want a computer to make a medical decision how do we go about it and that led to a very fruitful meeting of minds for years and years and years, and that's led to an ethical framework for AI that has I think being really important in getting stakeholder support all cycles really in healthcare to support the use of especially autonomous AI, because as you, as Adam, mentioned the perceived risk of autonomous AI is probably the greatest and we can discuss it a little bit. Also I think it's been an interesting journey since then because 2018, FDA authorized this AI to be used for patient care without human oversight, specifically a diagnosis the complication of diabetes called diabetic retinopathy and diabetic micro edema which are the most important causes of blindness. So it's well you know with Adam it serves a needs it's a major cause of blindness, it can prevent this, because it leads to early treatment and management. It's also traditionally a great source of health disparities
and that's another reason to to use AI to improve access for these patients, and in fact multiple randomized clinical trials coming out this year and already came out are showing that indeed health disparities are improved and in fact in the Baltimore area with black Americans having the same amounts 100 of diabetic eye exams now thanks to autonomous AI as white and Hispanic patients. So really it can resolve a very persistent health disparities that have been plaguing us for often decades and seemingly unsolvable even by throwing resources and money at it. So that's exciting because you know what it all started was really you know building autonomously AIs to hopefully improve outcomes Health Equity population and health, and it's indeed now showing certificates that all this trouble that everyone went to is worth it. But like I said it was not you know easy steps, FDA but it's also required for example National Committee of quality assurance when many of you are providers and they're probably familiar with measures like hedis and mips which until then always said that a human needs to close the care cap as it's known and that language was changed and now a care gap can be closed with an autonomous AI so there's all these small detailed steps that need to be taken for before you can actually say well this is now you know being widely deployed in clinic and is useful for solving these problems that we have in healthcare. Another step was reimbursement I didn't really see it very explicitly Adam on your slide, which was very helpful as a framework for panel discussion, but you may be aware of fair Therapeutics, a company that was very successful had FDA approval for an app, and I'm looking at Leah for addiction and curing addiction and showing in randomized clinical trials that it worked. So
everything you know, all the checkboxes were checked except that they had a hard time for various reasons why they didn't get reimbursement and ultimately that killed a company and it went bankrupt now a few months ago. And so a very useful AI technology that was shown to benefit patients that already had FDA approval didn't make it and now will no benefit patients in the technology essentially is lost. So I think reimbursement is is a very important factor but that of course requires every stakehold in healthcare to be supportive. For example physicians often fear job loss it's not only a job satisfaction but literally you know will I still have a job 10 years from now I'm often asked these questions by residents and fellows and so AI reached to these worries and that also of course can lead to lack of stakeholder support from physician organizations and that is typically you know a problem if you want to get reimbursement.
And so payers of course need to support it there needs to be an ROI ethicism to support our patient organization to support it. And I think the work we did on the ethical framework that they already mentioned, and essentially making the step from rather than talking about ethics measuring ethics, a concept called metrics for ethics where you say, well this AI meets this biological principle 1.5 on some scale and actually being able to have various metrics there's of course many of them that were published a few years ago. I think that really helped get these stakeholders on board and understand that we were addressing any concerns they could have and Adam at least many of them proactively rather than reactively as often as seen you know in other instances of new technology. Let's for example gene therapy. So I think a very worthwhile journey stakeholder support and that ultimately led to CMS and later all payers you know reimbursing this at a level where there's also a sustainable business model, which is of course important for sustainable rnd and continued investment by VCs and now also private equity.
I think with you know the results of the randomized clinical trials coming out this year that the circle is almost rounded this can be done you can really take an algorithm take care of bias, address all the issues that maybe with ethics get sake of support automatically get reimbursed and and make it benefit patients, and so I think it's really worthwhile to to discuss these various steps, especially this is still you know the first AI we created and we have many more is still prescription defined, so it's not for home use, we're not there yet. I think the FDA is not yet comfortable with this being used in the home, and so how do we move from the current state to autonomous use in, let's say at home. You know what are the steps need to be taken and absolutely I think we will get there but you know, step by step, so I will stop here. And maybe Leah is next? [DAVID SIMON] Yes, Leah.
[LEAH FOWLER] Yes, I am next. Let me get my PowerPoint shared. All right can everyone see that, great. So hi everyone I'm really happy to be here, it's so exciting to be on a panel with such great speakers and it's exciting because of the topic because the promise of technology to move diagnosis out of the confines of the clinic and into the homes of patients is actually a really big and exciting topic and one facet of that that particularly interests me a great deal, is the way that consumer health technology is often regulated very differently than similar data and tools in a medical or scientific context, and I often like to think about this disconnect and two major buckets one is more privacy and security and confidentiality, and the other is safety and efficacy and accuracy. And so that's actually what I'm going to talk about briefly today, and I'll be pivoting to the other digital tools, which certainly can but don't always include artificial intelligence that are one of the subjects of today's event. And these are tools that have
that maybe don't always live up to the potential to transform medical diagnosis in the home, especially in a consumer context, and a couple of the legal and ethical issues that they raise. Now this is kind of an ambitious 10 minutes, but I plan to with a very high level examination of our very basic assumptions about at least our traditional notions of healthcare, maybe a little bit further back than the evolution that we've talked about going or previous two speakers have talked about, and then consider how we engage in health promoting activities and activities that even look and feel a lot like diagnosing and treating from the consumer perspective, even if it technically isn't, specifically in a consumer context, but like I alluded to when I talked about the things that interest me in my research, the types of protections you get as a patient, are the types of protections you may expect, are very different than the types of protections you get as a consumer. And I will illustrate that point with two examples of digital health tools that are commonly used. Now when I talk about a healthcare context, what I mean is settings we typically think about when we think about the provision of care, like a clinic or a hospital. And they're among some of the most highly regulated settings in the United States. So because of those complex laws and regulations, we have certain expectations about the care we're going to receive and how our personal Health Data are going to be treated. At least as we traditionally think about it and I
know these are things that Dr. Landman actually mentioned when he was discussing the things that we need in place to advance AI diagnosis. And one of the first big ones is that we expect that the treatments we receive are going to be safe and effective and that we have enough evidence about them to make informed choices, about the risks and benefits, and that the diagnostic tools that are being used are going to be reasonably accurate and precise, and that we individually as patients don't generally have to do independent research to be sure of any of those things. And the second thing we assume is that our data will be kept private and secure and in some cases we have expectations about confidentiality. However it is worth putting a huge caveat on all of that. Just because these are expectations doesn't mean it's something that everybody gets. So for example not
everyone receives the same quality of care, either because of location or stigma or resources or structural barriers, but for simplicity many of us can go to the doctor have these basic expectations about things like accuracy and privacy and for the most part those expectations are going to be met. But of course a medical encounter is not the only place that people manage their health but you would certainly not be here today if that were true. And so for example you individually may be tracking your calories or your steps in an app, or you may be using a wearable like a smart watch or a ring, and some of these wearables also sync with other apps that aggregate large data sets that can use artificial intelligence to do things like improve health predictions and this can span health categories. So it could include weight loss, or menstrual cycle syncing, to mental health, to sleep, and so much more. And truly the space is full of products and services and advice that viewed in their most positive light can help us live our healthiest lives and they're tools that can liberate health care from just the confines of the clinic and bring it directly to consumers in their homes. But one of the things I alluded to is that our
assumptions about privacy and accuracy in a medical context do not always translate into a consumer context and it depends on many variables that are not always particularly clear to consumers. And in the interest of time I will give you only two examples though there are many examples. But the first is that in a consumer context your data are treated differently. Now many of you watching know that HIPAA and its state level counterparts protect Health Data privacy in very certain contexts. So while some states offer more robust protections,
HIPAA itself is only providing privacy and security protections for certain types of identifiable information possessed or controlled by covered entities and their business associates, which is not everyone. And so importantly the vast majority of cases HIPAA is not going to apply to consumer techh like your smartphone apps, or any health information you're receiving or sharing on like a social media platform. And second is that most apps and many wearables are not going to be FDA regulated medical devices, even if they look the same or similar to a device that is FDA regulated. And this is in part because the FDA has pretty broad discretion about how it interprets a product's intended use which is a special term of Art in the law, and further legislation actually carved out certain types of products from the FDAs definition of medical device. So now it excludes things like low-risk devices intended for maintaining or encouraging a healthy lifestyle, which includes a lot of consumer products. And that's all a very long-winded way of saying that
many products don't have to obtain any sort of pre-market approval or authorization or even baseline demonstrate that they work before they enter the consumer Tech Market. Now of course a lingering question in the background of all of this is why would it matter that things that look and feel like healthcare or health information are treated differently depending on the context, and I would argue that certainly can be a big deal especially as our interests and optimizing our health through consumer Technologies grows and private for-profit companies continue to offer oftentimes very promising technological solutions to the problems of Health Care in the United States. But if we continue to position consumer technologies even as perfect substitutes for evidence-based care, it does raise important legal and ethical questions especially since at least right now the prevailing advice to consumers in the absence of more robust legal and regulatory protections is unlike in a healthcare setting for you to do your own diligence, and your own research on these technologies before you pick one that you want to use. But I would offer that that's actually really difficult advice to follow. And I won't just tell you I will actually show you if two examples involving femtech, which is actually a really broad category of technologies that address female Health needs. And this is most commonly refers to period and fertility tracking
apps and I pick femtech specifically because it's super easy to understand why accuracy and privacy matter in this context. So if an app you are using to achieve or avoid conception is not accurate, you will either be not be able to become pregnant when you want to be, or you may unintentionally become pregnant when you don't want to be. And depending on where you live you may have limited access to the full spectrum of reproductive care. And further, menstrual data is legally and
medically significant, so for example the date of your last period is relevant to determining gestational age and period infertility trackers at their most basic taking away all of the technological shine are often just repositories of dates and menstruation. So let's talk about what it might look like for a consumer to do their due diligence and try and pick an accurate and private app in this space. And we'll start with accuracy now most people make decisions about the types of digital Health tools they're going to download at the point of download. And for most of us this is going to be the Apple App Store or the Google Play Store, and one of the first things you might see is images that the App Store shows you and this is an example on the slide here. And if you're looking for something that feels like an assurance of accuracy, your interest might be peaked like by claims like the ones that my teeny tiny Arrow points to and it says automatic and accurate predictions of fertility. And of course images are not the only thing you're going to find at the point of download you'll also find things like your app description, and these words are even smaller but what you need to know is it's echoing the same guarantees. It's saying that one of the
features is automatic and accurate predictions of fertility. So what can we conclude from this? Could we conclude that the product accurately predicts when you're fertile and if it can do that can it also predict when you're infertile, and if it can do both of those things couldn't you use it to achieve or avoid conception? But we can check one more spot just to be sure and that's the terms of service, and of course terms of service are not documents that people often read this one in particular is not available in the App Store I had to Google it and what you would find if you read it is likely a health disclaimer and this tells a very different story than the images in the App Store. Suddenly It's all language about how the information and predictions can't be used for diagnosis or treatment and you should not use this product for conception or contraception, if you trust it you do it at your own risk, and it's just interesting to think about how these documents tell very different stories than the most obvious consumer facing advertisements.
And just to do this again for privacy this is a screenshot from a different app, and what you'll see where the arrow is pointing if you can read it I know it's very small is that it says the app never shares or sells your personal data, and I want you to ask yourself to reflect on what you think the word never means. Because this is an excerpt from the app's privacy policy where it shows a non-exhaustive list of the ways the app does share your information with third parties. And I don't know about you but that's not what never means to me. Now I would hate for you to
think I'm just picking on a couple questionable apps I have no opinions on the products that I've shown you here. I just want to show you something that is fairly common in the health app space which is apps advertise things that consumers want even if it's not necessarily a thing they truly offer. But while I just talked about femtech I do want to be clear that this discussion goes far beyond it. It matters in a lot of contexts, especially ones that we might
think of as low risk but maybe aren't. So context from which the risk of physical harm is greater If the product isn't accurate or circumstances in which privacy and security are important because the risks of things like stigma and discrimination are higher. But no matter what, even if it doesn't fall into one of those buckets, we want products to do what they claim to do because even if they can't actively harm you if it's not working, it's a missed opportunity for improvement, and if we don't read the terms of service and privacy policies, the way they advertise product their products really does matter. But my final point because I know I'm coming up on my time, is what I want for you to take away from this is if we want consumer Technologies to be truly disruptive and game changers and how individuals self-manage their health or diagnose in the home, it's really important to assess honestly where they live up to those promises and maybe where they still fall a little bit short, and this disconnect between assumptions and protections and the limits of consumer due diligence is just one piece of that puzzle. And with that thank you so much. [DAVID SIMON] Great thank you all of the panelists for really terrific presentations touching on so many different issues. I think what I'd like to start with is a question that all of you can respond to if you'd like. Leah talked about
unregulated zone of products, the Zone where at least FDA is not doing the regulating. Dr. Abramoff talked about his product the product that he helped develop in the context of FDA regulation, and then Dr. Landman talked a little bit about both. And so I'm wondering what each of the panelists thinks about the current FDA framework, the current framework for evaluating these kinds of products, and how we might think about changing it or modifying it in the future. So I'll pose that question first to Dr. Landman. [ADAM LANDMAN] Thanks for the opportunity and I think the challenge is there's ambiguity in the current framework around you know what is regulated and what's not regulated. And so I think the crisper we can be on where the level of regulation is, is really important. And
ultimately I think that there are some aspects of regulation that can really help accelerate this work, right, so and frankly may also help what I'm seeing right now, is that a lot of centers are doing the same work, on these AI tools, because we're all trying to adhere to the principles that were described by everyone earlier. And so we're all trying to do our diligence to test and validate and ensure the safety and equity of all of these tools and if there were ways that we could agree on, and potentially through regulation set up ways that there were standardized processes and expectations and then transparency into that process for those who are consuming these tools I think it could help accelerate some of this work. So overall I think that particularly as AI advances and there's an increasing desire to use it at the point of care, either on the clinician's facing side, or with the patients as Heather described I think there's a real opportunity for us to bolster the processes and maybe even use a public partner private partnership to do that. [DAVID SIMON] Michael did you have any thoughts? You're muted. [MICHAEL ABRAMOFF] Sorry. Let me put a pull up two small points and then otherwise agree with Adam
and Leah. But first, the concept that assistive AI is in some way safer than an autonomous AI or autonomous AI is high risk, is probably you know debatable. And the the study I like to refer to is Fenton et al from 2007, where there was an FDA approved mammography AI that was validated under FDA purview as in essentially in an autonomous fashion, compared to radiologists at really high performance and therefore was approved. That was not the way it was used it was used as an assistive AI in conjunction with a radiologist, where it indicated lesions such as calcifications and nodules on the mammogram that supposedly the radiologists would then look more carefully at that had never been validated as a system. The assistive AI plus the clinician and it was shown
because everyone expected, well duh with an AI the clinician will obviously be better, Fenton et al decided to study that in 200,000 women and they showed that outcomes for women diagnosed by the radiologists assisted with the AI were worse than that for the radiologist alone. So even in this simple case AI does not always make things better in an assistive fashion. So I'm not sure whether the risk of an autonomous AI is actually higher it's perceived that way but at least we can test it as a determinant, the deterministic system, rather than a variable interaction of physicians with an AI. Data that's relevant is shown by the Boeing 737 Max example where Boeing developed an AI, it was you know even tested with very experienced pilots and it was fine and then less experienced pilots were starting to use it they over corrected and two planes were put into the ground as you may remember a few years ago. Again an assistive
AI really hard to validate, because you need a broad spectrum of expertise on the sides of the physician, or sorry the expert being assisted. So that is one aspect that is relevant because we you know also discussing llms and chatGPT. The second is that what Leah said is absolutely true that these apps have the potential to maybe harm patients in some way or at least not get them good care, but more importantly like you said David and Adam there is this tightly regulated pretty you know and in my view we're in a sort of Goldilocks situation where this tightly regulated AI right now which reimbursed it's regulated people feel comfortable with it, but there's this better AI that is actually harming patients that is not regulated, specifically I'm referring to my friend Ziad paper in science in 2020 where he studied in AI created by a payer, I won't name names that was used to create to determine care pathways for people with lung disease. It turned out that because cost was used as proxy for the severity of the disease, that actually black patients who had less cost for the same severity of disease in the training set would actually be directed to less sub-optimal care and being harmed compared to other patients. So this AI that was in a non-regulated space was causing harm, and you would say well okay we identified it and manufacturer actually improved and they were done the problem is that this is being cited widely by Congress and Regulators including the office of civil rights in HHS, which as you may know has a proposed Rule 1557 to essentially make liable anyone who uses digital Health Products that are biased. And so the sheer Factor this existed as it was harming patients
in a totally different space can have a little backlash on all AI in this case, and we have seen it with gene therapy years ago where gene therapy was doing really well in 90s some unethical experience were done it was shut down it was dead for 20 years, and it took a long time to recover to get FDA to proven when gene therapy looks now. So long story short, I think the non-regulated space is really important and is already having an impact what happens there on the regulated space. [DAVID SIMON] Thanks Leah do you have any comments about that? [LEAH FOWLER] I do, and I actually it's a way of building on and echoing two of the points that were made about transparency and the role of reimbursement, because obviously more tightly regulating in the space raises a lot of new challenges. So one of the one of the benefits of having sort of light regulatory touch is products can enter the market cheaper, they could people can access them at a lower price point, so the more regulation you add in it's going to drive up the costs of good and tested and evidence-based products, which may in turn especially in the health app space, drive people to download things that are free and use the things that aren't tested. So that is a challenge and a tight line you have to walk, and the other one is of course this transparency issue. And I think a lot of consumers don't generally understand you know what a product's intended uses and whether it's going to be FDA regulated or whether it's not and you even see apps advertising things like FDA-registered which is of course doesn't it's not a thing that really means anything other than that the FDA knows that it exists.
And so until I think we're able to communicate to consumers what these types of distinctions mean, I think we're going to continue to have struggles with apps that are more heavily regulated and may have gone through all of the FDA approval or clearance processes and these other ones that look visibly very similar if not identical to them that have had none of the oversight. [DAVID SIMON] Great there are a couple questions in the chat that I wanted to try to combine in some way. They're more technical questions, so perhaps Adam and Michael might be better able to answer than the Leah but maybe Leah knows a lot about computational computer science or something that I'm not aware of. The questions are really directed towards the potential functions of AI tools. So one question is is it possible to assign a probability of a diagnosis using an AI tool, and the second question is, is it possible that to design an AI that doesn't itself produce a diagnosis but suggests tests that could produce a diagnosis? So similar questions that are relating to the process of AI.
[ADAM LANDMAN] I mean I can happy to start if that's helpful. I mean the short answer is yes, right so there there are clinical decision support tools or AI that can suggest new tests in fact that's common it might be a recommendation to say you know consider you know consider these strategies for the patient. I think for associating a probability, it may depend on what AI technique is used, on whether it can associate a probability, I think in some of these tools some of the best practices we've seen is showing the test characteristics overall for the tool and making that very transparent to the end users. And whether or not it can display a specific probability for the specific patient that may be a little dependent on the techniques, but let me see if Michael wants to correct or add anything to what I'm sharing.
[MICHAEL ABRAMOFF] It's interesting I absolutely agree that it can be done, I think what patients want to know more than anything is the outcome right, what is my clinical outcome going to be, and can you do anything about it. So I think that's really more relevant this can be a tool the probability of well how should they adjust my risk and is it worth to do a certain intervention or a certain extra diagnostic with its own risk, and you know weighing that with risk to maybe the disease, I may get, or the poor outcome I can get. So I can see where that might be useful it's really interesting to see the discussions for the autonomous AI that we created where FDA was actually concerned about the too complex output meaning you can give a very high level output if there's this level of disease, and that has these associations with other diseases and these risks of progressions to various end stages, and FDA and this is for a primary care physician primarily, so let alone for the patient they considered all these outputs too complex and really want the the customers yes no, you know bad disease good disease, or a referral to a specialist for more care in this case an eye care specialist is warranted. So they really were focused on making this as simplistic as possible and so that was really interesting process to go through so rather than very sophisticated outputs, they think it's better to have as simple outputs as possible and from a interaction with AI outputs that's probably the right decision, and that may also have implications for apps you know in a non-regulated space where clearly you know keep it simple is often better. So I don't think I'm not sure that helps with with answering your question but I think that's an interesting aspect.
[DAVID SIMON] Yeah that actually leads to another question for Leah, which is how much information is the appropriate amount of information for consumers? And how do we know what's the right amount and then do we treat doctors as consumers or do we treat them as a special kind of consumer as a law traditionally has treated them? [LEAH FOWLER] So I guess obviously two different questions here, one of them being what's the right amount of information that we can give consumers and I would offer that it's not just what is the right amount but how do we give it to them. So if we have a lot of literature that suggests people aren't reading things like the privacy policies in terms of service inundating those documents with more information that people are not going to read is not going to be helpful. But if people are making decisions at the point of download, and we know that apps are digital products or advertising their products in specific ways be it puffery or whatever you want to call it, we have to be very careful about the information that they're sharing there. So if an app says that it's accurate or that it never shares your data I think one place we need to be clear is that that baseline should be true. And so whether that means we need more information and we need to make documents that are already 30 pages long into 60 pages long I don't think that's necessarily the right answer. But
we need to be more Innovative in the ways that we share information and ensure that the information that people do see is correct. So the other question that you asked is should we be treating physicians as just a different type of consumer and I I would say that it in a classic lawyer fashion it depends on the context in which we're talking about these digital tools right, so if we expect physicians to be making recommendations about apps or for their specific patients to use, we should be treating them I believe as more of a learned intermediary somebody who knows more about the product that they're recommending. And so yes I would expect that their understanding of the types of protections and regulations and evidence behind that product is greater than your average consumer in a consumer context, and that's really challenging when you talk about the consumer health Tech space more generally because if there's so many products it would be almost unreasonable to expect any physician to just know every single app and all the different nuance of it but if they're going to be recommending a specific one yes I would expect that there'd be a certain higher level of knowledge associated with that recommendation. [ADAM LANDMAN] And I build a little bit on Leah's great comments there which is I actually think there's a opportunity and a need to educate Physicians more on these tools. Right so for instance as you go through your medical training you learn a lot about diagnostic testing right so so as an Emergency Physician I got a lot of training around how to use a troponin which is a blood test to look for damage to the heart, and how to apply that test correctly in variety of clinical settings, like that was part of my medical school and then clinical you know residency training. I think we're going to need for some of these AI tools we also are going to need to train clinicians on how to use them appropriately, and we're going to need to think about standard ways in presenting you know AI tools so that Physicians can understand them and and also be able to manage multiple tools so I think there's a huge opportunity here as we go forward.
[MICHAEL ABRAMOFF] Let me add actually we have been working with FDA on a sort of AI effects label, like you have for food, right on every item of food where there would be the level of evidence, the level of reference standard Etc. Of course first of all we need to agree and what is a good reference center etc, but but there is some movement and I think that will be really helpful. But that's a few years away I'm afraid. [DAVID SIMON] Yes I'll just put in a plug for my colleague Sarah Gerkey she wrote a paper on that very subject so I'd recommend that to anybody who's interested. I did get a question in the chat here about reimbursement and the question is basically asking in light of paratherapeutics bankruptcy how do we ensure that there's a sustainable business model for digital therapeutics. [MICHAEL ABRAMOFF] I love taking that. So is it okay or Leah you want to start first okay. So I think that that is key. Like with my example every they had everything done except that right
and still they're dead and so like I mentioned I think stakeholder support is crucial any Group, Patient Group, access group a group that doesn't want it and everyone else wants it it's dead in the water in my view. And I've seen it happen if you go to the cp2 editorial panel where and I've shined these NDAs so I cannot disclose much, but I do know there's companies there that have been waiting eight years for a category three code, and if you're aware Category 3 code is not even leading to reimbursement, it just means that you're going to measure and be allowed to measure utilization then hopefully showing enough utilization, so a few years later you can move to a category one code. So there's this depending on the stakeholder and enthusiasm that you see the effort the level of evidence for improving the patient outcome, Health Equity, all these factors that really go into every small item on the way to reimbursement. Then CMS in our case spent more than 30 pages on three different proposed rules in a federal register, on what they call the guardrails around AI. What they didn't want is set a precedent for all sorts of bad AI that
we're discussing now to be reimbursed and blowing up the budget, so they want to be very careful and say is biased address? Which normally CMS doesn't really think about they now have to think about all these different aspects of how AI can cause harm right so data usage Etc they were really very considerate in in their decision to to do this reimbursement. I think the framework because essentially in many cases and most even most physicians are not aware the The Physician fee schedule which is core of CMS and for many payers the example to follow for private payers is really what the charge is and the charge it can then be reimbursed for the physician pays a charge and then it can reimbursed by the payer so assuming as an AI Creator you have to decide what is the charge I set. And if that's very high that looks very promising, but you know people say well why should we pay for this, is this cost effective Etc you get all these considerations. It's very low
typically if you're an AI Creator your investors will say this is not a sustainable business model. You know we cannot support this so you need to have the sweet spot I think the model we proposed which is we call an equity enhancing model where we said in this case, for this specific diagnostic procedure, right now instead of 100 of patients getting it it's a big source of health disparities most underserved patients are not getting it, and only 15 to 30 percent are getting it. Clearly there's a willingness to pay for this 30 percent let's Now set the charge for the AI where we can do all 100 percent of patients and not blow up the budget meaning but the same amount that we're the same expenses we're currently paying for 30 percent will now pay for 100 and we'll set the charge that we as AI Creator set accordingly. So now you go into these meetings and they say well clearly you're trying to save money here not not increase money, you're not trying to blow out the budget here, you're actually trying what we all want and you do it based on you know improved patient outcome. So I think that really helped and we published that right in nature digital medicine now a year ago I think already but this entire framework such equations it actually is I think very useful and is being used by other AI creatives to set a charge because that's what it starts with, people ask what what can I do for reimbursement that's not what you should be asking, you should be asking what is the appropriate charge that we should be asking in health system, a provider, Etc what is again, found in evidence I think that's what we did and that's why I think it happened and I think that's a good path to follow. [LEAH FOWLER]And it's such a rich and wonderful answer it's hard to hard to elaborate too much on that but for me what it really underscores is how intertwined reimbursement and regulation are.
So at least in the health app space we have certain expectations that apps are available for free if you're going to start charging for them being able to build in the ability to get reimbursed through insurance is something that could make evidence-based apps more accessible. And to me it also just really highlights to me how accuracy and privacy are so linked, because oftentimes when apps are developed and then offered for free consumers themselves become the product. Because you're able to transact in the consumer data that you're able to collect. So if we do want higher protections into when we are talking about privacy and when we're talking about security, we're now cutting off a revenue Stream So if we can't come up with another opportunity for reimbursement, we're not going to see the types of positive innovation in this space that really could move the needle for consumers and consumer Health Technologies. [DAVID SIMON] So there's a, oh go ahead Adam.
[ADAM LANDMAN] No I I guess I would just add one thing which is I'm disappointed that both of these companies are challenged and and did not succeed here, because they ostensibly did most things right, right, they spent they actually validated their products, right they went up for reimbursement. I also wonder if we're just not at the right time for them I think as I look forward like we have to figure this out. If you start to look at the demographics and particularly in behavioral and mental health, we do not have enough people to care for the number of patients that need this care and these services. So I think we've got to figure out what services need
a human and and particular physicians, and then where can we use automation? So I guess I would just say I hope we don't give up in this area, because I think it is going to be the solution it may take us longer but it's desperately needed and particularly in behavioral mental health. [DAVID SIMON] Let me push back against what you just said and propose something to the whole group, is probably will be the last question. So if automation is well let's say I agree with you that automation is going to be part of the solution. But something you said at the
beginning of your talk stuck with me which is this automated responses to incoming messages and you said that people were really engaged with these incoming messages and they really liked using the messaging service, and maybe we can figure out a way to automate that in some respect. I submit that I for one hate using the messaging service and I would much rather just talk to the doctor for 30 seconds and maybe the doctor doesn't have time to do that but for me that's would be more efficient. So I wonder if an over-reliance on artificial intelligence auto response automation not only will depersonalize further the already depersonalized Healthcare experience but lead to maybe not worse outcomes but more dissatisfaction with the healthcare system and how would that influence the growth of these technologies further? [ADAM LANDMAN] Yeah I can start and then let my colleagues jump in you're absolutely right in raising this. I mean look, ultimately we want to have multiple channels for the patient to be able to interact with not only the provider but the entire care team. And what we want to really try to do is figure out what they're reaching out about and balance what their preference is for how they want to interact, right and this the nice thing is we can think about what language, what format, right how you know do they prefer to use a smartphone and send a message, but also look at the clinical need and which format would work best for that clinical need. And I think what we're trying to solve for is how do you do that as efficiently as possible to address the patient preferences but also address the really significant capacity constraints and challenges that we have on the clinical care side and different my colleagues may have other perspectives on this.
[MICHAEL ABRAMOFF] I think Leah wanted to respond. [LEAH FOWLER] I mean you're not going to find a lot of pushback from me, because I tend to agree with you. And I know that one of the more recent examples we saw in the consumer digital health space had to do with chatbots for therapy and people tended to react pretty well, until they found out that it was a chat bot, and then you know statements like I understand how you feel stopped resonating with them. And
I you're not going to hear any pushback from me because I agree with you. [MICHAEL ABRAMOFF] I think you know what we're trying to do, what I have been trying to do is bring high quality Health Care as close to the patient as can be that can be from a specialty clinic to primary care, which is really our focus right now, and then well you know the things Leah is discussing bringing it from maybe the health system to even the home. And wherever that is appropriate and leads to better outcomes that we should be focused there's a lot right now where what we're not doing, we're not even discussing whether I'm comfortable. There's no
interaction of these patients with diabetes with a Ico specialist whats
2023-10-12