In this engaging conversation On Health Biz Talk, host Tony Trenkle, former CMS CIO, connects with Anand Iyer, President and COO at WellDoc, about AI’s transformative role in managing chronic conditions. Iyer, a top AI thought leader and Maryland Healthcare Innovator of the Year, shares insights from WellDoc’s FDA-cleared digital therapeutics for diabetes and cardio-metabolic conditions. The discussion explores how generative AI solves interoperability challenges, how agentic AI delivers personalized patient coaching, and why federated identity management is revolutionizing data access. Iyer also addresses public-private partnerships under the CMS Health Tech initiative, GLP-1 medication integration with digital tools, and the inevitable fusion of technology and medicine as the iPhone generation enters medical practice.
00:00:00 Intro
Welcome to Health Biz Talk, the industry’s leading podcast that brings you today’s top innovators and leading voices in healthcare technology, business and policy. And here’s your host, Tony Trenkle, former CMS, CIO and health IT industry leader.
00:00:12 Tony
Hi, I want to welcome Anand Iyer to our podcast. Anand is a respected global digital health innovator and leader, most known for his insights on and experience with technology strategy and regulatory policy. Anand has been instrumental in WellDoc’s success with the development of Blue Star, which is the first FDA cleared digital therapeutic for adults with type 2 diabetes. Since joining WellDoc in 2008, he’s held core leadership positions that have included President and Chief Operating Officer and Chief strategy officer.
In 2013, Anand was named the Maryland Healthcare Innovator of the Year in the field of mobile health. Anand was also recognised as a top AI thought leader globally when he was named to the Constellation Research’s prestigious AI150 list in 2024. So welcome Anand. Appreciate you taking the time to talk with us and we have a good bunch of questions and things to talk about today. So first, want to start off with how did you get to where you are today? What’s. What are some additional things about your background, your interest, major career turning points that you wanted to talk about?
00:01:32 Anand
Yeah, no, absolutely. First, Tony, thanks for having me. This is always fun to do and looking forward to the dialogue. For those of you who, who know me or don’t know me equally, I’m a telecom guy, a wireless technology strategy guy. I did my doctoral work, believe it or not, way back at Carnegie Mellon in the first incarnation of AI as it was known in the mid-90s, and did a lot of work with management consulting with wireless technology, rolling out wireless networks around the world, the services that sit on top of those networks, things including telematics, the first smart applications inside of cars, for example, RFID or radio frequency identification tracking in supply chains, military supply chains, for example.
And in all of that fun work, Tony, I developed type 2 diabetes myself, which is interesting wake up call for me as an individual, you know, as a consulting partner, you’re always travelling, you’re never eating at the right time, you’re never exercising at the right time. And, you know, asked a very simple question was is there an opportunity to take all that innovation in wireless technology that you’ve done in other industries and apply it to healthcare?
And I think there was a personal instance in many ways. You know, we talked when we first met about turning points in our careers that was an interesting turning point for me in my career, saying, hey, if I could direct that experience and how to start new businesses, how to build product strategies, etc. But in this case, to that Venn diagram that was bringing mobility technology, but healthcare, if you would, then could it actually unlock value that couldn’t be unlocked by medicine alone? And so it was a big shift for me, but that’s what brought me to Weldock. And as they say, the rest is history. So. So I think it’s, it’s advantageous and it’s kind of fun also to have the vantage point of a leader in the space, in the company, but also as a consumer of the products that we build. Because you get a firsthand experience of what works and what’s important.
00:03:36 Tony
Exactly. It’s like they used to say, your own dog food back in the day.
00:03:41 Anand
But drink your own champagne. Yes, right.
00:03:43 Tony
That sounds better. Yeah, I like that one. So you’ve gotten very involved in AI and as you said, you’ve been involved in earlier iterations of AI for years. And I guess one of the things that you’ve written about and talked about is our favourite buzzword. Well, one of our favourites, interoperability. So when we throw AI and interoperability together, we have two great buzzwords. But I think one of the things that you had talked about is the role that AI can play as a connector and it can bring together multiple data protocols and really can unify all these disparate data standards that we’ve had to deal with over the years. So can you talk a little bit more about that and some of the work that you’ve done in that area?
00:04:31 Anand
Absolutely. And I think first it’s important to understand when we say AI, what is AI in the first place? There’s a tendency in today’s world to synonymize AI with generative AI, which I’ll explain in a second, is just one subset. But when you think about AI, Sir Alan Turing, 1956, you know, teaching a machine to do what a human would otherwise do or think. And so even a rules based engine that then directs if you would a machine to mimic. Start to think about how diabetes and initial medication titrations are done. There’s a standard algorithm that providers follow, you know, wait, wait this long.
If haemoglobin A1C is this and do this. And so it’s literally if it’s a if then else kind of series of constructs that lead to a state vector machine or rules engine, you advance from that to the next form of AI, which is machine learning, and maybe a subset of that, deep learning, which is more neural network methodologies. But then of course, with the advent of chat GPT, which is what, December 2022, if I’m not mistaken, all of a sudden you have this capability called generative AI, where the machine comes up with content and rules based on that content.
And I think it’s in that incarnation of AI, generative AI, where this notion of interoperability, which has eluded all health care practitioners for decades, there have been so many efforts, whether they’re private efforts, whether they’re government efforts, to say, hey, we need interoperability, enter stage left, you know, pick your favourite four letter acronym for a standard. And it was probably some health protocol. But generative AI has that magic, if you would, that says, okay, I can train, I can take an agentic approach to translating X to Y. That’s no different than translating, for example, from English to Spanish or anything like that, where you can train the machine to say, look, I’m going to get a stream in this format HL7, and I want it to come out in a different format. Okay, but I want the variables to be mapped correctly, I want the units to be mapped correctly, I want the values to be mapped correctly.
And I think generative AI has a huge potential to maybe instead of forcing single standards, which I think is good at the end of the day, standardisation allows for scalability in an industry, but we can achieve that standardisation almost quickly by using generative AI as a series of smart connectors that says, look, I really don’t care what format it’s coming in, we’ll figure out how to make that into a single standard, which then brings that data together in a way that you can make more personal insights, you can make more precise predictions and things like that going forward. So I think generative plays almost a transformational role in this dialogue that’s been there forever on interoperability.
00:07:18 Tony
I guess, listen to your talk, I think about the whole issue around standards. Even as you put standards together, then you have to develop implementation guides to support those standards. And sounds from the way you’re talking that maybe generative AI could take the place or certainly help supplement what we do with implementation guides today.
00:07:40 Anand
It very well could. And I think that’s an opportunity that we’re already seeing manifest in other areas, not specifically not in healthcare. It’s there, for example, in how we manage protocols and supply chain. It’s there that’s now looking in retail and how they manage these large Supply chains. You think of the usual suspects like you know, FedEx, UPS, Amazon. There are lessons we can import from how they’re using that. Interoperability, different types of data, location data, product data, you know, customer location data, et cetera, where you don’t necessarily need a single standard gen. AI can play that kind of transformer role, if you would.
00:08:17 Tony
Right. How do you see that working with like blockchain and areas like that, It’ll.
00:08:23 Anand
Be necessary to include things like blockchain because at the end of the day security and privacy of that information, making sure that it’s never tampered with is going to be, you know, the highest level of importance. And blockchain of course can be used to do that kind of authentication and encryption. Right. At the packet level.
00:08:42 Tony
Right.
00:08:43 Anand
Which is, which is unique. So you’re now, you’re now assigning. And we’ll talk about it I think in a little bit. This whole notion of what is now people like ID me or people like clear IAL2 certification authentication, where you’re now ensuring that the source is the source and nothing but the source.
00:09:05 Tony
Right.
00:09:06 Anand
And that’s important going forward. So I think there is a role, huge role for blockchain to play going forward.
00:09:12 Tony
Yeah, I think in the past we talked a lot about technical non repudiation, especially as it related to blockchain and other areas. But it sounds like. Well, I don’t want to get too far off, but it does sound like we will get into that in a few minutes, but does sound like that. You know, what you’re talking about is something that could really replace that with a stronger type of identification.
00:09:33 Anand
Yeah, I mean just you mentioned non repudiation. You know, security in its basic, you know, if you were to define it in a very basic sense, it’s the sum of three parts. Right. It’s authentication, which we all know. And then you have NIST now with IAL2 as the highest level. It’s encryption.
00:09:52 Tony
Right.
00:09:52 Anand
Which there’s so many different forms of encryption that we’re. And then of course it’s non repudiation, making sure that the acknowledgement loop is complete. And so I think we will redefine ways of doing just that, fulfilling the intent of those three elements, but in maybe computationally more efficient ways and certainly computationally more scalable ways.
00:10:16 Tony
Right, right, right. That makes a lot of sense. So I’m going to switch topics a little bit. You’ve been involved, as you mentioned, with the chronic care population and certainly with diabetics and other areas. And I Guess one of the questions I had was, you know, you’ve developed various platforms. We spoke about the, how you’ve worked with the fda and what have you seen as the biggest challenges in that area and what do you see as kind of your biggest successes? Is it, is it more regulatory, less technology? Is it more the actual patients themselves? Is it the kind of the healthcare industrial complex? What do you, what are kind of. Or maybe it’s all the above. But what are your thoughts?
00:11:07 Anand
You know, a rich question packed with a lot of different sub questions. We’ll try to untangle it. But to start, first of all, who are we and, and what do we do? So at WellDoc, we’re a digital health leader. We’re very fortunate to be in that position where we deliver what we call healthcare ready AI.
00:11:28 Tony
Right.
00:11:29 Anand
What do I mean by that? We’re talking about flexibly providing clinically validated digital support for both patients and providers who suffer from chronic conditions or who manage chronic conditions.
00:11:42 Tony
Okay.
00:11:43 Anand
And when you think about that and you think about the stakeholders, this is a three person play, for lack of a better way of saying it. There’s three actors in this play that, that we’re part of. One is the actor, of course, is the patient themselves, the consumer who has either one of these conditions or multiple conditions, which I’ll explain momentarily, but it’s their health care provider or constellation of healthcare providers. Could be their primary care endocrinologist, nephrologist, cardiologist, etc. And then it’s the enterprise themselves. Could be a insurance company, could be a hospital system, could be a PBM medical device company. So there’s a, there’s a constellation of these enterprises. And for each one of these actors, digital health provides a different value proposition.
So for the patient, it’s a mechanism to take ambient data. Okay. Whether it’s through connected devices. Today, WellDoc connects to over 400 different data sources, direct Bluetooth devices, API connections, cloud connections, et cetera, to bring in that data and then convert that data into what I would call information, knowledge, action and outcomes to provide them personalised coaching and guidance on what they should do based on that data. And that’s wonderful because you’re giving them almost like turn by turn instructions that we’re all familiar with. For example, with Google Maps, for the healthcare provider, it’s using advanced AI algorithms to detect patterns of interest where the healthcare provider can now be educated themselves upon hate. This is where your patient was, say three months ago, this is where they are today. This is what’s changed and this is what you should do against evidence based medicine. But you know, you’re the doctor, do what you think is right for your patient.
And then of course for the last stakeholder, the enterprise, they’re looking at the pop health level, they want to see the greater population. And so I think whilst we started this journey with these actors that we just discussed, whilst we started the journey with type 2 diabetes, we’ve really expanded to what we call a multi condition chronic condition platform today. So today it’s all forms of diabetes, so type 1, type 2, pregnancy as well as prediabetes. But also add to that heart failure, add to that obesity management with or without GLP1 management and add sleep management, CKD mash. There’s a series of these cardio metabolic conditions that are managed now within a single user experience. Because as an, as an individual, I don’t want to go to three different apps to manage my three different conditions and certainly my doctor doesn’t want to go to three different portals to do that.
00:14:25 Tony
Right.
00:14:25 Anand
And so I think some of the successes, when you think about the path, Tony, that we’ve taken, it’s one of what I would call scientific rigour. So for example, 11,510k FDA clearances to demonstrate both patient outcomes and efficacy, but also good manufacturing process, right. That we’re actually building these robust digital tools just the same way we would build a new drug or build a new medical device. We use the same kind of scientific rigour, if you would. At the same time we also have, I think it’s 103, so 100 plus clinical publications, which means in simple terms it works. And the clinical community. So these are all peer reviewed publications by the clinical community and scientific communities that it actually works. And so I think some of the successes are when you have those fundamentals in place. That’s what leads to the most important thing, which is the healthcare outcomes.
Now we’re Talking about a 2 to 3 point delta and haemoglobin A1C, right. When you add CGM, that number creeps above 3, which is, keep in mind, a delta of 0.5 is what the FDA expects for a new drug to clear in its randomised control studies. You’re talking about 6x, what the effect is that they expect just huge. Right. At the same time, you’re Talking about a 7 to 12 millimetre systolic blood pressure drop, you’re talking about a anywhere between an 8 to 16% obesity drop, weight loss drop based on your BMI starting band all of these collectively I think are very positive things that have happened. So these are all great successes. Okay, Wonderful successes. I think the challenge that if I were to boil it down to one or two things that I think we still need to solve for, not just well Doc, but I think the community at large, I often joke and say that the headache doesn’t go away if they don’t swallow the Advil.
00:16:25 Tony
Right.
00:16:27 Anand
So how do you get them to engage with the app? But how do you get them to engage in a way that’s meaningful for them? Because how you engage and how I engage may be different. So the notion of personalised engagement that works with their constraints, they could be medication accessibility constraints, it could be other social determinant constraints, there could be health literacy constraints. So how do you work within the constraints that they have but still get them to the point where they engage and they sustain that engagement over time? Part of that magic is removing friction. So for example, being able to connect to 400 devices, ambient listening, imputing things, using AI where they might not have put in data, but you can tell from a continuous glucose monitoring trace what they ate or that they exercised right.
AI all of a sudden helps you fill in the blanks on some of that data that may not have come or they may have forgotten to enter. And then based on that input, you can now provide them that even more hyper precision coaching, if that’s the right way to say it, this hyper personalization. So I think that’s number one is challenge number one is just getting them to do those things. But there’s techniques that we’ve uncovered. I think, I think challenge number two is when you think about, and it’s something that we’re seeing now, these things cannot work in point solutions or vacuums. They have to work across and be integrated with our healthcare system.
So there’s too many players who have come and gone in this digital health space who are almost creating parallel health universes, their own coaches, data that doesn’t talk to their primary care Doc. So to the extent these solutions can be integrable into how we deliver healthcare going forward, integrated into clinical workflow in a manner that fits the doctor’s workflow too, those are challenges that we’re overcoming as an industry. We’re not there yet fully, but we’re headed the right direction.
00:18:28 Tony
Yeah, I think, I think that’s that that’s right. And part of it really does relate to this whole issue of health literacy. And so I guess as a follow on question is how do you really empower people to better self manage their chronic conditions. I mean some people got there because of genetics, but a lot of people it’s because of lifestyle. So trying to get these people to change. No matter how much data you give, what do you think? How’s the, how can you do this with, you know, there’s so much data nowadays it’s hard for people to understand what it means. I know I have a lot of portals with my various, I guess, problems for want of a better way of putting them and a lot of it is hard to understand and there’s people who have a lot less literacy in it than I do. So, so what is your thought with that and where can agency help with that?
00:19:30 Anand
So let’s. Great question. Let’s first unravel what we mean by this types of data or heterogeneous data. I often use the acronym metals to explain it in simplistic terms which is M medications. So there’s, there’s data on your med, your med class, how much you’re supposed to take, when you’re supposed to take it, etc. E is education. So now you think about the American Heart Association’s life’s essential 8 you think about ADESS 7, you think about these well established courses, topics, lessons. There is education that people, people can take to bolster their own healthcare knowledge about the condition that they have. D is of course diet.
00:20:07 Tony
Right.
00:20:08 Anand
Micro macronutrients. Okay. A’s activity which includes not just exercise but sleep. Also, how many new emergent articles have they been in, in clinical journals about the effect of sleep on fasting glucose or the effect of sleep on overall wellness? Sleep is creeping up in its importance. We need to consider that sleep vector as we give coaching to patients. So med A L lab values, things that I actually enter, my blood pressure, my blood glucose, my weight, cholesterol levels, things like that. And then of course S is kind of the surveys and symptoms, for lack of a better way of saying it, it’s unstructured data. So now just think about. I’ll use me as an example. You want me to manage all of that during the day, simultaneously, you know, throughout the day when you know there’s a little thing called life that gets in the way.
00:20:57 Tony
Right, Right. Good luck.
00:20:58 Anand
Right. So the analogy for me, Tony, is if you have a dry sponge on a table and you pour a, you know, you know, jug full of water on it and you just carelessly pour a whole bucket of water, the sponge might absorb a little bit of the water, but most of it’ll flow off the side of the sponge. Whereas if you carefully tailor the flow rate from your jug to match the absorption rate of the sponge, then the water isn’t being wasted, it’s being absorbed by the sponge. And so here the analogy says, can I deliver tailored information, actionable insights in bite sized chunks to patients? Okay. And can I do it in a way that fits what they want to manage? Okay, so, okay, I got it. You got to manage all these different metals, vectors. But you know what, let’s start with this one. Let’s start with glucose management. Okay.
Now let’s think about when it’s right to add into your journey, food management or exercise management. And so I think agentic AI, this is, this is almost like a silver lining of agentic AI. It you can create models that focus on every one of these, individually or collectively, that’s up to you. And you can then drip, if that’s the right word, you can drip these models, these different agentic approaches to help the patient absorb any one of these things one at a time. Maybe you have patients who have a higher absorption rate. Bring it on, tell me what to do. I can do more, because I can do more. It allows that kind of flexibility, but you’re delivering those things in bite sized chunks. And I think that ability for AI to either take that holistic approach based on parameters that the patient may set, or just by observing, hey, notice that this person is really good at collecting all this different multivariate data.
00:23:01 Tony
Good.
00:23:02 Anand
You can amp up feedback and amp up coaching on any one of those vectors. Whereas if they tend to be doing one thing at a time, one thing at a time, that in and of itself should be a clue that says, hey, maybe there’s a more serial approach you take. Either way, they’re going to get to the right place.
00:23:19 Tony
Right, right, right.
00:23:20 Anand
But I think understanding that bite sized chunk, actionable, personalised information is something that can truly help. And agentic AI is written just for that.
00:23:31 Tony
So are you doing anything with agentic AI with that in that area today.
00:23:35 Anand
Both for patients and for the healthcare providers? And so every one of these, manager meds, manager glucose, manage your blood pressure. Think of all of these as age managing a part of their overarching health. Because as a diabetes patient myself, I don’t think about these things separately, I think about my overall health. Right. So these agents work in the background, right, to create this umbrella of better health. And it’s no different for their healthcare provider. They may want to see certain things as it relates to your cholesterol. They may want to see certain things as it relates to your exercise patterns, but at the end of the day, your provider is going to be interested in your overall improvement in health, you know, as a sum of these things. So agentic AI that we’re already building, that’s already in the product, looks at these both to support the patient as well as to support the healthcare provider, which is. Which is pretty fascinating.
00:24:28 Tony
That is fascinating. So I. I guess I wanted to kind of turn to some of these other players in this field and, you know, we talked about the providers, of course, we talked about. About the patients in their role. What about some of these others? The payers, employers, government regulators, how are they kind of tie into all this? I’m sure you got a lot to say about the payers because obviously they control a lot of this because they control a lot of the money.
00:24:56 Anand
So in many ways they’re also the beneficiaries, right?
00:25:01 Tony
Exactly.
00:25:01 Anand
They’re the ones holding the bag. And so for them, you know, if I. We did a study with IBM, Watson Health, Truvan analytics, where we demonstrated, using actual adjudicated claims data, that when somebody improves their haemoglobin A1C to the tune of 2 points, which is what we showed in our clinical studies, on average, you’re looking at about a $3,100 per patient per year cost savings. All right? So right away, that’s valuable to the payer because they’re holding the bag of money, but it’s also valuable for, for example, in a capitated system where somebody’s on the block for paying for this care for an individual, and now you’re able to reduce, if you would, that overhead that’s being spent on this person. So you can predictably know what you should spend and what you should not spend on a population given an A1C distribution, for example. So inherent in that, and I think we’re seeing this in spades now, especially with the current administration, is the notion of public private partnership. So we’re seeing the CMS Health tech initiative that Dr. Oz is spearheading, for which Weldoc was one of the first 60 companies that were invited to the White House to come and pledge, if you would, that, hey, we can actually help solve this conundrum. Right, by bringing scalable, cost effective, but highly, highly effective treatment tools to these members who suffer from these chronic conditions. We’re also seeing it in the rural health transformation programmes that are happening at the state levels.
00:26:39 Tony
Right.
00:26:39 Anand
You know, Dr. Oz affectionately says, hey, the whole purpose of this effort is to partner up or buddy up. And so I think we’re going to see an increase in public private collaboration. I think we’re going to see an increase in data sharing. So, for example, we in December went and presented our model along with our friends from Clear, where we incorporated IAL2 certification. So imagine this, you get the app, you download the app and it tells you, hey, want to make sure it’s really you? Use a Clear authentication to say, yep, it’s really me. And then once that token is issued, you use that token to then bring in all the data from these disparate sources, different formats that we discussed earlier. And now all of a sudden, your algorithms inside your app are powered by all this data that you wouldn’t have otherwise had access to. So the specificity and the personalization of that app just skyrocket because you have all this ambient information that’s been brought in securely, that’s part of you. And that can only happen with this kind of collaboration with a payer, an EMR vendor, maybe it’s a state Medicaid programme, in one case a federal Medicare programme. So ultimately, right data, right person, right place, right time, right outcome, I think we’re going to see more and more of that kind of collaboration, Tony, going forward.
00:28:08 Tony
Yeah, and I think it makes a lot of sense. I mean, I mentioned the employers, but I really think the big players in this, particularly if you’re looking at chronic disease, are the government entities and the major health plans as well as, of course, the providers and the patients. Because of the fact that if we looked at, I know it’s cms, a large share of our dollars going out were for, you know, what we called, you know, multiple chronic conditions or comorbidities. I mean, so, you know what was.
00:28:40 Anand
Fascinating when we did the Truven study that I alluded to earlier on with our friends, is that we looked at three different sectors, if you would. We looked at the Medicare, so 65 and above, but we also looked at kind of the commercial sector in two age bands, 40 to 64, and then I think it was 18 to 39 or whatever the balance is. And what was fascinating is there was always the sentiment in the community, ah, diabetes, you know what? It’s not our problem, it’s Medicare’s problem. That’s when that’s going to rear its head and we’re like, no, no, no, no, no. There’s actually a cost savings opportunity today, right in the commercial sector that says you can arrest that growth right now, you can stunt that growth. And there’s today cost savings and of course there’s going to be cost avoidance down street to Medicare. So I think you’ll see it for sure. You’re absolutely right.
The public entities, Medicaid programmes, huge beneficiaries, but employers and health plans, they also stand to gain a lot right now. And I think that’s what, in many ways is encouraging them to participate, because we’re not telling them, oh, you’re going to have a benefit 20 years from now. No, no, no, no. I’m going to cut down your acute utilizations. Right. I’m going to optimise your healthcare spend, whether it’s, you know, drugs, whether it’s dme, whether it’s, whatever that contributes to the total spend. I’m going to optimise that right now. So there’s a cost savings opportunity. I think that economic proof point or set of proof points is going to continue to drive this at the private level as well as the public level. And certainly the combination of the two.
00:30:21 Tony
Yeah, I think the private public partnership makes sense for a number of reasons. And one of the areas you just talked about is by the time people get into the Medicare programme, a lot of them already have comorbidities and it becomes more of a maintenance issue. So if you can convince the employers, and of course, Medicare, Medicaid is another one that’s always had a big population of people with chronic diseases. But if you can get these different players to kind of look at this as a, almost a life cycle type of thing, I mean, the amount of money and frankly, burden on our hospitals and providers will be greatly decreased if we can start this early. And like you said, what you’re looking at, developing and implementing can go a long way, particularly if you can get the patient to really. And I guess part of this also gets into the, you know, the drugs like Ozempic. And how do we kind of tie some of that into this effort as well?
00:31:29 Anand
I joke that it’s almost like these GLP1s or incretin therapies. So Ziliozempics, the, the, the Tirzepatide molecule. So that would be, you know, Mounjaro and Zepbound for, for Eli Lilly, these drugs. It’s almost like there’s some. There’s nothing they can’t do, Right?
00:31:49 Tony
Exactly.
00:31:49 Anand
Every day there’s a new benefit. At the same time, we know that these drugs have a number of side effects which cause people to stop taking them, you know, prematurely. We also know that they’re quite expensive, notwithstanding the Price pressures that are coming down, but it’s still in the grand scheme of things, expensive. But they also require certain things that the patient has to do to ensure that they optimise use of that drug in their body. For example, protein management with GLP1s or strength training with GLP1s to make sure you’re not losing muscle mass.
00:32:25 Tony
Right, right.
00:32:26 Anand
And so, so you know the FDA recently announced the notion of piders, right. Prescription drug use related software, if I got that right, that says I now have a digital component that’s going to be in the label of my, my, my drug and that there’s going to be an uplift in how that drug is used or maybe there’s an uplift in the benefit or outcomes if you actually use it in conjunction with the app. Okay. That’s the whole notion of padirs. It’s the wraparound, it’s the software wraparound the drug, if you would. And, and in many ways you’re amplifying how that drug can actually create benefit for that person. And so I think as you, you know, you mentioned Ozempic is a great example. All of these GLP1s require that careful attention of symptom tracking, you know, knowledge about, you know, food management, activity management, et cetera that’s specific to this drug class. Now imagine taking that same pattern, Tony, and expanding it beyond obesity to things like oncology or things like rare conditions where interactions and symptoms may actually help optimise that treatment pathway. I think there’s a huge opportunity that looms ahead for all of us by looking at things the same way. We’re looking at GLP1 plus these, plus these apps to help manage, if you would, the best outcomes.
00:33:52 Tony
And it sounds like, at least from my perspective, there’s always a question of costs and benefits and how do you create a health care payment system that rewards where it needs to be rewards. Sounds to me what you’re talking about is designing a model for the Innovation centre to start really looking at this.
00:34:18 Anand
And I think that’s exactly what the administration is looking at today. Saying you have Amy Gleason at CMS who is coming up with the access payment model that supports, if you would, the CMS Health Tech initiative and all bets are it’s going to be something that has a large value based component to it. And so we’re truly looking for Delta A1C and Delta blood pressure and Delta BMI and other markers. And if you can shift that population, knowing that you’re going to shift cost, you’re taking cost out of the system by trapping those and arresting those higher costs from manifesting in the future. Then all of a sudden you’re bringing forth a model that can be implemented. Because value based care, it’s been a, it’s not a new concept, it’s been around for quite a while. Very few people have actually cracked the nut on how to implement it. But I think the combination of digital enablement software as a medical device, along with treatment as we know it today, drugs, providers support, et cetera, I think that’s going to be the right set of ingredients to then lay a pathway forward for how do we actually bring value based care to fruition.
00:35:40 Tony
Right. And I think one of the challenges with value based care has been figuring out not just the short term gains but the longer term gains. And how do you monetize something like that? And because a lot of what happens in value based care is really more short term focused and what you’re talking about is really something that’s really a lifetime changer.
00:36:06 Anand
Yeah, I mean I still remember some of the early dialogues and people say, oh great, you’re going to create this benefit for me in 10 years, great, come back and I’ll pay you in year 10. That doesn’t work for a startup company. Right.
00:36:17 Tony
So exactly.
00:36:18 Anand
I think we have to show both what I call cost savings which is today, and cost avoidance, which is tomorrow. And if we can show a balance between those two and then you have a shot of success.
00:36:32 Tony
That’s exactly right. I totally agree with you on that. So we’ve been dancing around this a little bit and the whole issue around identity management, you talked about a few things here and one of the challenges that I have, and I know CMS has been looking at this, but me as a patient is I’ve got so many portals now and I, you know, if you belong to certain health systems, sometimes it works. Well, if you belong to multiple health systems, they don’t always get the information from one health system to another. And so we were stuck with these portals for, you know, lab tests, for radiology tests, for this specialist, for that specialist for the insurance company, etc. You know, Medicare with my Medicare. So identity management and this whole idea of federated identity has been something, you know, we’ve had looking at the government with the different work that ID me and others have done. Where are you looking at taking this next?
00:37:44 Anand
So, so great question. We’ve actually already implemented this with our friends at Clear and, and the ability to create a unique identity and a token that then that token can be used by another aligned network, any aligned network. And that could be a hospital system, it could be a lab system, it could be a pharmacy system. Any of them who take that same identity and incorporate it into their authentication procedures or stack. All of a sudden you’ve allowed for all of that data to be brought in at the touch of a button. So for example, the flow in our app now is once you’ve gotten that IAL2 authentication token, hey, looks like your data, you have data here in location A, you have data here location B. Would you like us to bring the data in in one place? All of a sud. Sudden it comes into one place securely, privately. All those things we said earlier on, right. Encryption authentication and non repudiation cheque, cheque, cheque. Wonderful. And I think that is going to be in many ways we didn’t have this notion of clear or ID me. Ial2 I think is a game changer here.
The whole concept of this method of authentication allows for that heterogeneous data all over the place, different locations, different types of data to be brought into one place that are then used by these applications to personalise, customise. I mean, imagine this. I did simple example in diabetes. If I have knowledge of what your haemoglobin A1C is, my feedback for what you do on an individual blood glucose level or a CGM continuous glucose monitoring signal is going to be different if I actually have knowledge of that vector and if I have knowledge of the second vector of your comorbid conditions, okay, your blood glucose is 250mgs per deciliter, but you also have heart failure, so you can only drink as much water as your doctor is recommended to bring that sugar level down. Knowledge of those different data sources drives precision in that feedback. I think we’re on as an industry and certainly. Well, doc, as part of that, I think we’re on to something, Tony, very, very transformative that we couldn’t do before because there was no way of bringing in all that data in a scalable, cost effective way without hiring a lot of people to work on a lot of integrations. That took a lot of time and a lot of money.
00:40:14 Tony
Right, right, right. Well, I hope we get there because it’s certainly is challenging, I have to say, both as a patient and as someone who’s been tied in with the industry for a long time.
00:40:30 Anand
We’re definitely getting there. You know, it’s interesting when, when we were all together at the White House at the first kickoff for the CMS Health Tech event. Dr. Oz was speaking about his, some of his recent travels where, you know, he had been to Southeast Asia and he’d been to Japan and he’d been to, you know, Dubai. And he bells already says, look, these guys have figured it out, this is the way they do it. Now they also don’t have legacy systems that they had to interoperate with because they’re doing it from grounds up right with the emergent technology. Great. We have the additional challenge of yes, we have legacy. So we have to have not just forwards evolution, we have to have backwards compatibility. Okay. Slightly different set of challenges, not insurmountable by any means, but. But I think we’re getting there and that’s. Yeah. I always ask, are you a cup is half empty or cup is half full person? Well, I’m a cup is half full person. We’re going to get there.
00:41:24 Tony
And I think you’re right. I think what’s going to happen is it’s not going to come all at once because the US health system is a complex model with a lot of like you said, legacy systems, legacy policies and legacy organisations for that matter. And it’s not like a small country where we can say, okay, since it works, you know, in company country X, it can work here in the us But I agree with you, it’s, it’s more, it’s going to be more of an iterative process and a big bang change.
00:41:55 Anand
I think you’re right.
00:41:58 Tony
All right, Anand, we’re gonna kind of wrap things up here. We’ve got a few final questions to ask you. One is turn into the personal side. What keeps you busy when you know you’re not doing or thinking about health care?
00:42:12 Anand
Oh boy, where do I start? How much time do we have? All my friends know me as a hockey player. I grew up playing hockey all the way through college.
00:42:21 Tony
Okay.
00:42:22 Anand
I coach hockey here in, outside of D.C. and I spend some time doing that cooking. My wife and I love cooking. We both enjoy our wine and we enjoy travelling and I’m a, I’m a South Indian classical drummer, believe it or not. And so I spent some time doing my music as well. So yeah, those are the kinds of things that keep me, you know, I think keep me well grounded and balanced when we’re not doing work.
00:42:46 Tony
Right, Yeah, I agree with you. You have to, you have to have outside interest because otherwise this thing would just frustrate the heck out of you. And for one of a better way of putting it. And what are, where are some places people can go to get Further information about the work you and some of your cohorts are doing. Are there certain websites, podcasts, blogs, other materials that are there, certain places you go to when you’re trying to get more information?
00:43:17 Anand
Yes, certainly. For those of you interested more in what WellDoc is doing, specifically WellDoc.com has a rich resource library. All of our publications are there, all of our podcasts are there, all of our interviews, press releases, everything is there. So you can kind of see what’s going on from the vantage point of us as a leader in this space. But there are other things that I would also recommend. HLTH and the podcast that health puts on my friends Mark Minovich and Ray Wang at Constellation Research and AI150. Mark does a tremendous job of putting out a blog on what’s the latest happening in AI, not just within healthcare, but outside of healthcare. Because it’s good for the AI practitioners in healthcare to kind of look around every now and then.
00:44:04 Tony
Exactly.
00:44:05 Anand
See where are people making strides forward with healthcare. Hims and then there’s one Stan Stanford the Amy it’s the artificial intelligence, medical imaging and they’ve been at this for a while, but they always have good things to say and good points to learn. So those, some of the places that I go to. But, but Please do visit Weldoc.com It’s a great place to start.
00:44:31 Tony
Yeah, I visited on my doing my research for the discussion today and you do have a wealth of material out there. So finally I’m going to have you put your big strategic hat on and just since you’ve been involved in this field for a long time, what, where do you see things going in the next five to ten years?
00:44:54 Anand
Sorry, you know, I’ll answer that, I’ll answer that properly and then I’ll answer it tongue in cheek. I always like the tongue in cheek one better. People always ask me, you know, what’s been good for digital health and, and, and, and, and its proliferation. Sadly, one of the answers I say is Covid COVID 19 which I think it awakened, if that’s the right way to say it. Our everybody’s understanding of what digital could do and we’re not just talking about telemedicine, which is it came to life.
00:45:37 Tony
During most people think is telemedicine. Tele. Right.
00:45:40 Anand
But telemedicine, all it does is it, it, it replaces the synchronous interaction between you and your provider. It’s now happening over a zoom call or you know, a secure telephone call. We’re talking about what happens when they’re not with their doctor. How do you support these chronic condition members making all of those turn by turn instructions when they’re not with their doctor? So in between their office visits. And I think that’s something that technology has really helped bring to the forefront that says, hey, we actually have ways now, okay, to do that. And I think to complement that, a lot of people will say, well, the FDA standing in the way of innovation and blah, blah, blah.
And I actually have quite the opposite perspective. I think that in many ways they’re really working hard to put the right guardrails to ensure safety of these solutions and to ensure efficacy of these solutions. The latest PCCP or the pre controlled change process guidance document on the use of AI in regulated software as medical device products is a great example. So I think regulatory innovation, technology innovation certainly are things that are, that I think are, you know, fundamentally, you know, kind of changing this going forward and they’ll continue to accelerate, I think, the proliferation of these solutions. And then my tongue in cheek answer, which I use a lot, but it, for me, it makes a lot of sense. Tony, the, the iPhone came out in what year?
00:47:06 Tony
2007?
00:47:07 Anand
Sure did. The 2007 means we’re in 26 today. Right. So if my math is right, the iPhone generation is 19 years old.
00:47:16 Tony
That’s right.
00:47:16 Anand
Which means they’re probably two to three years away from going to medical school, if they choose to go to medical school. And do you think they’re going to want to graduate with a stethoscope that goes when they can get ECG signals on the back of a phone. Perfect. And so I would say there’s an inevitability. For me, that’s the word. There’s an inevitability of the role. We won’t call it digital health in five years, it’ll just be called health. And if it doesn’t have a digital component, it’s not going to be relevant. We don’t call it E banking anymore. You better have a E strategy, otherwise you’re not relevant. So I think there’s a societal inevitability of the role that technology and digital will play. Will it ever replace doctors? No.
00:48:06 Tony
No.
00:48:07 Anand
But when you combine AI and digital health with the doctor who uses it versus a doctor who doesn’t.
00:48:13 Tony
Right, right.
00:48:14 Anand
Maybe those who use it will replace those who don’t. That’s quite possible.
00:48:17 Tony
Right.
00:48:18 Anand
So I’m excited that these are the types of things of where it’s headed because I think that the next generation of practitioners won’t accept mediocrity. They won’t accept old ways. They will say, why are we doing things this way when we have all this capability at our fingertips that can help me to help manage my patient better?
00:48:38 Tony
Right. Right. Well, thank you. Anand appreciate you spending the time. It’s been a great conversation.
00:48:44 Anand
Thank you, Tony.
