How AI is Revolutionizing Market Access in Pharma- Discover How Companies are Doing it !

Daniel Levine (00:00)

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Amar, we've got Rick Lloyd on the show today; for audience members who may not be familiar with Rick, who is he? Rick is an experienced commercial leader from the pharma industry. He's the founder and senior healthcare advisor for consultancy Range Health Advisors. He has a BA in business economics from Brown University and an MBA from the Wharton School of Business. He started his career at McKinsey and he has spent more than 20 years with Novartis,

first in various leadership roles with Novartis Consumer Health, and more recently leadership roles in Novartis Oncology as Senior Vice President. While in oncology, he led the US market access organization, the global market access organization, and data and digital organization, among a lot of the other positions. But this unique combination of experiences positions him to offer insights into the role of data analytics and AI

in market access. And what are you hoping to hear from Rick today? So we have touched on many aspects of drug discovery, development and health care on the show so far, but we haven't talked a lot about market access. Rick brings a wealth of knowledge about that. And we're going to talk particularly about the application of data and analytics and AI to navigating the complex issues around market access, pricing, and reimbursement.

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Thanks for joining us. We're going to talk today about market access for drugs and how drug companies are using data analytics and AI to better navigate payers, pricing and the regulatory landscape. So let's start with some terminology. When I joined commercial side of the pharma, market access was a very new concept for me. And for those in the audience who don't know or understand it, what is market access? Amar, a great to be here today and that's a common

question for even people within pharmaceutical companies. But in summary, it's the function that develops and helps with the implementation of strategies to secure reimbursement for medicines from health care payers and ensure patients have access to medicines. And payers in some geographies or private payers and insurance companies, other cases, it's government organizations or governments directly.

And if that's the one sentence explanation, I think about market access as really having three steps. The first step is demonstrating the value of a medicine. And that involves market access coming up with the evidence strategy to support access. What's the clinical value, the patient value, the healthcare system value of a medicine? So market access is giving input to clinical trials,

real-world evidence strategies and other ways to create the evidence that are required to demonstrate the value of medicines to payers. The second is creating the conditions for access. In terms of showing the evidence, usually, see there's  clinical trials and the clinical trials usually the new drug is shown to be better than the standard of care, right? And that's how you get the regulatory approval.

What is different in market access? What are some additional things that we need to show for that? Sure. So to get medicine approved, you need regulators to approve that. The FDA, for example, in the US. To achieve reimbursement and access, you need payers to pay for that. And some of the different

criteria, sometimes clinical is not enough or your product may be clinically major outcomes similar to a competitor, but you can show differentiation in terms of patient outcomes, for example, or you may be able to show differentiation in terms of healthcare system resource utilization.

So the overall cost of care may be lower for a medicine and you want to demonstrate that. That's typically not demonstrated in a well-controlled clinical trial, but it is valuable when it comes to reimbursement. The final thing I'll say is some outcomes that are valued by regulators, for example, in oncology, the outcome of progression free survival or PFS, that's recognized by many regulators

as a viable outcome but for some payers especially outside the US that outcome is not given any credence and therefore you need to rely on some of these other outcomes I mentioned to get the product reimbursed. Okay so can you elaborate a bit more on these like the patient outcomes or like what are some of the different types of outcomes and also

when you talk about utilization, if you can explain that a bit, that would be great. Sure. Patient outcomes, simply put, you could say it relates to the quality of life for a patient. So things that may be beyond the clinical outcomes or measures of scientific effectiveness, but it's more related to the experience that

patients have while on therapy. Could be reduced side effects, you know, is often one of those. It could be higher energy levels and ability to be productive in society. It's those kinds of outcomes that are more specific to patient reported experiences that may or may not be captured in the well-controlled clinical trial. And then in terms of healthcare utilization, if you think about the care

journey for patients. Medicine is one small piece of that journey and also for the healthcare system and for payers it's one small piece of the cost of care. And so oftentimes payers are requesting what is the impact of this medicine on the cost of care for patients that have this condition. And if you're able to demonstrate

similar outcomes or better outcomes from a clinical perspective, but also reducing the overall resource utilization - that's attractive to payers. So like reduced stay in hospitals or so. Exactly, reduced emergency readmissions, you know, reduce time in hospitals, those kinds of things, reduce need for surgery. wow. Okay, great.

And yeah, you also started talking about real-world evidence. So again, can you explain that a bit more, like the real-world evidence and why that's important, why just the clinical trial data is not enough, if you can talk more about that. And what is real-world evidence? Some people don't even understand what that exactly means. Sure, real-world evidence. I'll start with a description. A well-controlled clinical trial is well-designed, very specifically selected patients

who are being treated in a very consistent way, consistently across hundreds or sometimes thousands of patients in the clinical trial. Real world evidence, and before a product launches, that's primarily what you use to get approval and initial reimbursement. Real world evidence incorporates, once a product is on market,

there will often be thousands, if not tens of thousands of patients who take a medicine and receive treatment. And their experiences, both from a health economic perspective, as we talked about resource utilization, their outcomes, whether it be from insurance claim data, whether it be from data that's extracted from EHRs, electronic health records,

those are some of the elements of real world data that are used to understand more fully the effectiveness, side effects, et cetera, of a medicine after it launches, after it goes through the clinical trials. And many health authorities, but also reimbursement authorities, will be looking for real world evidence for the medicine or for other medicines and comparisons

in their patient populations after launch. So that's essentially what real world evidence entails. Okay, great, great. So now we've talked about like the first element of your framework which is showing the evidence, but you are going to explain some more, so please continue. Yeah, so the first one is demonstrating the value of the medicine. Once you've done that, you need to create the conditions for access. And what that means is working with now the

insurance companies, working with reimbursement authorities, working with governments, depending on the country, to establish the pricing and any contracting that's required for that product to gain access to patients so that patients can gain access to that medicine and it will be reimbursed and essentially covered by either government or the private insurer.

And that process is a very complicated process. You're taking that demonstration of value that you have created in the first step and you're combining that with what's all of the data about competitive medicines to one, make the case that there's distinctive value here. A, that patients should have access to it and B, that it should be priced at a

price that achieves a fair return for the value that's delivered and that process while the first process you know requires real world data in well-controlled clinical trials this process is more about working directly with the payers, sometimes you know, making - always making submissions and proposals but also detailed negotiations that can be high-pressure negotiations

to ensure that the product has access. And that's the second step, creating the conditions. There are number of different contracting elements and each of the countries and many different payers have their own different criteria of how to do that. So the complexity multiplies as you get to this step of creating the conditions for access.

And anything else after that that comes for market access? Well, now that you've demonstrated the value, you've created the conditions for access. The final step is around enabling access. And I'll use the US here as an example, because enabling access is a very significant challenge in the US market. You're essentially helping patients and helping

physicians, reimbursement managers, other stakeholders in the healthcare ecosystem navigate the complexity of this reimbursement paradigm. You know, create, manage the complexity of the reimbursement process to enable patients to gain access. And I'll come back to the complexity, but traditionally there are two elements to this. The most traditional is patient services.

And those in the pharmaceutical industry are familiar with patient services and patient hubs where pharmaceutical companies are working closely with individual patients to help them navigate who is their payer, what product did they have to try before they get reimbursed for this one, what evidence do they have to submit that they have the medical necessity for this product. It's overwhelming for patients.

So patient services is part of enabling access. The second part of it is an area that's really emerging, which is around access realization. And that's helping the other stakeholders, physicians, office managers, bio coordinators, nurses that are involved in this process, helping them navigate, how do I help my patients get the information that they need so that they get access to

the appropriate medicine. And so all of the complexity that occurs in demonstrating value and creating the conditions for access flows down to the patient, to the physician, to the reimbursement managers, to areas that requires concerted effort by pharmaceutical companies around enabling access. This is just amazing, the way you explain market access. Really appreciate the framework and how you explain that.

You mentioned  a few times that there's the US versus the other countries. Can you talk about what are some of the big differences in market access between the US and some of the other countries? Yeah, in my experience, I led US market access teams, led global market access teams, and the difference between the two is striking. In the US, one of the signatures

of market access in the US is the degree of fragmentation. Whether it be the payers themselves being fragmented, the insurance companies, whether it be the plan types, for example, in the US, a pharmacy benefit product will be paid for through a pharmacy benefit manager, whereas a medical benefit pharmaceutical product will be managed through

different plan, often in a different insurer, even though it's a pharmaceutical product. And then the healthcare delivery systems in the US are also extremely fragmented with a lot of private, nonprofit, for profit, you know, very diverse set of delivery systems as well. Typically ex US that's more centralized. And sometimes that's, you know, easier because

the decision making often around reimbursement for a medicine, the national reimbursement decision is also often one of the most fundamental to occur. Whereas in the U.S. that fragmentation means it's a payer by payer decision. And, you know, just in one summary, what does that mean for market access? When I was in a global market access role, I would share with people that

80 % of the work of that low that market access team occurs before a product is launched. I need to get the evidence right. I need to get that national contracting right. And when I get that right, I launch and then I have 20 % of the effort, you know, from then on,  whether it be renewals or renegotiations, those types of things In the US, I would say that 80 % of the work comes after a product launches.

And that's because every year, every payer goes through a new policy design and plan design. You know, every year there are changes, the fragmentation and the effort to enable access and realize access is a lot higher. And therefore pharmaceutical companies have to put a lot more effort into that phase of market access. Those also lead to one final thought is just

optimizing the value and the evidence strategy across markets, the first step we talked about, is exceptionally complex because you're dealing with such different stakeholders. When you create an evidence strategy, you're doing it to try to gain access to the US market, to UK and Germany and China and Australia and Vietnam and all the different

requirements they have from an access perspective - mean this evidence planning and strategy is very complex. Yes, it sounds very complex. And we hear the things like the NHS in the UK, right? And they said no to some drug or so, right? And those kind of news that we hear. it's basically their national system saying no to the reimbursement, right, for a particular drug in the UK. That's right.

When there are centralized bodies making decisions like NICE in the UK, there are situations where the evidence package is essentially not accepted by the reimbursement authority and therefore a medicine that may be available in other markets either ultimately isn't available in a country where that decision is made or

availability and access comes later when additional evidence is developed and able to be shared. Pretty interesting. Yeah, so we've talked a lot about market access itself and thank you for giving this really  really nice introduction. Let's talk about AI in market access. So what trends are you seeing right now  about AI market access? Well, some of the trends that create the conditions for the value

of AI. One we already talked about, this, you know, complexity and fragmentation. It's just a means to market access. A second is the overall trend for more specialized medicines. You know that that creates more therapeutic alternatives and faster changing standard of care and when reimbursement decisions are made you know that standard of care is often what the reimbursement authorities are comparing to.

So the pace of change and the need to sometimes supplement your well-controlled clinical trials, that need is going up. And then the third, there are things that are compressing timelines. So the IRA or the Inflation Reduction Act in the US is putting pressure on pharmaceutical companies to realize the value of their medicines more quickly

and more urgency. So things that used to take nine months, you want to get that done in three months now because there's real value there. And the urgency that the IRA created, the urgency that faster evolution of standard of care have created, have created an urgency for market access organizations to be able to navigate this complexity in a way that drives access

in a much quicker manner. Okay, interesting. So let's now start going deeper into the AI for market access here, you know, going back to your framework, right? So the first area you talked about, the demonstrating the value of medicine, how can AI or analytics help within that area? Sure. And before jumping right into that, one thing that I think about in terms of just

the highest level, very simple use cases for AI. One is enhancing human productivity and that could come in many different forms. And the second one is generating superhuman insights. The ability of pattern recognition and the ability to generate insights that no human on a spreadsheet could ever have imagined. So as we go into demonstrating the value of the medicines, that's how I think about,

you know, what are those use cases? If you  take demonstration of value, you know, some pharmaceutical - every pharmaceutical company creates a global value dossier. You know, this is a summary of the evidence that is used to demonstrate value along with disease background, along with disease burden, burden of disease prior to

you're coming to the market. So a very complicated hundreds of pages, what's called a global value dossier outlining the value of the medicine in great scientific detail. They're thinking of those high level use cases. There are probably three things that in that value dossier that can be transformed through AI over time. You know, one is the use of generative AI

in the near term of enhancing the efficiency of creating components of that value dossier. So whether it be something like disease burden that primarily uses public data, you know, using generative AI trained to draft a section, you know, what's the disease burden in this area? That's one example. A second is you can definitely see the ability to use

generative AI to enhance the just productivity and efficiency of the production of the entire dossier and there are you know global teams, global organizations that are working on that right now. You know, there's a couple use cases that they're working on. A third which is more, I would say, you know longer term and aspirational is on the insights side, right? There's no reason

that over time, you shouldn't be able to train a generative AI model to digest the competitive market clinical standard of care context, as well as digest all of the internal data that you have about your medicine and help shape the value strategy,

shape the evidence strategy, not only at the global level, but down to what do we need to supplement in individual markets that we didn't do at the global level - really leveraging the insights that can be gained through artificial intelligence to shape that strategy. It's a third opportunity there. And probably for another whole podcast

is post launch, generative AI can be used in the areas of real world evidence and there are organizations using it today. In a way, you and I will go to Gemini or chat GPT to ask basic questions. They'll have researchers and individuals within the pharmaceutical company going to their RWE

version and gaining insights that can unlock both reimbursement and evidence for reimbursement, but also many other uses in terms of what patients are responding, what new indications should we go for, et cetera. So how do you leverage real world evidence in market access is definitely one of those applications. But real world evidence also goes beyond market access.

Yes, absolutely. So in terms of, so we talked about real-world evidence and this just creating the global dossier and those could then be used easily like as you talked about, different countries in the world are going to require different types of evidence maybe so those can be then channeled into the different countries. Yeah, that's right and as you think about the

in the secondary, we talked about creating the conditions for access. That's also when you start going into the individual countries, because you take that global value dossier, which by the way, we've now completed in two months instead of nine months, because of AI. And you need to create your own local submissions and those, whether it be NICE in the UK,

you know the German authority which takes a very different approach from NICE, or the US payer who takes a very different approach from NICE or Germany. So you're creating now a local submission document. And all of the productivity elements, you know, also are relevant there in terms of the ability to streamline, you know, the production of different segments, etc

There are people that are working today on informing the negotiation strategy, you know, and predicting the negotiation outcomes. Leveraging right now, and I saw it for years, you have very smart people who have been in a country for 10 years working with that health authority, and they're using what's in their mind to help inform

the best strategy for that submission and how to position the value dossier. If you're able to digest and you had someone who knew all of the details of every submission, all of the outcomes of each submission, one, it could help with forecasting, hey, this is our evidence package. What kind of outcome do we think we're going to get if we added this evidence in?

And that sounds easy, but adding evidence, creating new evidence can often cost, you know, millions, tens of millions of dollars. But if we add this in the potential outcome with NICE could change like this. And therefore, you know, we should go ahead and invest that. So creating tools that help people predict, know, reimbursement outcomes and inform those submissions

is something that people are working on today as well. So looks like in this new world where the AI agents are coming in, it looks like we need a market access AI agent who is able to then take a lot of information, get some insights, and really drive the strategy. No, I couldn't agree more. And to where we started the conversation today, market access is

you know, a complex, not super well understood, you know, segment of the pharmaceutical market. And what I see in some of the work I do in this space is even at the fundamental levels of cleaning data and linking data and understanding the basics, you often don't have that knowledge as a starting point in the teams that are doing

the technical work and therefore the outcome is pretty poor. So yes, you need those agents that are informed by the experts in this space, just like you've done, Amar, I know you've done work in this space. It's partnering that expert capability with the technology. Absolutely.

Let's talk about pricing. That's something that people talk a lot about, right? So how can AI help with setting the prices of the drugs in the different countries? So if you take, let me first talk a little bit about the US and then, actually, no, I'll start with what we were talking about before, let's say in the UK or Germany. They have very well

defined approaches to assessing medicines. In the UK, it's something called HTA, Health Technology Assessment. And so they're using HUR and Health Technology Assessment, HUR, Health Economics and Outcomes Research Evidence, HTA models, a lot of language. But bottom line is they have a

defined approach to articulating in their mind from a UK perspective the value of a medicine and every pharmaceutical company is working to provide the evidence that's required, not only the clinical evidence that often gets you the regulatory approval but some of those other elements of evidence to fit within their

requirements. So as I was talking about earlier, if you're able to understand, you know, every submission that's ever occurred, you know what's worked, what hasn't, you can determine more effectively how: one, you should shape your evidence strategy in your submission; two, how you should present that. Right. So those are,

you know, a couple of different ways. I guess the last one is often in those submissions, they rely on some degree of real world evidence, whether it be about an existing standard of care, whether it be about existing resource utilization, whether it be, you know, about using that existing data to project into the future, the ability to use

AI to more effectively translate real world data into the evidence you need. That's another way to affect pricing in HTA markets or markets that are more central decision making. Okay. Okay. So that's one. And then in the US,

it tends to be more of a competitive negotiation. First, based on therapeutic interchangeability and therapeutic necessity, US payers, you know, look at that first. But then once they determine that, Hey, these two medicines have a similar therapeutic effect, now it becomes a very

capitalistic negotiation on price. And you know, who is going to be in a preferred coverage position? Who's going to be in a secondary coverage position? And a preferred position may mean that you have to try product A and fail product A before you try product B. Right. And so in those negotiations,

10 years ago, I was sitting in that chair, you know, day to day having those negotiations. And the best we were able to do was more or less directionally, you know, look at our thumb, see which way, see which way the wind was blowing and try to guess as to whether, you know, that, that difference in coverage would have a difference in outcome. But now using AI, you're able to look across

a lot more categories of medicine, you know, look across a lot more payer situations, plan types to make much better informed decisions about how much discount should I give for that preferred position? How important is it that I be there? So it really elevates, brings superhuman intelligence to the decision making

around those negotiations. So it's interesting. So a lot of the inputs that go into the pricing, AI can help a lot with that, right? And then come up with like a model for pricing, whether that's in different countries or like with the US, right, as the contracting is happening there. Yeah. And to link some of what we've talked about already, the evidence plan is part of what will help you set your

your ultimate initial price. And so the more you can use superhuman intelligence to have a smarter initial evidence plan, you can likely set, you know, a more appropriate price that captures full value or deliver more value and capture more value. Then that second level, which I described is the negotiations,

whether it be with the HTA authorities or payers. That's a second area where you can use precedent in advanced analytics to make more informed decisions related to the steps you take and ultimately the net price that you end up with. That's fantastic. Now, in terms of the different pharmacy channels, is there like  

that you need to work with for reimbursement? Do you have any thoughts about how AI can be used there? Well, one area, if you think about the third step I mentioned of enabling access. As a patient, you encounter a lot of complexity.

What are the two medicines I have to take before I can get approved to be reimbursed for this medicine? What is the pharmacy channel I have to use to get my medicine? What's the infusion? Where can I get my infusion if it's an infused product that takes four hours to be infused? Well, this infusion center is covered. This one's not. So all of that lands

on the patient. And the patient, with the help of pharmaceutical companies, often through patient services or patient hubs, are working to navigate that complexity. And this is one area where the use of AI can actually be different from what we're talking about. Now we're talking about customer experience. Now we're talking about customer experience AI.

So a company like Authentics that uses AI, listens into patient conversations with the hub to one, improve those individual experiences. I've seen they're doing like sentiment analysis. They're doing, how is the voice of the patient? Are they frustrated?

Is the call agent using language that the patient can understand? And are there insights we can gain around consistent questions that are coming in? Typically, that work has been done by sampling of 2 % of calls that come into a call center. Now you can use AI to enhance that patient experience to

be more effective through both improving the performance of the people who are engaging, but also using the insights to improve how you support them. Yes, yes. And as you said, the new aspect now coming about the access realization, so working with the health care professionals, about this. So AI can maybe help them as well, right, in terms of getting the right information, even finding the right people there as well, right?

Yeah, so in this area of access realization, which again is supporting other healthcare stakeholders beyond patients, physicians, reimbursement managers, bio coordinators and others. Reimbursement in the US in particular is moving from a coverage based world. And this is where are you covered? Are you not covered? That's what people needed to know, more to a policy based or criteria based world.

And what that means is now it's about the details of, you're covered, but you have to go to this location. You need this test. You need to have failed these three products. So there's a list of what's in the policy. What are the criteria for reimbursement? So when you move from a coverage-based world to a policy or criteria-based world, the complexity increases. Physicians can't keep it straight.

You know, the office managers can't keep it straight because in the healthcare system, they're dealing with, you know, not one or two products, but tens or hundreds of products, depending on your specialty. So enabling pharmaceutical reps, pharmaceutical reimbursement manage, pharmaceutical account managers with the information they need to help their customers and / or supporting the customers directly

is a very important emerging space as we move from a coverage based to a policy based world. Great. Great. So Rick, we have discussed a lot of use cases here for AI in market access. Beyond what we have discussed, do you have any advice for executives who are trying to harness AI in market access? Yeah, one of the hardest things for people to do is figure out how to

take a portfolio approach because you can never invest in every opportunity that exists. So one stress is take a portfolio approach, which means for me, one, don't shift all of the focus to AI. Always pursue areas that are high impact where capabilities are substandard, where technology is proven. Like do those. And in the area of access realization is one example. even, we're working with a company called Accessing. It's around getting the

data right and marrying the expertise with the data to enable pharma companies to provide the right information to their customers to help patients navigate. So that's one. Don't shift all the focus to AI. But when you do, expect the most in the near term in areas that enhance human productivity for centralized groups of experts.

When I talked about value dossier creation, what I envision is you're creating the first draft of the value dossier and the experts are determining how good a job AI did. So focus on those areas first. Don't go right to the patient. Because if you go right to a patient solution, your patient solution has to be 99.99 % right if you're a pharmaceutical company.

Start with superhuman productivity enablement of your experts because if they're 95 % right, it's still a huge improvement, right? And it's still a huge part of it. And then finally when I think about superhuman insights and applications, just first think through and make sure you have quality data, right? It doesn't have to be organized, doesn't have to be neat, but make sure and I've used the UK

HTA authority as an example a lot but a better example is Germany because in the UK oftentimes you don't know the final outcome from other players, other companies' negotiations. You know what they submitted; you know so much data but you don't know the outcome and if you don't know that, it's gonna be hard to predict, whereas in Germany all the outcomes are public.

Right. So start in Germany where you feel like you have robust amount of data, you know, so focus on data, I guess, is that third element. Right. Great. This is a lot of great insights. Now, where do you see AI having the greatest impact

on healthcare. It can be within access. It can be beyond. You're an expert in a lot of areas. So just wanted to have that as like a larger question of how do you see this? Yeah, short term I see it as human productivity for experts. And we've talked about that in value and access, medical writing, regulatory writing, all of those things that can shorten

the time to market for medicines, which has a huge impact. A third area, another example there is assisting physicians and healthcare providers because they need assistance. We do not need to replace physicians. We need to help them. I do think that AI can be an area that increases their productivity as well.

So enhancing human productivity in the near term, longer term it is about those superhuman insights. You know, I think one area is truly discovery of new mechanisms and new medicines. Yes. And the reason I say that is I had the insight a while back when I heard the first AI generated song. You know, it's an app where you give it a

Give me a song about and write a sentence and give it a type. And all of a sudden you get a song, a pretty darn good song. So a lot of Gen AI is about language. And so we all can understand languages, Latin, right? And it's very easy for us to understand images. Music is broken down into zeros and ones as a language now. And

people like, I remember listening to one of your podcasts earlier, Amar, with the CEO of DeepOcean, and there's another company, sorry, DeepGenomics, and another company called OceanGenomics, and they're looking at the language of RNA and genomics and proteomics, right, and transcriptomics. Their language is there that just like,

you know, using generative AI or machine learning even more heavily, but to make sense of a song with a couple of prompts, you know, that same technology, now that language is applied in these areas, I think ultimately will lead to tremendous breakthroughs, even though there have been setbacks early on. I think that's the greatest long-term impact for society. Great. So Rick Lloyd,

industry veteran and founder and senior healthcare advisor for Range Health Advisors. Rick, thank you very much for your great insights today. Thank you. It was fun, Great seeing you. Thank you.  

Amar, we haven't taken a deep dive on market access before today. What did you think? I thought this was an excellent lesson in market access,

really going deep into, well, you know, what are the different areas in market access and talking about each of those and how they actually - each of those areas are then actually, you know, help in getting the drug in the patient's hands, right? So it was very well laid out. And then again, not only just the basics of market access, but then what is the application of AI in each of those? I think Rick talked about

at least 50 different use cases here. It was a pretty comprehensive lesson in market access and AI market access. You talked about the growing interest in real world data. Payers are often interested in different data than regulators. How might AI improve the ability of drug companies to call on real world data to make the value case that payers care about?

So real world evidence is now definitely getting more and more important because in the clinical setting, that's a bit of an artificial setting. That's why a lot of times the results that you see in the clinical setting, they are usually very good. And it's hard to replicate those results in the real world setting because of the real world issues. So that's why a lot of these

reimbursement health care authorities, they do care about, okay, well, what is actually happening in the real world rather than just in this kind of clinical setting. And the other thing also, as Rick talked about, right, it's not just about what was the primary endpoint that was met in the clinical trials, which could be overall survival or so, but what the reimbursement authorities do care about is a lot of other things as well, quality of life, a lot of some of the other -

a lot of the other metrics, but also the utilization as you talked about, How much of a burden is it to the healthcare system? Is this drug going to alleviate, right? So these are a lot of these things that you're not necessarily capturing in the clinical trials that you do need to capture for the value dossiers that he talks about for reimbursement. So yes, just getting the approval of a drug is not enough these days. You have to get the reimbursement as well.

He talked about the complexity and fragmentation that characterizes market access issues. It would seem this is something AI would be well suited to tackle and also provide substantial labor savings in the process. How do you see AI changing the challenges of market access? Will drug developers be able to be more efficient or actually do things they couldn't previously do?

Yeah, the way I see it is right. Like I mean, there's a fragmentation in the US market he talked about, but also he was talking about the different countries outside of the US and then even asking for different types of information. So it's not just  enough to just have the same value document, value dossier and just giving that to a lot of different countries. So you do have to customize that for every country, which is huge. And when you look at that

that task, I would say AI, that's why I did bring up this market access AI agent concept during the chat there. But see, the AI can have the ability, especially generative AI can have this ability where it can understand the nuances of the different payers within the US. mean, those can be programmed into it or it can learn

those different nuances of the different payers in the US, but then also it can learn about what are the different requirements in the different countries. And also what he talked about was even the negotiations, based on the different negotiations, what works or what not. All this is for one person

to actually be an expert in all of this is difficult. I'm sure there are some really great experts like Rick in market access who will be able to remember a lot of that, this is a pretty tough task. But with AI, with the AI market access agent who gets trained on these different aspects and then whenever we need some outputs, it is able to

provide that output based on the specifics around that payer, I think that can be done, that can be trained. So I would say it will be tremendously beneficial to bring AI and really simplify a lot of these in terms of like AI doing a lot of the tedious work of how to customize for specific payers based on how we see the characteristics of how they behave, what is the kind of information that they want.

Rick was not only compelling, but it's such a reminder how ubiquitous the impact of AI is going to be on the drug industry here. Until next time. Thank you, Danny.

Thanks again to our sponsor, Agilisium Labs.

For Life Sciences DNA and Dr. Amar Drawid, I'm Daniel Levine. Thanks for joining us.

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