The Business of Health with Chip Kahn

When Minutes Matter, What Is AI’s Role? 

May 12, 2026

Video

Audio

About this Episode


Episode 3, AI Series: When minutes matter for a patient’s care, what is AI’s role in clinical practice? Elad Walach, co-founder and CEO of Aidoc — a company with a comprehensive AI health care platform that analyzes real-time images and flags time-sensitive findings — shares his perspective on AI’s transformative power in dodging diagnostic error, improving access to care, and ensuring care quality in busy clinical settings.

The Host


Headshot photo of Chip Kahn wearing a navy blue suit with a red tie, red pendant on lapel, and glasses.

Sr. Visiting Fellow

Charles N. Kahn III is a senior visiting fellow at KFF. He is also a visiting senior fellow at the American Enterprise Institute and a nonresident senior scholar at the University of Southern California’s Schaeffer Center for Health Policy & Economics. He serves as co-chair of the international Future of Health collaborative.

Guest


CEO of Aidoc

Elad Walach is a co-founder and CEO of Aidoc. He is an expert in AI with visionary business insights in the healthcare space. Since establishing Aidoc in early 2016, Elad led the company through four investment rounds bringing the total investment to $250M, drove the commercial availability of 20 product lines, created an install base of over 1,600 global hospitals while growing the company to over 450 employees.   

Transcript


AI Usage Disclosure: This transcript was created with assistance from AI tools. It was reviewed and edited by KFF Staff.

Chip Kahn: In our earlier episodes, we covered the strategic landscape for AI in health care and drew the line between what AI can do in clinical settings and what it cannot do. This conversation goes to the front line, to a firm that has put FDA-cleared clinical AI into more hospitals than anyone else. For most of modern medicine, the great leaps forward have been physical things you could see and touch. A new drug, a surgical technique, a new machine. But what we are witnessing now is something different. The new medical miracles may come not from a molecule or a device, but from digits, from patterns detected in data, from algorithms operating in an abstract, informational space that no surgeon’s hands will ever reach. That is what makes this episode both exciting and maybe a bit unsettling. You will learn that Aidoc’s platform is running right now in more than 1,600 hospitals worldwide, analyzing over 70 million patient cases a year. It holds 32 FDA clearances, the most in its category. When a patient gets a CT scan in an emergency department that runs Aidoc, the AI analyzes the images as they complete and flags time-sensitive findings. Brain hemorrhages, pulmonary emboli, aortic dissections. So, the radiologist sees the most urgent cases first, rather than in the order they arrived at the ER. When minutes matter, that patient lineup can be the difference between a good outcome or a catastrophic one. Elad Walach co-founded Aidoc in 2016, years before the current AI hype cycle. He came from national security AI where the lesson was that cutting edge algorithms mean nothing if they are not actionable. He has built the most widely deployed clinical AI platform in health care. And the question today is what that deployment has actually taught us. How will it change clinical practice? And can FDA-cleared AI change patient outcomes at scale or will obstacles presented by payment structures, workflow constraints and liability concerns limit dissemination despite the clinical validation? Elad Walach, welcome to KFF’s Business of Health with Chip Kahn. 

Elad Walach: Hey Chip. Great to be here. 

Chip Kahn: It’s so wonderful to have you here because you really represent what we’re trying to get at in this series, which is the ultimate application of AI. And that’s what we’re going to talk about. But before we get into, Aidoc, your company, and talking about the specifics of what you do, I’d like to give our audience some background and ask you how did you get from national security, in Israel, where you were doing AI to health care? And in a sense, what did that transition teach you as you went? 

Elad Walach: I do not come from health care originally as you’ve mentioned, I led an AI division for more national security purposes in Israel. But I always had this health care bug in me. A lot of it was through my father, who’s been a huge influence on me. So, when he was younger, years ago, his sister unfortunately passed away due to diagnostic error. And I always had this influence in the back of my mind. It also influenced him. He was actually working in IBM research at the time, and he was one of the people that actually pushed IBM to go into health care, back in the day. He would tell me all the stories on what they’re working on and kind of ingrain that passion into me. When I finished my service together, my two co-founders, we all finished our service at the same time. We said that’s what we’re really passionate about — helping improve quality of care, helping mitigate errors and improve access. And we just spent a lot of time in hospitals. Basically, because we didn’t know much about health care, we spent about a year to a year and a half until we’ve started seeing these recurring problems, recurring problems of lack of access, recurring problems of amazing people who are the clinicians barely holding on in to this crazy system. And that’s why we decided to start Aidoc, really with the mission of improving access to care, improving quality of care. 

Chip Kahn: Before we get into Aidoc, and I want to take you back to 2016 and the genesis but what is it about, Israel, and technology and health care that come together? I mean, I’ve spent a lot of time in Israel and done work with Sheba’s ARC, their innovation center. What is the sauce there that’s produced so much innovation? 

Elad Walach: First of all, we’ve been proud to be working with Sheba’s ARC from the early days. And I think there are two things that work well in that ecosystem. First of all, we’re all very pragmatic AI people. So especially in the early days of clinical AI, people could get really excited about the model building. And especially I did a lot of work with professors in academia and they could get super excited about this new model coming out or this new paper and always trying to test stuff. But I think you kind of learn about a lot of the practical application and there is a lot more into making an impact than there is to the model-building. You would find a lot of innovation in Israel because of the fact we have to make things work. We’re becoming very practical. And that was a big aspect, by the way, of how we built Aidoc. The second aspect it is, you, know Israel is very small, and that actually has an advantage because everybody knows everybody. If you want to get a clinician together with a technology guy and a business guy in a room brainstorming ideas, it’s much more possible. It’s very easy to kind of find the right cohort. 

Chip Kahn: That’s interesting. And just for the audience that, probably don’t know, Sheba Medical Center is, the largest hospital in the Middle East, 1,600 beds and just a tremendous complex and really a university unto itself, in terms of the teaching and the research that’s done there. So, let’s go back to 2016. And you, got into AI before all the hype. And what did you see that others didn’t see? 

Elad Walach: I sometimes say I got into AI before it was cool, but it was always cool. So I, wouldn’t say that. But look, I think we’ve realized that there are intractable problems in health care that are going to be very hard to change, manually, basically. And we got into AI in the 2016 world. That was just the first time where deep learning came into being. So those that don’t know, machine learning before that was much less accurate. And it’s about that time when deep learning was invented. And that was the first time we could build an AI that was good enough to provide results that are clinical grade or physician accuracy. Not with the aim of replacing physicians, but finally you have to be roughly as good as them to be actually be able to support them. And that was only possible for the first time. Then there was a tradeoff, by the way, which we can talk more about, but the tradeoff was always that you can be really accurate, but you have to be very specific. You could build an algorithm, for example, to find brain bleed. That was the first one we built. It was very accurate. It could do it like 95% accuracy, but it could only do brain bleeds. If you want to do spine fractures or stroke, that’s a whole different model. And that was the world back then when we got started. 

Chip Kahn: So, the focus is on imaging, and the focus is on the emergency room and this indispensable technology, the CT scan. Talk a bit about when a patient gets a CT scan and what Aidoc does. 

Elad Walach: Clinical AI, the category we’re in, I think, is the category where people are thinking about health care AI, that’s what they’re imagining. So, Chip, you’ve mentioned that’s a lot of the conversations we are building up to. To be clear, I think we need to tackle the administrative burden. The backend offices describing all of those are really important problems. Clinical AI is really touching care delivery and boy, is that an area where I think health care needs the help. We’re stretched so thin. We have amazing, amazing, passionate clinicians, but the system they’re surrounded with is so overwhelmed. I just read a study today that shows that in ‘22 and ‘23, in two years, we more than doubled the wait times for outpatient imaging diagnosis in two years. The shortage of physicians is becoming so profound. It’s literally impacting access and quality of care. Another study that’s really influenced me is a study by a group at [Johns] Hopkins with David Newman-Toker who did a study about the impact of diagnostic errors in the U.S. and every person probably listening to this will suffer diagnostic harm due to diagnostic error in their lifetime. And the group estimated we have, every year in the U.S., 400,000 deaths due to delays and diagnostic errors. That is such a big number, it’s hard to wrap our heads around. And that is where I don’t think there are any manual things we can do to solve this problem. We threw labor at the problem for years and we’ve made some impact. But the problem keeps growing and growing and growing. The only way out in my mind is technology. And what we need to build as a society is this ubiquitous layer that analyzes every diagnostic encounter and provides this kind of second set of eyes that would help both the patient get accurate and timely diagnosis, the physician do their work efficiently. To imagine what Aidoc does today, we do exactly that for imaging, either as an ED patient or as an outpatient. You go to a facility that has Aidoc, you would get kind of this clinical AI platform, also analyze your data, and then identify certain types of findings. So, let’s say you’re in an ED with an abdominal pain, and today you’re going to get a scan and you’re going to go on a list with, maybe, 100 other patients and you’re going to be waiting for your diagnosis. With Aidoc, basically, the AI look at your scan and find whatever diseases are there. Let’s say it would now scan you for 15 different diseases and we’d say, oh, you likely have appendicitis, or you could have maybe a liver lesion, and it would then prioritize you for a radiologist read almost immediately. Or a pulmonary embolism is an example. So instead of waiting tens of minutes or an hour, you could get the results almost instantly and then get your next step of care. So that’s an example today of how patients get an impact through this technology. 

Chip Kahn: So, to get this into the hospitals in the United States and get it disseminated…was it used in Israel before it came here? 

Elad Walach: Actually, in tandem with Israel and the United States…  

Chip Kahn: The first sort of regulatory hurdle is FDA. You’re in 1,600 hospitals. I want to talk about that in a moment, but let’s start with the regulation part of it. How do you deal with FDA with this kind of technology? How receptive was the regulator to this really disruptive technology that changes things completely? 

Elad Walach: When we first started this, I was imagining FDA as this, you know, bureaucratic wall, that is more process than anything else. But through the years, I’ve actually learned it’s people, and it’s actually really passionate people about changing health care. The interaction is not like you’re submitting this and then you forget about it. You actually have continuous interactions with the agency. We’ve submitted more than 30 submissions to the FDA, so we’ve seen that often through the years. And they’re very passionate. And I actually believe they’re providing a critical service to the adoption of clinical AI. The way FDA reacts to it, they’re saying, we understand this is going to be one of the most transformative changes in care delivery we’ve had in decades. But on the flip side, we see a responsibility to both keep patients safe and create a layer of trust. Trust is going to be a determining factor in what gets adopted, what’s not going to get adopted, and, also, what’s getting used and what’s not getting used. It’s how do I know that you work in a safe manner? And I think FDA is actually serving a really critical function in that way. And what they do is they basically ask us, look, tell us what you think your device does, what is your claim, and then let’s validate that. The issue is that our claims keep changing as the technology evolves. But, they’re with us on the journey to keep evolving how we test these. I would say our testing methods really changed over the years as the technology expanded in capabilities. 

Chip Kahn: So, in terms of first adoption, you’ve got health systems and you’ve got physicians, you’ve got radiologists and emergency room doctors, who are going to be either aides or obstacles here. And you’re at 1,600 hospitals worldwide right now, which is a phenomenal number. But I assume getting that first one or two here in the United States was a real hurdle to talk them into testing this. And then you had to go through a process. Can you talk about that process? I know with others I’ve talked to who brought startups in the United States from Israel, the biggest issue is how do we show what we can do and get a place to demo this. 

Elad Walach: It all comes back to two things that were critical. First of all, it’s the topic of trust and safety and quality. Hospitals and health systems are very concerned on partnerships, both for patient care reasons, for cyber reasons. You have to show them. Even when we were a way smaller startup, right, we had to show them that we’re going to handle this relationship with the utmost care and that breeds trust. And I think that’s going to be really key for adoption. The second thing is they were really compelled by the vision of what we offered, especially in the early days, by the way. I believe in AI. It still is true today. You don’t partner on product, you partner on vision and roadmap and belief in execution. I think AI is in such rapid innovation right now that if you’re actually just evaluating a product, you’re valuing the wrong thing. Because a year from now, the product is going to be different and the value you’re going to get is going to be different. And you always have to think not just what the product that I want can do, but who am I going to bed with because that is going to matter a lot more than anything else. 

Chip Kahn: Well, that actually maybe is a good opening to talk aboutWellSpan expanding from radiology AI to 21 care pathways, across nine hospitals. What convinced them? 

Elad Walach: With WellSpan specifically, we started with six products. And each product, when I say product, I mean a different disease. So, think about, I mentioned the example of an abdominal pain or a brain bleed. Each one of them was a different disease. During the early phase of the partnership, it was all about obsessing about showing value. And that is a really, really important lesson learned. A lot of people in AI world are focusing on the model or focusing on the product. Both are awesome. But what people really care about in health care is outcomes. So, the real question is, what are the outcomes we can achieve? And for them, the things that they really cared about was time to diagnosis. In their backlog was efficiency. They want to make, you know, to make the reading more productive. They want to ensure their care is coordinated better. And within about 12 months, we were able to show massive improvement across all of these metrics enough that they said, look, we want to go big. On their end, they realize that AI is going to be one of the most important aspects of their strategy to moving forward. I think we all probably believe that AI is going to be a transformational technology, but the implication of that is that we have to change how we operate to absorb it and adopt it in a much more rapid fashion. They’ve basically said, look, we understand we want to build the future, and we don’t just want the future be built on top of us. And that kind of bold leadership is what allowed them to say, hey, we’re going to look at the outcome we’ve generated and then we’re going to go big and we’re not going to do this one at a time. We want in a year’s time to get from the six use cases to over 20 use cases. Once they made a choice, because they had an amazing team, they were able to get there not in a year, but actually in three months from the day we’ve signed. They were like, okay, we’re going to go from 6 to 20 and basically, we’re able to quadruple all the outcomes they’ve seen before, which is only possible, I think, with bold leadership and strong change management practices that their team has. 

Chip Kahn: Just because the audience may not be aware, can you sort of talk about where they are geographically and what the spread was geographically of the operation? You just described this dissemination. 

Elad Walach: I think that the geographical dissemination was less important because it was always around their whole footprint. The big deal was growing across service lines. So how health care health systems work is typically you have one service line, let’s say radiology, or one service line, cardiology. And the way health care AI can be adopted, there are two different approaches. There is the approach of letting a thousand flowers bloom and there is the approach of let’s develop an enterprise strategy. The thousand flowers bloom, meaning I’m going to let every clinician, every service line, pick their own flavor of the day, and they’re going to build their own point solutions. And these point solutions are really valuable. I don’t want to use “point solution”—is not a bad term. There are incredible point solutions out in the world, but that is one worldview: I want to let everybody do their own thing. I personally have not seen that scale in health care. The reason why it’s not scaling is because change is too complicated for health systems, there’s too many dependencies, and we’re lacking a lot of the platform and governance infrastructure to actually scale these one-by-one use cases. The other approach is saying we’re going to determine as an enterprise what is our approach. And yes, it may be less democratic, and yes, it may be less consensus driven, but what we’re going to get in return is velocity. We will get to dozens, if not hundreds of use cases in a very rapid clip because we’re creating the guardrails for that adoption that I think was the key for the WellSpan growth. It’s not just the geography, it’s more about the fact that they said we’re not going to let every service line or every clinician have a go at their point solution. We’re going to define enterprise guardrails, but with that look at the outcomes. Dozens of use cases running in production in less than six months. It’s pretty remarkable. I mean, if you try and do this in a one-by-one fashion, it’s almost impossible. 

Chip Kahn: It’s interesting you describe it like you do because one of the things that was really stressed by Eric Larsen, our first guest on AI, was this notion of at the end of the day it’s going to be top down, in terms of getting the kind of dissemination that’s going to work. This is difficult, this process for the frontline physicians, radiologists, the ER docs, the other docs. And there’s this thing called “alert fatigue” that comes in decision support. And this is really a type of decision support. How do you deal with that? What is it? And how do you make sure it doesn’t undermine all of the advantages you have from the very specific accurate readings that, in a sense, your machine is doing? 

Elad Walach: You’re asking a really good question about both alert fatigue and I’ll connect it to the topic of safety and quality. And both are really, really difficult. People are trying to understand, well, why, why wouldn’t we just use ChatGPT, you know, or Claude, and just let it run on all of our scans to identify signals. And you’re right. I think people treat accuracy as a solved problem, the model is going to be good enough. And I will tell you it is not good enough. It is actually the determining factor of what gets adopted versus what’s not getting adopted. Because accuracy means both safety and quality. You’re not missing critical things. But also, it’s the opposite of alert fatigue. I think we’re all sensing it. Also, why are agents not proliferated in every aspect of our life yet? Because agents are proactive. They analyze every piece of data to trigger an action that is a very, very difficult thing because the accuracy requirements when you analyze every piece of data are way higher. So, accuracy is going to be a really, really important factor in all of this. And in my mind, to create accuracy you have to do two things really well. First of all, on the model side, you have to build really, really good models. And we tried to use a lot of different tier models to help speed our work. It was not very productive. In fact, a few years ago we made the very tough decision;I still remember my hand shaking the day I had to present it to the board. We’ve decided to build our own foundation model. Basically, a model that is like a ChatGPT that can take a scan in this instance and not just find one disease like we spoke about before, but actually every disease, altogether. I remember the day the team had the first breakthrough and the team told me, Elad, the model is working. It can find every disease in like 95% accuracy, which was the accuracy of our old production grade models that have been analyzing tens of millions of patients a year. I was like, that’s great. But then my team told me I was like the overly optimistic CEO. My team told me, Elad, you’re not getting it. 95% is no longer good enough when you’re running on 100 diseases all at once because you’re going to have compounded errors. So, the 5%, to give a simple math, if I have a 5% false positive on a scan and I’m running 20 models, I know I’m oversimplifying the math here. It’s roughly going to be false positive, every scan you’re going to like, we’re going to false alarm the hell out of the physicians. Basically we then realized we have to get our models from 95 to 99.5 to become production rate. People don’t understand the difficulty of getting that extra ounce of accuracy. But effectively the model we built and recently got FDA cleared could get up to 99.7% on the specificity. And that was really the key determining factor. So that was the one aspect of everything you’ve mentioned. There is another aspect to it, which is unrelated to the model at all, and that’s thinking about the governments and the monitoring. Data drift is very, very real. It is not an imaginary ghost. On average, we as the market leader, we have the most volume of clinical AI. Even for us, accuracy drifts about 10% every 18 months, we don’t track it 10% in the accuracies I spoke about is the day and night difference between usable and unusable. So you have to build a governance and monitoring infrastructure to track performance drift, to track data drift, and then fix it somehow. Both of these components, the governance, and the monitoring, as well as the model, are key to get this higher accuracy level, which I agree with you, is the only difference between what you can and can’t use in the real world. 

Chip Kahn: I want to get a deeper dive into some specific examples, but before that, let’s define, when you say data drift, what’s causing that? Because you’ve got a tremendous amount of records that are feeding all the information you have, and you’ve got all these scans, why a drift? 

Elad Walach: I love that question. And it’s very counterintuitive when you’re kind of outside in. I’ll give a simple example. How do you know that a scan, let’s say, is a head scan and contains or doesn’t contain contrast? I would imagine it’s somewhere there in the metadata. It’s written somewhere, somebody knows. Nobody knows. It is all manual. You know, it’s manual all the way down. You need to have a person typically saying, this is a head CT and this is without contrast. And typically you need a human in the loop to determine that. So that is the problem called model orchestration. You need somebody to say, this model, this AI, is relevant to this piece of data. And in theory, if you don’t have another solution, it will be a very manual process. And these protocols keep changing. For example, I added a new stroke protocol, or I had a new type of machine, or I changed this type of workflow, or I acquired a health system, or I had another scanner in the ED. Each one of those changes my data. And because the data is unstructured by nature, that’s what causes the data drift. And you add one plus one plus one, and effectively what you’re getting is a whole complete new data set after two to three years. That is some of the problem we have in health care. When you think about even units of medication, types of medication, types of procedures, all of it changes all the time. So that is why another layer is necessary to both mitigate and then monitor all of that drift. 

Chip Kahn: In terms of this drift, is the machine smart enough to constantly calibrate, or does it take human eyes to make sure that calibration is there? 

Elad Walach: Yeah, that’s a beautiful question. And the answer is both. On the one hand, the machine can do some of it. If you train the model orchestrator good enough, it can fix some of this. But at the end of the day there are a lot of these that are unknown. You don’t know what’s going to change. And it’s very hard to train an algorithm that can face the unknown. You almost always have to have some sort of human in the loop to monitor at least the high-level components of the data. And I think it’s here to stay. I think it’s a new profession. I remember when the first ChatGPT paper came out, they thanked all their team members and one of the roles was they thanked their AI babysitters. And I found that so interesting that we have this new generation of professions of people whose expertise is to really manage these new models and agents and workflows. And it’s necessary. I actually think you have to have a human in the back end holding up the AI to some extent. 

Chip Kahn: And this isn’t just a health care issue. This would be in every data-based process you’re going to have the same thing happening as you get new data expansion and the feedback loop is going to be somewhat distorted, it sounds like, and you’re going to have to constantly keep calibrated. 

Elad Walach: I agree, it’s not just a health care issue. I think it’s slightly worse in health care because of the pace of change combined with the needed accuracy. Again, we’re all going back to accuracy, that it’s not a solved problem. Because safety and quality are so paramount. We have to be much more hawkish on ensuring consistent accuracy and performance. 

Chip Kahn: So, let’s take a deeper dive. Patient comes in, Aidoc flags a brain hemorrhage 30 minutes earlier. Where’s the value versus what would happen without Aidoc? 

Elad Walach: There are multiple areas of value. One area is that you mentioned the emergency department that is truly time to diagnosis, time to treatment. A friend of mine called me to tell me a true story right now. They went into a Mayo institution, and they got pulmonary embolism results in minutes from coming in. So, they came in, they were scanned for something oncological related, and within minutes they got, hey, you have a pulmonary embolism. We need to treat you for that. Because of the increasing backlogs we have, the likelihood of a pulmonary embolism being identified within minutes is very low if you’re not using AI because the backlogs in the ED are increasing by a lot. The other area of value is proactive care, or precision medicine. And I’ll give the example from Mercy, which is a fantastic hospital in St. Louis, what they said is there is a test called calcium scoring. It’s one of the best predictors we have for heart disease. You know, you pay whatever, a hundred bucks, you do a dedicated study. Well, guess what? Most people are not going to take the time of day and pay 100 bucks and get their calcium score. Especially as you go to rural America, right, or more community-based care. The opportunity we have is immense. Today AI can look at the scan and say, hey, we have suspicion, you actually have calcium score. We should take a look at that and we actually can marry that with your clinical record to find the short list of patients that have heart disease risk and are currently unmanaged. The example of Mercy in St. Louis, they’ve decided to be proactive for their patients. And they found that about 6% of all patients doing a chest CT for whatever reason—think about you broke a rib or whatever—have this unmanaged intermediate to high-risk calcium score and are now reaching out to those patients to help them get care. So, think about this, us also moving with AI, the health system from reactive—I’m just doing what I’m told—to proactive. We’ve got your back because we have all this data. These are some examples of how clinical AI can be used to really change the care paradigm. 

Chip Kahn: I understand that one of your customers flagged 10,000 incidental findings in a year and these are conditions that nobody was looking for. What does it mean to sort of treat the whole patient through imaging here, and what are the positives and negatives of that? Because in a sense you’ve got, you know, finding disease. On the other hand, you have issues of liability and payment, and it gets complicated because of the way our system works. 

Elad Walach: It does get complicated. I will tell you, I’ve met a lot of health system executives, and I think they all share the passion for improving care. They’re generally doing this for the right reasons, which is amazing because I get all these warnings at the beginning. It’s all cynical. It really isn’t, like people want to do the right thing for their patients, but obviously we have to be realistic and build sustainable models. In my mind, the key is not just creating problem with “lets us find all these solutions,” but actually help with the management and the workflow of these patients. And I think for that, that’s where you really need to have an end-to-end understanding of the situation. Notice the example of calcium scoring I gave you. I could have ended with the image. I could have said we’re just scanning the patient, looking at the image for suspicion, but I actually added another component. We’re looking at the charts, we’re looking to understand are they managed, what is the clinical history. And then you actually can shortlist the patients that require that follow up care. And that I think is the key. Don’t stop in just the detection in an already overwhelmed system. Help with risk certification to make sure that the patients that we’re finding are those that you actually want to treat. 

Chip Kahn: Let’s talk a bit. And we’re sort of heading in that direction about the business model. I mean, there’s no reimbursement, necessarily, for this extra service. There’s the cost that’s paid, I mean the charge, that’s paid for the scan. So, who’s paying for this? I mean at the margin. Is this costing more money? And then, you know, where does the money come from? And obviously you need to be paid. 

Elad Walach: You have to find sustainable ways to create innovation. Right? I think the people that I know in health care are doing this because they want to help improve care. But we all live in an environment where, you know, building this foundation model I described earlier costs $300 million at least. You know, you’re not going to get $300 million for just doing good, right? So, you have to figure out a sustainable way. And you’re right, there is no reimbursement. The way these models work, you have to find an ROI that is directly good for the health system in a financially sustainable way. And typically, the way these work is you have to show, and obviously it highly varies per the AI solution and disease, but you have to show either you’re improving efficiency, maybe reducing time to diagnosis. So, improve efficiency, that influences things like ED length of stay or driving more revenue because you’re able to create more valuable patient encounters. But whatever which way you’re looking at this, you have to find a way to show that every dollar you’re spending on improving quality of care through clinical AI is also good for them, by improving their operations. Otherwise, it’s just not sustainable. And they’ll just do like maybe a few, but they can’t really sustain that. 

Chip Kahn: And then also, I guess the other factor is that you’ve got the EHRs, you’ve got Epic, that dominates, but you also have Oracle and Meditech and a few others. How do you integrate with them? And then in looking into the future, how do you sort of stay parallel and maintain your business model when in a sense you could argue that something like this should just be part of the EHR, the electronic health record in the first place. 

Elad Walach: I’ll actually start with the second question. You know you’re asking really, you know, really bold and tough questions, which is great. I think that’s what makes it interesting. 

Chip Kahn: That’s why they pay you the big bucks. 

Elad Walach: Yeah, exactly. I think so. Let me start with the second question. I actually think those are two somewhat distinct categories like diagnostic FDA-cleared complex signal AI. FDA actually has a different category for what they call complex signal analysis. Like an image versus what they call clinical decision support, more text based. And in my mind the line draws on the topic of commoditization, what is like very hard to do. And we talked about accuracy, we talked about workflow integration. There are all sorts of these aspects that require this distinct category. And we’re seeing that trend of differentiation between what I would categorize, AI enabled system of records to an AI specific category. If you look at the broader software market, the interesting thing I find is that you find a lot of the AI natives are actually gaining a lot of share for AI-specific non-commoditized use cases while the AI enabled system records are also monetizing AI, but for very different use cases. And again, we’re seeing that play out in the broader software market as a whole. I’m not going to get into an argument who’s going to win in the long run in the broader software market but I think in here it actually is parallel. Those are two different spaces where we’re talking about the uncommodified, incredibly complex diagnostic AI versus doing some of the things that are more native to the workflow kind of semi-tech solved problem that are still very difficult. I’m not going to reduce the magnitude of them, and I do think they’re going to maintain parallel. Should they be integrated? Absolutely. I think workflow is key. Anybody who ever touched health care applications will tell you the same thing. And luckily, I actually think that all the vendors, if it’s Oracle or Epic or Meditech, I think they’re actually all opening up. We found them incredibly collaborative in a way to integrate back into the workflow. I would say the key would be not to take people out of the workflow but actually feed more information in. But I think it’s actually very possible today. 

Chip Kahn: Well, it sounds like from your description that there has to be in proper use of Aidoc integration with the EHR so that you’ve got that data that sets a context for the image, reading, right? 

Elad Walach: Yes, but even more than that, I think that it’s, A, it’s a patient context, but B, it’s about being easy to use and being automatic. The more you’re going to ask people to go outside of their norm, the harder adoption is going to be. So, you have to find ways to not overwhelm your users. If the whole point is to increase efficiency, you cannot ask them to click a button, go to a different system, click three more buttons, go back into the system, copy paste. I just think it’s going to be way harder to drive adoption this way. So, I think the integration into the EHR is going to be key, yes, for patient context, which as mentioned, multimodal is going to be key, but also to be embedded in the way they work. 

Chip Kahn: And so where is the physician, particularly the radiologist here? Who’s had the role historically, traditionally of reading the image? I mean, prior to this technology, you know, you had to wait til the radiologist read the image, right? So, you’re coming in on top. What’s the response of the profession and what are the implications for the future of that profession and the other maybe other, specialties also? 

Elad Walach: So, let me state very plainly. I don’t believe in the “we’re going to replace radiologist” paradigm. I also don’t believe in the paradigm of bypassing. I do believe it will enable better collaboration between the specialties. Because imagine an ED physician that now has these AI flag that alerts both them and the radiologist for the existence of a patient. Maybe for normal findings, they can speed through the ED. I do believe we’re going to see more of these workflows. Generally, I think we have to be extremely careful in how we think about workflow in the context of safety and quality. I know I’m like banging that drum time and time again, but what we don’t want to do is to move too fast and then have this blow up in our faces and actually, taking a decade back, I think there are ways to progress. There are collaborative and are not going to be overly disruptive to any specialty, but actually shifting over time more tasks between the different specialties. I do believe we want to do something like that to allow everybody, basically, to diagnose—radiologist or not—top of their license. And I do think saying that means we will shift over time some tasks but called the bottom of the license work out of that. But I think we need to do it safely, carefully, and it’s going to take us time to think through all the governance, through all the guardrails and how do we do it in a way that really preserves the quality of care. 

Chip Kahn: So, to sort of see what comes next. I think your January 2026 clearance points towards an AI managing whole clinical workflows. And you’re beginning to hit on that. Where is that headed? 

Elad Walach: I mentioned foundation models before. I think it’s the most transformative technology for care delivery, honestly, that we’ve seen. So, what are foundation models? I’ll say it briefly. It’s basically as mentioned, instead of finding one disease at a time, it’s a model think about like a ChatGPT where you can upload a scan and find every disease all at once. And also, as mentioned, with way higher accuracy than 99.5. It’s quite incredible. It’s expensive to build, but once you build it, it’s incredibly powerful. Today, an average health system using clinical AI with a platform would adopt 12 use cases, 12 different diseases. I think we’re a year and a half away with foundation models that we can cover every disease on an image, at least in CT and X-ray. My belief that a health system will be running over 100 clinical AI or disease detectors, at least an average system. We’re not that far away from this. I know it sounds crazy right now, but that’s the nature of exponential growth. Things like we can’t imagine are growing very, very rapidly. What I’m imagining in the future is that clinical AI will be as ubiquitous as driving with a seat belt. Right? You can’t think of going into a car and not putting your seatbelt on. I think similarly we should imagine a world that no diagnostic encounter doesn’t have this AI layer supporting it. And with foundation models, I actually don’t think we’re that far away. It has been that transformative and as you mentioned, it’s very new. We’ve got, to the best of my knowledge, world’s first clearance for a foundation model-based application. This was like a couple months ago, so, it’s all very new, it’s all very rapidly evolving, but the exponent is coming and I think it will have an immense impact on care. 

Chip Kahn: I mean, that sounds transformational, not just a game changer. And maybe we’ll close out with this question. What should a patient know about AI’s role in their care that they certainly don’t know today, I mean, clearly, what you just described is something I think the average patient couldn’t. I can’t visualize even, what should they know? 

Elad Walach: So today, AI has been serving a very backend role, and to some extent, justifiably so. The heroes in this equation are the clinicians, and the AI is just augmenting them. And I still believe that is going to be true. However, I do think as AI Is becoming more proliferated, and now we’re at the pace of over 70 million scans a year, so it’s becoming quite proliferated, it will become the standard. And as you want to know that your health system is using the best tools out there, I think patients should be aware of, at least, what are the systems doing to ensure they’re using the best tools out there. And I do think patients should be aware of that. I’ll give an example. If you’re a patient going into an outpatient imaging center that has clinical or imaging AI, it would scan you for acute findings, even if you’re not being suspected for that. If you are a cancer patient, you have increased risk of pulmonary embolism. Today, because of the backlogs, we have some institutions, not everywhere, but some, where you would be waiting a week to get your diagnosis. Now, imagine you’re one of the two of those 2 to 4% that has an acute finding. And now imagine the world where you’re waiting a week to get this result. Maybe pick up the phone, maybe not, versus getting picked up by the head immediately to go to the ED. That is not science fiction. That is an existing capability today and I think we should all start becoming more and more aware of that. 

Chip Kahn: Elad, thank you so much. This has just been so informative, and, I think I used the word unsettling at the beginning, to characterize it. But I don’t think it’s unsettling. I think it really is exciting. We all should look for a better future, I think, from what you’re doing. 

Elad Walach: Thank you, Chip. 


SERIES

This weekly podcast features insightful conversations between host Chip Kahn and his guests, who discuss the business of health care, connecting the dots between the health care business, policy, and patients.

The podcast’s first series on AI in health care illuminates how AI is changing health care, and features guests who are deploying this technology, managing its consequences, and designing policy around it.

5 Key Facts on Adolescent Mental Health and Substance Use Disorders

Author: Nirmita Panchal
Published: May 11, 2026

In a landmark case earlier this year, K.G.M. v. Meta Platforms, Inc. et al., a jury held major social media platforms responsible for the mental health harm of an adolescent due to their platforms’ addictive design features. This case underscores growing concerns about adolescent mental health and substance use and draws attention to the factors that may be linked to these concerns. In recent years, a large share of adolescents reported heavy screen time and social media use, trauma exposure – including direct and indirect exposure to gun violence – loneliness, and sleep deprivation. These experiences are tied to poor mental health outcomes, including anxiety and depression. Meanwhile, suicide death rates remain high among adolescents and overdose deaths reached record numbers amid the opioid epidemic.

To address growing mental health and substance use concerns several measures were introduced. This includes the 2022 Bipartisan Safer Communities Act (BSCA), which allocated funds to strengthen school-based mental health services, particularly in high-need areas, and utilize Medicaid to expand youth services. In 2021, the U.S. Surgeon General declared a youth mental health crisis and released an advisory outlining a wide range of recommendations to support this population. However, recent policy actions under the second Trump Administration have rolled back some of these efforts. Funding for school-based mental health services via the BSCA was disrupted, prompting questions about how to retain school mental health providers and reach students in need. Other, large-scale changes to insurance are expected to negatively impact coverage and access to care in the coming years. This includes changes to Medicaid, which provides coverage to nearly 40% of children and teens. Additionally, the President’s budget for 2027 proposes cuts to agencies engaged in mental health and substance use disorder services, including ongoing efforts to restructure SAMHSA. SAMHSA also oversees the 988 crisis hotline – which was recently linked to decreased adolescent suicide mortality. Funding for 988 remains flat in 2027, though previous actions have already impacted services, including the removal of an extension line to assist LGBTQ individuals. LGBTQ youth are more likely to experience suicidality compared to their peers.

This brief analyzes the latest data (2024) from the CDC WONDER mortality database and the National Survey on Drug Use and Health to examine the prevalence of adolescent mental health conditions and substance use, related deaths, and access to behavioral health services.

1. Despite some modest improvements, many adolescents experience poor mental health and substance use issues.

In 2024, 15% of adolescents (or 3.8 million) reported a past year major depressive episode (MDE) and 19% (or 4.9 million) reported moderate to severe symptoms of anxiety (Figure 1). The share of adolescents with an MDE decreased from 21% in 2021 during the pandemic – the oldest trend data available – to 15% in 2024. Additionally, a survey of high school students found that the share of students reporting feelings of sadness and hopelessness – which can be indicative of depressive disorder – increased from 30% in 2013 to 42% in 2021, before slightly declining to 40% in 2023. While NSDUH data on anxiety cannot be trended over time, other survey data suggest that diagnosed anxiety has increased over time among adolescents, with prevalence remaining higher than pre-pandemic levels. Anxiety is the most common mental health condition in this population.

Many Adolescents Reported a Major Depressive Episode or Anxiety in the Past Year, While a Smaller Share Reported a Substance Use Disorder (Column Chart)

In 2024, 7.8% (or 2 million) adolescents reported having a substance use disorder in the past year (Figure 1). This is a slight decrease from the 9.2% of adolescents reporting a past year substance use disorder in 2021 – the oldest trend data available. A prior KFF analysis found that the use of drugs and alcohol among high school students slightly declined from 2017 to 2023, despite the surge in drug overdose deaths during pandemic years.

Among adolescents with mental illness and/or substance use disorder, over half have private insurance and 4 in 10 have Medicaid. However, Medicaid is a major payer for mental health and substance use services for youth. Medicaid can facilitate access to care in schools and its Early and Periodic Screening, Diagnostic and Treatment (EPSDT) benefit requires coverage of medically necessary services and maintains low out-of-pocket costs.

2. Substance use is higher among adolescents with mental illness compared to adolescents without mental illness.

In 2024, the use of any illicit drug was significantly higher among adolescents with a past year MDE than their counterparts (33% vs. 12%); and among adolescents with symptoms of anxiety than their counterparts (26% vs. 10%) (Figure 2). The co-occurrence of poor mental health and substance use is common. In a CDC convenience sample survey of teens, ages 13 to 18, who used substances in the last 30 days, 40% reported using substances to cope with anxiety or depression.

Adolescents with Mental Illness are More Likely to Report Using Illicit Drugs Compared to Their Peers (Column Chart)

Marijuana, a drug that is now legally available to adults in many states, has been linked to the onset of psychoticdisordersamong youth, and is used by a large share of adolescents with an MDE (25%) and adolescents with symptoms of moderate to severe anxiety (18%). Adolescents without a past year MDE or anxiety were significantly less likely to report marijuana use (8% and 7%, respectively). Marijuana use among adolescents is associated with earlier onset of psychosis, with more frequent use linked to increased risk of adverse mental health outcomes, including schizophrenia. Further, the presence of the compound THC in marijuana has increased over time and is linked to psychotic symptoms and disorders.

In the past decade, adolescent suicide deaths increased and peaked in 2018 (1,750 deaths), followed by a gradual decline through 2024 (1,478 deaths, Figure 3). There was a 5% decrease in adolescent suicide deaths from 2023 to 2024 (1,555 vs. 1,478). These deaths are more prevalent among adolescent males and are more rapidly increasing for adolescents of color compared to their White peers. Although suicide deaths are higher for adolescent males than their peers, serious thoughts of suicide are higher for adolescent females. Data on the suicide deaths was not available by LGBQ+ identity. However, LGBQ+ youth are more likely to report suicidality than their heterosexual peers. It is possible that some suicides are misclassified as drug overdose deaths since it can be difficult to determine whether drug overdoses are intentional.

More Than 17,000 Adolescents Died by Suicide Over the Past Decade and Many of These Deaths Involved a Firearm (Stacked column chart)

More than 17,000 adolescents died by suicide over the past decade and over 4 in 10 of these deaths involved a firearm (Figure 3). Access to firearms, particularly in the home, is a risk factor for suicide deaths among children and adolescents

4. After surging during the pandemic, drug overdose deaths among adolescents sharply declined in 2024 but remain above pre-pandemic levels.

The sharp decline in drug overdose deaths among adolescents was driven by a reduction in opioid-related deaths: 557 deaths in 2023 vs. 272 in 2024 (Figure 4). Despite decreases in substance use among adolescents, adolescent drug overdose deaths more than doubled during the COVID-19 pandemic. This shift was largely due to drugs laced with opioids, particularly the synthetic opioidfentanyl. Opioid-related deaths among adolescents jumped from 165 in 2019 to 396 in 2020 and continued to increase for several years before declining by approximately 50% between 2023 and 2024 (from 557 to 272, Figure 5). These trends mirror broader trends in opioid-related deaths across the total U.S. population.

Drug Overdose Deaths Among Adolescents Declined in 2024 But Remain Above Pre-Pandemic Levels (Stacked column chart)

Multiple actions may have contributed to the decline in overdose deaths, including school-based initiatives for youth. Policy actions implemented in response to the opioid epidemic include expanded access to treatment, public awareness campaigns, and improved fentanyl detection. Schools have also responded to the overdose crisis in multiple ways. For example, in the 2024-2025 school year, 52% of public schools offered fentanyl education to students and 77% stored naloxone – a nasal spray to reverse opioid overdose. However, challenges remain, such as adolescents obtaining drugs through social media which may be contaminated with fentanyl.

5. Adolescents receive mental health services in a variety of settings, including schools; however, recent policy changes may affect access in the future.

Six out of ten adolescents with a past year MDE reported receiving mental health treatment in 2024 (Figure 5). Many of these adolescents report receiving outpatient care in a variety of settings, including through telehealth treatment (33%). Thirty-one percent of adolescents with a past year MDE report taking prescription medication (31%).

Six Out of Ten Adolescents with a Past Year Major Depressive Episode Received Mental Health Treatment (Bar Chart)

Approximately 3 in 10 youth with a past year MDE obtain care through school health or counseling services; however, changes in funding put access to school-based mental health services at-risk. During the second Trump Administration, ongoing policy changes may impact youth access to mental health services. This includes rolling back funds to broaden access to mental health and trauma services in schools that were originally allocated through the Bipartisan Safer Communities Act.

Among the 2.4 million adolescents in need of substance use care in 2024, only 30% received treatment. This may be linked to limited access to buprenorphine and residential addiction treatment facilities among this population. Additionally, many residential addiction treatment facilities do not have availability for adolescents and are costly. These facilities often do not provide buprenorphine to adolescents with opioid use disorder.

This work was supported in part by the Philos Foundation. KFF maintains full editorial control over all of its policy analysis, polling, and journalism activities.

What to Know About the BALANCE Model for GLP-1s in Medicare and Medicaid and the Medicare GLP-1 Bridge

Published: May 11, 2026

Editorial Note

This brief was updated on May 11, 2026 to reflect changes in the Trump administration’s plans for implementation of the BALANCE Model in Medicare and an extension of the Medicare GLP-1 Bridge program through 2027.

GLP-1s, a class of drugs used to treat type 2 diabetes, obesity, cardiovascular disease, and other conditions, have exploded in popularity in recent years due to their demonstrated effectiveness, but are often not covered by insurance, particularly for the treatment of obesity. According to KFF polling,  about half (56%) of GLP-1 users say these drugs were difficult to afford, including one in four who say they were “very difficult” to afford. The Trump administration is pursuing various approaches to lowering the cost and expanding coverage of these medications. These approaches include striking “most-favored nation” deals with GLP-1 manufacturers Novo Nordisk and Eli Lilly, providing access to discounted prices for GLP-1s through TrumpRx, and proposing to implement a new demonstration program called the BALANCE (Better Approaches to Lifestyle and Nutrition for Comprehensive hEalth) Model to expand Medicare and Medicaid coverage of GLPs for obesity, which is currently subject to statutory limitations (prohibited in Medicare, permissible but not required in Medicaid). In addition, the GLP-1 drug semaglutide (branded as Ozempic, Wegovy, and Rybelsus) was selected for Medicare drug price negotiation in 2025, with a negotiated price set to take effect in 2027.

This brief describes current coverage of GLP-1s in Medicare and Medicaid, the Centers for Medicare & Medicaid Services’ (CMS) efforts to expand access and lower costs for GLP-1s through temporary demonstration programs, and potential impacts on beneficiaries and program budgets. It also describes recent changes to the administration’s plans for temporary coverage of GLP-1s in Medicare, including an indefinite delay in implementation of the BALANCE Model in Part D and an extension of a separate short-term program, called the Medicare GLP-1 Bridge, which was originally scheduled to run from July-December 2026 but will now run through the end of 2027.

Current law prohibits Medicare from covering obesity drugs and gives states flexibility to cover weight loss drugs under Medicaid

Limitations on coverage for obesity drugs in Medicare and Medicaid mean that millions of people who have obesity and might benefit from taking GLP-1s may be unable to access them unless they are able to pay the full cash price out of their own pockets, which would likely be prohibitive for people with Medicaid who must have low incomes to qualify for the program, as well as for many people on Medicare with low and modest incomes. Under the Medicare Part D outpatient prescription drug benefit program, Part D plans are required to cover a minimum of two drugs in each therapeutic category and class, but from the outset, Medicare has been prohibited by law from covering medications when used specifically for weight loss. People on Medicare can get GLP-1s covered by Part D plans only if they are used for a medically accepted FDA-approved indication other than obesity, like type 2 diabetes, cardiovascular disease risk reduction, or sleep apnea.

Under the Medicaid Drug Rebate Program (MDRP), state Medicaid programs must cover nearly all of a participating manufacturer’s FDA-approved drugs for medically accepted indications. However, federal law gives states the option whether to cover drugs used for weight loss. As a result, GLP-1 coverage for medically accepted FDA-approved indications other than obesity is required while access to GLP-1s to treat obesity under Medicaid is currently limited. Only 13 states provided coverage as of January 2026, down from 16 states in 2025, likely reflecting the significant costs of coverage and recent state budget challenges and federal funding cuts.  

Even with these coverage limits on obesity drugs in place, utilization and gross spending on GLP-1 drugs for approved uses in Medicare and Medicaid have increased considerably in recent years. In 2024, there were 8.4 million prescriptions and $8.6 billion in spending on GLP-1s in Medicaid, and 21.8 million claims and $27.5 billion in gross spending (not excluding rebates) on GLP-1s in Medicare (Figure 1).

Medicare and Medicaid Gross Spending on GLP-1s Has Increased Substantially From 2019 to 2024, Though Net Spending Would Be Lower Taking Rebates Into Account (Split Bars)

CMS is proposing temporary expansions of Medicare and Medicaid coverage of GLP-1s for obesity through demonstration programs

CMS initially proposed a two-step approach to expanding coverage of GLP-1s for obesity in Medicare – a temporary payment demonstration for 2026, known as the Medicare GLP-1 Bridge, and a new Center for Medicare and Medicaid Innovation (CMMI) model, known as the BALANCE Model, that was originally scheduled to begin in January 2027 and end in December 2031. CMS has recently announced an indefinite delay in implementation of the BALANCE Model, however, and an extension of the GLP-1 Bridge through the end of 2027 (as described below).

For Medicaid, GLP-1 coverage will be expanded through the BALANCE Model beginning in May 2026 and ending in December 2031. (See Figure 2 for a timeline of key activities associated with the GLP-1 coverage demonstrations.)

Medicare coverage of GLP-1s for obesity begins in July 2026 through the Medicare GLP-1 Bridge and will be extended through the end of 2027

CMS will provide Part D beneficiaries with coverage of select GLP-1s for obesity from July 1, 2026 to December 31, 2027 (originally December 31, 2026) through the Medicare GLP-1 Bridge, which is a short-term demonstration established using Section 402 demonstration authority. The Medicare GLP-1 Bridge is a nationwide demonstration program that will be separate from Part D coverage, meaning Part D sponsors will not have to opt into the demonstration for eligible beneficiaries to gain access, nor will Part D sponsors bear any financial risk for costs incurred by their enrollees associated with the demonstration.

Medicare beneficiaries enrolled in Part D plans who meet the eligibility criteria will have access to GLP-1 medications approved for weight reduction (all formulations of both Foundayo and Wegovy and the KwikPen formulation of Zepbound) at a copayment of $50 per month. For a beneficiary to qualify, their provider must submit a prior authorization request that attests the beneficiary is being prescribed the drug to reduce excess body weight and ongoing maintenance of weight reduction and that they fall into one of three categories related to BMI and other clinical diagnostic criteria (Figure 3).

Clinical Criteria to Participate in GLP-1 Demonstrations (Table)

Manufacturers have agreed to provide eligible GLP-1s under the Bridge program at a net price of $245 per month supply. When participating beneficiaries fill a prescription for one of these drugs, pharmacies will collect the $50 copayment and submit claims to a central processor for reimbursement. Pharmacies will be reimbursed by CMS at no lower than the wholesale acquisition cost (WAC) of a drug, less the beneficiary copay, plus a dispensing fee and, as applicable, sales tax. Manufacturers will then owe money back to CMS for the difference between the WAC and the negotiated $245 net price.

Because this payment demonstration operates outside coverage under a Part D plan, the $50 copayment toward these medications will not count toward a participating beneficiary’s Part D deductible or the $2,100 out-of-pocket maximum in 2026 (increasing to $2,400 in 2027), and copayments will stay consistent at $50 per month, regardless of the Part D benefit phase a beneficiary is in when they fill the prescription. Medicare beneficiaries who are already receiving coverage from their Part D plan for a GLP-1 for a Medicare-covered use, such as type 2 diabetes, cardiovascular disease risk reduction, or sleep apnea, will continue to access the drug through their Part D plan and not through the Medicare GLP-1 Bridge, which will only provide coverage of GLP-1s when used for obesity.

For beneficiaries enrolled in the Low-Income Subsidy (LIS) program, the LIS cost-sharing subsidies will not apply in the Medicare GLP-1 Bridge. This may make it more difficult for low- and modest-income beneficiaries who are otherwise eligible to participate to take advantage of coverage under the short-term demonstration in 2026 and 2027 if the $50 monthly copayment is unaffordable.

At this time, it is uncertain how participating beneficiaries will be able to maintain Medicare coverage of their GLP-1 medication for obesity after the Medicare GLP-1 Bridge ends at the end of 2027, pending further action from CMS to implement the BALANCE Model in Medicare Part D in 2028, as described below.

The BALANCE Model was designed to expand coverage of GLP-1s for obesity in Medicaid and Medicare Part D

According to CMS, the BALANCE Model aims to increase access to GLP-1 medications and healthy lifestyle interventions to help people on Medicare and Medicaid improve their overall health. Under this model, CMS negotiated with manufacturers of GLP-1s to provide lower prices to the state Medicaid programs and Medicare Part D plans that choose to participate in the model. For beneficiaries to be eligible under the BALANCE Model, providers must attest that they meet certain clinical criteria, including qualifying for a GLP-1 for a currently covered use (such as type 2 diabetes, cardiovascular disease risk reduction, or sleep apnea) or for use to treat obesity based on similar clinical criteria as applied in the Medicare GLP-1 Bridge (Figure 3).

In addition to offering lower prices for GLP-1s, this model will provide patients with access to lifestyle support programs at no cost, which are intended to support medication adherence as well as increase GLP-1 effectiveness. (Details about these programs are not yet available.) These lifestyle support programs will be provided by participating manufacturers. As part of their agreements with CMS, the manufacturers must demonstrate how these lifestyle support programs will meet the program requirements, including encouraging healthy eating and increasing physical activity, supporting medication adherence, ensuring engagement with the program on a regular basis, and ensuring availability of this program to all patients receiving these medications, either online or offline for those who have limited digital access. However, prescribing providers will not be required to document that patients are actively participating in these lifestyle support programs as part of attesting to their eligibility for the model based on other clinical criteria.

Participation in the BALANCE Model is voluntary for drug manufacturers, state Medicaid programs, and Medicare Part D plans, but there was insufficient interest among Medicare Part D plans to move forward in 2027

Participation in BALANCE is voluntary for drug manufacturers, state Medicaid agencies, and Medicare Part D plans. The date for manufacturers to notify CMS of their interest in participating was January 8, 2026; for Medicare Part D plans was April 20, 2026; and for state Medicaid programs is July 31, 2026 (Figure 2).

On April 21, 2026, the day after the application deadline for Medicare Part D plan sponsors to participate in the BALANCE Model, CMS announced that it would not be moving forward with implementation of the BALANCE Model in Medicare in 2027 and instead would be extending the Medicare GLP-1 Bridge to run through the end of 2027. CMS said that this was in part to allow for collection of additional data on GLP-1 utilization to share with Part D plan sponsors ahead of potential implementation of BALANCE in Part D. CMS also stated they received feedback from plans that an extension of the BRIDGE would facilitate a smoother transition to potential implementation of BALANCE in Part D. At the same time, it was also reported that major Part D plan sponsors were reluctant or unwilling to participate in the BALANCE Model as it was originally designed. The discussion below of Medicare Part D participation in BALANCE reflects implementation details under CMS’s original design. However, implementation of this model in Medicare faces an uncertain future, since CMS has not yet announced specific plans to restart or restructure the model for future years. The delay in implementation of the BALANCE Model within Medicare Part D will not affect plans for implementation of the model within Medicaid beginning in 2026.

Drug Manufacturers

During the model pre-implementation period in early 2026, CMS negotiated with Novo Nordisk and Eli Lilly to come to agreement on the key parameters of the model, including details about pricing of the model drugs, cost sharing, rebate calculations, access policies (i.e., coverage criteria and prior authorization policies), the length of the agreement, data sharing arrangements, and agreement on lifestyle supports that will be offered. Both manufacturers have agreed to participate in the model, and the following medications will be included: all formulations of Foundayo, Mounjaro, Ozempic, Rybelsus, and Wegovy, and the KwikPen formulation of Zepbound. The manufacturers have agreed to a $245 net price per 30-day supply for all model drugs in 2027 for the Medicare program though the net price for state Medicaid programs is confidential to the public. CMS and manufacturers may renegotiate terms in the future depending on certain circumstances such as changes in the FDA labeling, new clinical evidence, or new products launched.

State Medicaid Programs

Participation. For state Medicaid agencies that opt to participate in the model, implementation will be on a rolling basis from May 1, 2026 through January 1, 2027. To participate in the model, state Medicaid programs must sign a State Agreement with CMS and then adopt supplemental rebate agreements (SRAs) with each participating manufacturer that reflects the standard key terms that the participating manufacturers and CMS have agreed to. While states typically develop their own utilization management strategies, the model key terms establish standardized coverage criteria (Figure 3). States may offer broader coverage but cannot make coverage criteria more restrictive, and the Medicaid key terms must apply equally in both fee-for-service and Medicaid managed care. Although the Medicaid component of the BALANCE Model will launch May 1, the deadline for the state Medicaid agency Request for Applications (RFA) is July 31, 2026.

Cost. The discounted GLP-1 net price for state Medicaid programs will be available through additional supplemental rebates (on top of statutory rebates through the MDRP). To participate in the model, state Medicaid programs must terminate or update any existing SRAs with participating manufacturers. While the original announcement of the MFN deals with Eli Lilly and Novo Nordisk noted Medicaid programs would also have access to the $245 price available to Medicare, the final negotiated discounted price available to state Medicaid programs mentioned in the state Medicaid RFA is not available to the public, only participating states. The model will not affect out-of-pocket costs for Medicaid enrollees, which are limited to nominal amounts under federal law.

Medicare Part D Plans

Participation. Part D plan sponsors needed to apply to participate in the model by April 20, 2026. Participants could include sponsors of Part D stand-alone prescription drug plans (PDPs) and Medicare Advantage prescription drug plans (MA-PDs), including Special Needs Plans (SNPs), and employer plans that offer Part D. Eligible plan types exclude Defined Standard benefit plans, which charge a standard 25% coinsurance amount for all covered drugs and do not vary cost sharing by drug type or formulary tier, although plan sponsors could indicate if they wished to convert a Defined Standard benefit plan to another basic benefit type in order to become eligible. Participation was defined at the plan sponsor level, and plan sponsors could choose which of their specific plan benefit packages would be part of the model. However, CMS required plan sponsor participants to include all of their enhanced alternative plans and 90% of their enrollment in basic plans.

CMS was aiming for a “critical mass” of Part D sponsors to participate and established a threshold participation rate of 80% for 2027. This participation rate was to be calculated as the enrollment in Part D plans applying to participate in the model divided by the total enrollment in all Part D plans, excluding special needs plans and employer plans, even though they are eligible to participate in the model, but including Defined Standard benefit plans, even though they are ineligible to participate. (This calculation could produce a different participation rate than if it was based on enrollment in all participating plans divided by enrollment in all plans eligible to participate. CMS did not explain the rationale behind their approach.) CMS specified that if the 80% threshold was not met, CMS would not move forward with the BALANCE Model in Medicare in 2027, and that appears to be what occurred, as CMS’s April 21, 2026 announcement suggests.

Cost sharing. Part D plans must adopt specific cost-sharing limits under the basic benefit plan structure as agreed upon by CMS and manufacturers. Cost sharing for model drugs will be limited to $245 for a 30-day supply in the deductible phase and a lower amount in the initial coverage phase: $50 per month for enhanced plans and employer group plans and $125 per month for basic plans (though plan sponsors can choose to apply lower cost-sharing amounts to model drugs as long as they do so uniformly across all model drugs). Once beneficiaries reach their out-of-pocket maximum (set at $2,400 in 2027), they will pay $0 for their medications, including for model drugs. Part D plans participating in the model are required to place all model drugs on the same formulary tier, cover all medically accepted indications for model drugs, and apply the same cost sharing to all indications.

Participation incentives. Because the model was voluntary for Part D plan sponsors, CMS designed financial incentives to encourage plan participation. The primary approach to encourage participation for 2027 involved an optional narrowing of the risk corridor thresholds to reduce the range of spending where PDPs bear full risk for actual costs higher than their bids (Figure 4). Model participants would qualify for this additional financial protection if they opted in and had higher-than-average utilization of model drugs relative to other similar plan participants. CMS initially stated that for future model years, the agency was considering additional payment incentives, including higher direct subsidy payments based on an adjustment factor to the beneficiary risk score in participating plan bids.

Figure 4 - The Federal Government Offered Optional Enhanced Protection Against the Risk of Losses to Participating Medicare Part D Plans in the BALANCE GLP-1 Model for 2027

Millions of people with Medicare and Medicaid could benefit from expanded coverage of GLP-1s for obesity

Overall, CMS’s efforts to expand Medicare and Medicaid coverage of GLP-1s for people with Medicare and Medicaid could greatly expand access to these drugs, albeit on a temporary basis, depending on the level of participation by drug manufacturers, state Medicaid agencies and Part D plans. In addition to providing coverage of these drugs for obesity, which would be a new indication in Medicare and in most states under Medicaid, these efforts will also allow beneficiaries in participating states and Part D plans to access GLP-1s for obesity at a lower out-of-pocket cost than if they were to purchase them with cash at the direct-to-consumer prices offered by the manufacturers. Additionally, this model will provide access to a lifestyle support program to promote healthy behaviors and increase the effectiveness of GLP-1s, which could provide improvements in health along with sustained weight reduction.

However, there are factors that could limit the reach of these temporary programs for both the Medicaid and Medicare populations. For example, if the level of participation by state Medicaid agencies is relatively low, the impact of this model for Medicaid recipients may not be very far reaching. In addition, state Medicaid agencies can choose to participate in the model initially but then decide to drop out of the model in later years, which could result in disruptions in coverage. And there is no clear path forward for GLP-1 coverage in Medicare after 2027 if the Medicare GLP-1 Bridge is not extended further and the BALANCE Model is not implemented. This sets up the possibility that Medicare beneficiaries could have coverage of GLP-1s for obesity under the Medicare GLP-1 Bridge in the latter half of 2026 and 2027 but then lose access in 2028 if the BALANCE Model or another coverage pathway are not implemented.

If the BALANCE Model launches in some future year with at least 80% participation, Part D plans could drop out of the model before the final year (originally 2031), which could interrupt treatment, or require frequent plan changes for Medicare Part D enrollees to maintain access. It is also unclear whether Part D plan sponsors would be able continue to cover GLP-1 drugs for the treatment of obesity once the model ended if Medicare’s statutory exclusion on weight loss drugs is not lifted.

Another uncertainty for Part D enrollees in terms of their out-of-pocket costs is the impact of the BALANCE model on Part D premiums. Participating plan sponsors would factor the cost of the model into their bids. That could have the effect of increasing Part D premiums across the board, although Medicare Advantage plans can use rebates to buy down Part D premiums, but this is not an option for stand-alone PDPs. It is possible that CMS could modify the parameters of the Part D PDP premium stabilization demonstration to provide greater premium subsidies for PDP plan sponsors that participate in the BALANCE Model. The ultimate effect on Part D plan bids and premiums is unknown at this time.

The potential federal and state budgetary impacts of expanded coverage of GLP-1s for obesity in Medicare and Medicaid are unknown

CMS documentation does not include potential federal or state budgetary impacts from either the BALANCE Model or the Medicare GLP-1 Bridge. Spending on GLP-1 drugs for currently covered uses under Medicare and Medicaid has increased substantially in a relatively short period of time and could increase further with expanded coverage of GLP-1s for obesity, even at the lower net prices for these medications under both demonstrations. The cost to Medicare of covering obesity drugs under Part D has been estimated at between $25 billion and $35 billion over 10 years, which could have been a driving factor in the reluctance or unwillingness of major Part D plan sponsors to participate in the BALANCE model as it was originally designed. The cost to Medicaid of covering obesity drugs has been estimated at $15 billion, with the federal government paying $11 billion and states paying nearly $4 billion of this estimated total.

The law requires Innovation Center models to either maintain or reduce program expenditures. The BALANCE Model is designed to test whether lower negotiated prices as applied to currently covered indications will lower program spending. It is unclear how the new negotiated prices under the model for state Medicaid programs (which remain confidential) compare to the net prices state Medicaid programs are currently paying for these drugs, but Medicaid already typically pays lower prices than other payers for prescription drugs. In general, without knowing what Medicaid and Medicare generally are currently paying for these drugs on net, it is uncertain whether the lower prices that will be made available to state Medicaid programs and Part D plans under the BALANCE Model for currently covered uses of GLP-1s will generate enough savings to offset the additional costs from expanded use of these medications for obesity. If state Medicaid programs estimate the costs from expanded use will outweigh the savings, initial state Medicaid participation may be limited, especially given recent state budget challenges and federal funding cuts. Based on the reluctance of major Part D plan sponsors to participate in the BALANCE Model in 2027 under the original specifications, it appears that sponsors had insufficient data to estimate their added costs associated with covering GLP-1s for obesity.

Evaluations of the model will also determine whether improvements in health related to the use of these drugs and associated reductions in health care utilization are significant enough to maintain or reduce health care costs in the Medicaid and Medicare programs, after taking into account expanded use and coverage of GLP-1s for the treatment of obesity. Even with lower prices, there is little evidence to date to suggest that the expanded use of GLP-1s will be offset by lower spending on other health care services in the short term, even though the drugs do provide significant health benefits to users.  

The cost of the Medicare GLP-1 Bridge is not discussed in CMS documentation about this demonstration. Federal spending is expected to increase under this demonstration due to paying for GLP-1s for obesity, which is not currently covered under Medicare, but the amount of the increase is unknown since CMS hasn’t disclosed the projected cost. In addition, because the demonstration will operate outside the Part D benefit, the manufacturers won’t be responsible for providing the manufacturer price discount on eligible GLP-1s (10% in the Part D initial benefit phase and 20% in the catastrophic phase.) While many prior section 402 demonstrations have had to conform to budget neutrality rules by the Office of Management and Budget (OMB), some demonstrations have been able to move forward without this requirement. Given the short timeframe of the GLP-1 demonstration, it is unlikely that there will be cost offsets from improved health due to increased GLP-1 use for obesity that can be documented.

This work was supported in part by Arnold Ventures. KFF maintains full editorial control over all of its policy analysis, polling, and journalism activities.

Poll Finding

KFF Health Tracking Poll: Public Views on Foreign Aid and Global Health Spending

Published: May 11, 2026

Findings

U.S. global health efforts have undergone substantial changes since the start of the second Trump administration, including the freezing of funding in early 2025, the cancellation of numerous projects, reduction in funding, and the dissolution of the United States Agency for International Development (USAID)—once the world’s largest foreign aid agency. Following these actions, the State Department released the America First Global Health Strategy, claiming that existing global health programs were “inefficient and wasteful” and that countries were too dependent on the U.S. for support, proposing a new approach to “make America safer, stronger, and more prosperous.” Among other things, the Strategy is anchored in new, time-limited agreements with countries with plans to reduce funding even more, by billions of dollars in the next few years.

When asked about their awareness of the Trump administration’s reductions to U.S. spending on foreign aid, including funding to improve health for people in developing countries, nearly six in ten (58%) adults correctly identify that the administration has made cuts, including over four in ten (44%) who say there have been “major” cuts and one in seven (14%) who say there have been “minor” cuts. An additional third (36%) of adults say they don’t know enough to say.

While majorities across partisans know there have been reductions to U.S. spending on foreign aid, Democrats are 24 percentage points more likely than Republicans to identify them as “major” cuts (59% vs. 35%, respectively), while a larger share of Republicans than Democrats say there have been “minor” cuts (23% vs. 6%). Among independents, 44% say the administration has made major cuts to spending on foreign aid and 13% say they have made minor cuts.

Stacked bar chart showing share of adults who say the Trump administration has made major cuts, minor cuts, no cuts, or say they don't know enough to say to U.S. spending on foreign aid. Results shown among total and by party identification.

When it comes to assessing the impact of the changes the Trump administration has made to foreign aid and global health, majorities of the public say these changes have had a negative impact on “how people around the world view the U.S.” (64%) and on “the health of people in developing countries” (59%). Additionally, nearly half (46%) say “the ability to keep infectious disease from spreading to the U.S.” has been negatively impacted by the changes made by the Trump administration.

The public is more divided about the impact the Trump administration’s changes to foreign aid and global health have had on the U.S. budget deficit. Similar shares—about one-third—say the changes have had a positive impact (31%), a negative impact (33%), or no impact (35%) on the budget deficit. KFF polling from 2025 found a majority of the public overestimated the share of the federal budget allocated for foreign aid; on average, U.S. adults said foreign aid spending makes up one-quarter (26%) of the federal budget. In reality, about one percent of the federal budget has historically gone to foreign aid, with an even smaller fraction going toward global health efforts.

Stacked bar chart showing share of adults who say the Trump administration's changes to the U.S. approach to foreign aid and global health have had a positive impact, negative impact, or no impact to various topics. Results shown among total.

Unsurprisingly, public opinion on the impact of the Trump administration’s changes to U.S. foreign aid and global health efforts is highly partisan, but patterns differ somewhat depending on the impact being measured. Democrats and independents are overwhelmingly more likely to say these changes have had a negative rather than a positive impact on how people around the world view the U.S. (86% vs. 6% and 68% vs. 9%, respectively) and on the health of people in developing countries (86% vs. 4% and 61% vs. 10%). Both groups are also more likely to see a negative rather than a positive impact on the ability to keep infectious disease from spreading to the U.S. and on the U.S. budget deficit, though fewer than half of independents say there has been a negative impact on each of these areas. 

In contrast, Republicans are more likely to say some of these areas have been positively rather than negatively impacted, such as the U.S. budget deficit (66% positive, 16% negative) and preventing the spread of infectious diseases to the U.S. (39% positive, 15% negative). Nearly half (46%) of Republicans say there has been no impact at all on preventing the spread of disease, and about one in five (18%) say this about the U.S. budget deficit.

Republicans are split when it comes to the impact of the administration’s changes to foreign aid and global health on the health of people in developing countries, with about three in ten saying these changes have had a positive impact (28%) and another three in ten saying the impact was negative (30%), while 42% say there has been “no impact.” And, when asked about international perceptions of the U.S., more Republicans say the administration’s changes to foreign aid and global health have had a negative impact (40%) than a positive one (28%), and 32% say it has had no impact.

Split bar chart showing share of adults who say the Trump administration's changes to the U.S. approach to foreign aid and global health have had a positive or negative impact on various topics. Results shown by party identification.

As the Trump administration continues its efforts to significantly reduce U.S. participation in global health efforts, nearly half (45%) of the public supports the U.S. playing a “leading” or a “major, but not a leading” role in improving health for people in developing countries. Nearly six in ten (58%) Democrats say the U.S. should play at least a major role compared to fewer independents (42%) and Republicans (35%) who say the same. Still, the share of Republicans who say the U.S.’s role should be major (35%) is larger than the share who say the U.S. should take “no role at all” (24%) in improving health for people in developing countries. About one in five (18%) independents also say the U.S. should play no role at all in improving health for people in developing countries, and even fewer Democrats say this (8%).

Stacked bar chart showing share of adults who think the U.S. should take the leading role in improving health for people in developing countries, take a major role but not the leading role, take a minor role, or take no role at all. Results shown among total and by party identification.

The share who say the U.S. should take a leading or major role in improving health for people in developing countries has declined somewhat since last year (45% now, down from 50% in February 2025), reaching a new low since KFF began asking this question in 2016. The most recent decline includes an 11-percentage point decrease in the share of Democrats who say the U.S. should play at least a major role in this area. The share of Republicans who say the U.S. should have at least a major role in improving global health declined during President Trump’s first term, though it has remained fairly steady since 2019.

Split bar chart showing the percent of partisans over time, who say the U.S. should have a major or leading role in improving health for people in developing countries.

Methodology

This KFF Health Tracking Poll was designed and analyzed by public opinion researchers at KFF. The survey was conducted April 14 – April 19, 2026, online and by telephone among a nationally representative sample of 1,343 U.S. adults in English (n=1,251) and in Spanish (n=92). The sample includes 1,023 adults (n=81 in Spanish) reached through the SSRS Opinion Panel either online (n=999) or over the phone (n=24). The SSRS Opinion Panel is a nationally representative probability-based panel where panel members are recruited randomly in one of two ways: (a) Through invitations mailed to respondents randomly sampled from an Address-Based Sample (ABS) provided by Marketing Systems Groups (MSG) through the U.S. Postal Service’s Computerized Delivery Sequence (CDS); (b) from a dual-frame random digit dial (RDD) sample provided by MSG. For the online panel component, invitations were sent to panel members by email followed by up to three reminder emails.

Another 320 (n=11 in Spanish) adults were reached through random digit dial telephone sample of prepaid cell phone numbers obtained through MSG. Phone numbers used for the prepaid cell phone component were randomly generated from a cell phone sampling frame with disproportionate stratification aimed at reaching Hispanic and non-Hispanic Black respondents. Stratification was based on incidence of the race/ethnicity groups within each frame. Among this prepaid cell phone component, 140 were interviewed by phone and 180 were invited to the web survey via short message service (SMS).

Respondents in the prepaid cell phone sample who were interviewed by phone received a $15 incentive via a check received by mail or an electronic gift card incentive. Respondents in the prepaid cell phone sample reached via SMS received a $10 electronic gift card incentive. SSRS Opinion Panel respondents received a $5 electronic gift card incentive (some harder-to-reach groups received a $10 electronic gift card). In order to ensure data quality, cases were removed if they failed two or more quality checks: (1) attention check questions in the online version of the questionnaire, (2) had over 30% item non-response, or (3) had a length less than one quarter of the mean length by mode. Based on this criterion, no cases were removed.

The combined cell phone and panel samples were weighted to match the sample’s demographics to the national U.S. adult population using data from the Census Bureau’s 2024 Current Population Survey (CPS), September 2023 Volunteering and Civic Life Supplement data from the CPS, and the 2025 KFF Benchmarking Survey with ABS and prepaid cell phone samples. The demographic variables included in weighting for the general population sample are gender, age, education, race/ethnicity, region, civic engagement, frequency of internet use and political party identification. The weights account for differences in the probability of selection for each sample type (prepaid cell phone and panel). This includes adjustment for the sample design and geographic stratification of the cell phone sample, within household probability of selection, and the design of the panel-recruitment procedure. Initial coding for open-ended questions was done using BT Insights AI Platform and then reviewed, edited, and finalized by KFF researchers.

The margin of sampling error including the design effect for the full sample is plus or minus 3 percentage points. Numbers of respondents and margins of sampling error for key subgroups are shown in the table below. For results based on other subgroups, the margin of sampling error may be higher. Sample sizes and margins of sampling error for other subgroups are available on request. Sampling error is only one of many potential sources of error and there may be other unmeasured error in this or any other public opinion poll. KFF public opinion and survey research is a charter member of the Transparency Initiative of the American Association for Public Opinion Research.

GroupN (unweighted)M.O.S.E.
Total1,343± 3 percentage points
   
Party ID  
Democrats420± 6 percentage points
Independents450± 6 percentage points
Republicans372± 6 percentage points

 

A Look at the GENEROUS Model and Factors That Could Impact Medicaid Drug Costs

Published: May 8, 2026

While spending on prescription drugs accounts for a relatively small share of overall Medicaid spending, Medicaid drug spending has grown in recent years. As a result, both states and the federal government continue to prioritize the management of rising prescription drug costs. There have been several recent Trump administration prescription drug initiatives, including negotiating “most-favored-nation” (MFN) drug pricing deals. These MFN agreements are based on the premise that the U.S. shouldn’t pay higher prices for prescription drugs than other comparable nations. The deals include agreements by drug manufacturers to provide MFN pricing in Medicaid and other commitments in return for a 3-year reprieve from tariffs, though the specific details of these agreements remain confidential. Though prices vary across countries, studies have shown that drug prices in the U.S. are about three times higher than in other countries.

To make MFN drug prices available to state Medicaid programs, the Centers for Medicare & Medicaid Services (CMS) developed the GENEROUS (GENErating cost Reductions fOr U.S. Medicaid) Model, a drug payment model through which CMS will negotiate supplemental drug rebates based on prices paid in other countries. Given significantly lower drug prices internationally, this approach could result in substantial Medicaid savings, with a recently released White House report estimating that a voluntary MFN framework in Medicaid would save $64.3 billion over a 10-year period. While initial savings would be large and diminish over time – in part due to the fact that prices in other countries might increase as a result – savings would average $6.43 billion a year, or approximately 14% of annual Medicaid prescription drug spending.

However, it is unclear what assumptions were made to develop the administration’s estimates, and there remain several uncertain factors that make it difficult to assess the overall impact the new model will have on Medicaid drug costs. This issue brief provides background on the GENEROUS model, examines the factors that will contribute to the model’s overall impact on Medicaid drug costs, and illustrates how savings will depend on model details that are confidential or uncertain at this time. Key takeaways include:

  • The impact of the GENEROUS model on Medicaid prescription drug spending remains unclear due to several uncertain factors related to drug pricing and model participation.
  • Existing Medicaid rebates already reduce overall Medicaid prescription drug spending substantially, likely limiting the impact of the GENEROUS model’s MFN supplemental rebate approach.
  • GENEROUS model savings will depend on which manufacturers and states participate as well as what drugs are included in the model due to variation in drug rebates (and net prices) as well as spending and utilization trends.

What Is the GENEROUS Model?

The CMS Innovation Center launched the GENEROUS model in January 2026 with the goal of lowering Medicaid drug spending by offering prices based on what other countries pay. The model is voluntary for manufacturers, though it is expected that the seventeen pharmaceutical companies (e.g. Pfizer, AstraZeneca, etc.) that have signed MFN agreements will participate. Manufacturers now have until June 11, 2026 to apply to participate in the GENEROUS model (the deadline has been extended twice from March 31, 2026 to April 30, 2026 and again to June 11, 2026). The model is also voluntary for states, with states having until July 31, 2026 to submit their application and until August 31, 2026 to execute a state participation agreement with CMS. States may be able to join the model after August 31, 2026 at CMS discretion. The model will run for five years through 2030, though manufacturers and states may voluntarily terminate their participation and key terms between CMS and manufacturers may be renegotiated.

Through the GENEROUS model, CMS will negotiate supplemental drug rebates based on prices paid in other countries (or the “MFN price”). For model drugs (single source or innovator multiple source drugs, also known as brand drugs), CMS will calculate the MFN price based on international pricing data provided by manufacturers across eight other countries (the United Kingdom, France, Germany, Italy, Canada, Japan, Denmark, and Switzerland). The MFN price is the second lowest reported net price after any rebates or discounts and is adjusted by gross domestic product per capita using a purchasing power parity method. CMS will then calculate the supplemental rebate for each model drug that results in a Medicaid net price equivalent to the MFN price (more specifics on the MFN price and Medicaid guaranteed net unit price calculations are available in both the state request for applications (RFA) and the manufacturer RFA). States can select which model drugs they’d like to receive MFN pricing for and must enter into new supplemental rebate agreements (SRAs) that reflect the model’s key terms (states cannot receive additional supplemental rebates outside of the model for drugs they have opted into).

CMS and participating manufacturers will also negotiate uniform coverage criteria, which includes utilization controls such as prior authorization or step therapy. These terms will be based on existing criteria states have negotiated, and states will have to adopt the uniform criteria to access the supplemental rebate for a given drug. States currently negotiate their own SRAs with manufacturers and use an array of payment strategies and utilization controls to manage prescription drug expenditures. States often use placement on a preferred drug list (PDL) and prior authorization as leverage to negotiate supplemental rebates with manufacturers, though the specific strategies vary by state. The negotiation of standardized coverage criteria could help reduce the administrative burden for states and manufacturers of negotiating individual SRAs tied to specific clinical criteria. However, the negotiated criteria may be more broad or more restrictive than the criteria states have already developed, which could have implications for state participation and model savings.

CMS will also conduct GENEROUS model monitoring and evaluation. The law requires Innovation Center models to either maintain or reduce program expenditures, and the model will test whether the MFN supplemental rebate approach can reduce Medicaid drug costs. To the extent negotiated clinical criteria broadens, the model may also increase enrollee access. Throughout the model, CMS will track data and assess the impact of the model on health care spending and access to care as well as audit the manufacturer reported international drug pricing data. GENEROUS works within the existing Medicaid Drug Rebate Program (MDRP) framework and builds on other CMS supplemental rebate models including the Cell and Gene Therapy Access Model and the BALANCE model. Notably, while these models aim to address high drug costs for the Medicaid program, they do not affect out-of-pocket costs for Medicaid enrollees, which are limited to nominal amounts under federal law.

What Are the Potential Impacts on Medicaid Prescription Drug Spending?

The impact of the GENEROUS model on Medicaid prescription drug spending remains unclear due to several uncertain factors related to drug pricing and model participation. Several factors will affect model cost savings (Figure 1), many of which are confidential or not yet available, including:

  • While data on gross drug prices is available, data on the size of rebates for specific drugs is proprietary in both Medicaid and internationally, making it difficult to compare net Medicaid prices to net international prices.
  • At this time, it also remains uncertain which manufacturers and states will participate in the model and how long participation will last.
  • Further, while model documentation makes it clear that states may select model drugs, it remains uncertain whether all drugs in a participating manufacturer’s portfolio will be subject to the model. The RFAs note that “model drugs are limited to all the single source drugs or innovator multiple source drugs of a participating manufacturer”, indicating manufacturers must include all of their covered outpatient drugs. However, the RFAs also report that the listed terms may differ from the final terms, and a CMS presentation to states noted “manufacturers will opt into the model for certain branded Medicaid covered outpatient drugs”, leaving it uncertain whether all of a manufacturer’s drugs will be subject to MFN pricing or if exemptions will be available. Recent letters from the Senate Finance Committee sent to drug manufacturers push for more model details, including which drugs will be included in the model.
  • Lastly, details on the uniform coverage criteria for model drugs have not been released, making it difficult to assess the impact the terms may have on drug spending or access.
Several Factors Related to Drug Pricing and Model Participation Will Affect GENEROUS Model Impact on Medicaid Drug Costs (Table)

Existing Medicaid rebates already reduce overall Medicaid drug spending substantially, likely limiting the impact of the GENEROUS model’s MFN supplemental rebate approach. Medicaid programs already pay lower prices, net of rebates, than other payers due to the MDRP, which requires manufacturers to rebate a portion of drug payments to states. Medicaid rebates overall reduced gross Medicaid spending on prescription drugs by 53% on average from FY 2019 to FY 2024 (Figure 2). Rebates for brand drugs are typically even higher, with a Medicaid and CHIP Payment and Access Commission (MACPAC) analysis of FY 2020 data finding a 62% rebate overall for brand drugs.

At the same time, a recent study showed that U.S. drug prices overall were 2.78 times international drug prices (across 33 OECD countries); this differential is similar to those found in other research. Assuming U.S. drug prices are 2.78 times more than international prices, this would mean international prices are about one-third (36%) of U.S. drug prices. Based on this calculation, an MFN approach that reduced prices to international levels would provide a 64% rebate off existing U.S drug prices (Figure 2). The same study found U.S. brand drug prices were 4.22 times international drug prices, which is effectively a 76% rebate. While this is an illustrative example based on non-Medicaid specific drug prices in aggregate, it indicates that an MFN approach could provide substantial discounts given the large difference between drug prices in the U.S. and abroad. However, Medicaid already receives sizeable rebates, signaling there may be limits to this approach when applied to the Medicaid program.

Existing Medicaid Rebates Already Reduce Overall Medicaid Spending Substantially (Bar Chart)

However, rebates and net prices vary substantially by drug, meaning GENEROUS model savings will vary for each model drug (Figure 3). While data on the total rebate for a specific drug is confidential, rebates vary substantially by drug. The minimum federal statutory rebate for a brand drug is 23.1%, but FY 2020 data shows statutory rebates for brand drugs subject to Medicaid’s best price provision and inflationary rebate component are generally much higher, reaching 77% overall. Further, as of January 1, 2024, there is no longer a cap on the total rebate amount if a drug’s price increases quickly over time, meaning overall rebates may now be even higher. In addition to these federal statutory rebates, states have been increasingly negotiating supplemental rebates with manufacturers, with supplemental rebates across states reducing gross Medicaid spending by 7% in FY 2024 (resulting in a higher total rebate estimate of 84% if added to the 77% in statutory rebates for some brand drugs). International countries in the model may also negotiate rebates or discounts. Available studies indicate rebates vary by type of drug and country, ranging anywhere from 0% of gross spending in Japan to about 25% or more in several model countries including Canada, Germany, France, Switzerland.

To illustrate, this analysis examines three example drugs (Drugs A-C), all with a gross Medicaid price of $422 compared with $100 internationally (based on the above study) but with different sized rebates (Figure 3). Some drugs, typically newer drugs with few (or no) competitors in their therapeutic class, may have smaller rebates and large gaps between the net Medicaid and net international price (like Drug A). For example, Biktarvy, the first single tablet combination HIV treatment with the ingredient bictegravir, was FDA approved in 2018 and had an estimated Medicaid rebate of 24% in 2019. It can be difficult for states to secure supplemental rebate agreements for these types of drugs, meaning their inclusion in the model would likely result in savings for states but at a cost to manufacturers.

However, there are also drugs, typically those with more competitors or that have been on the market longer, for which states are already receiving sizeable Medicaid rebates (like Drug B or C). For example, Eliquis, an anticoagulant (or blood thinner), was FDA approved in 2012 and had an estimated Medicaid rebate of 100% in 2019, meaning Medicaid programs are likely paying little to nothing for the drug. In cases where Medicaid rebates are already high, there may not be substantial savings for states through the GENEROUS model, but the impact of the model on manufacturer profits would be mitigated.

Rebates and Net Prices Vary by Drug, Meaning GENEROUS Model Savings Would Vary For Each Model Drug (Bar Chart)

Medicaid drug rebates also vary by state, resulting in differing model impacts across states. While federal statutory rebates are required by law and calculated by CMS, the number and magnitude of SRAs vary across states. Medicaid rebates reduced gross Medicaid spending on prescription drugs by 53% on average nationally from FY 2019 to FY 2024, though the percentage varies across states. MACPAC data from FY 2024 shows that drug rebates (including both statutory and state supplemental rebates) reduced gross Medicaid spending on drugs by less than 40% in four states (Kentucky, Oregon, South Dakota, and Virginia) to over 90% in another four states (Delaware, Mississippi, Nevada, and Wyoming). This variation likely reflects differences in the amount and types of drugs paid for as well as differences in SRAs across states. States will likely complete their own internal analyses to assess model impact, including comparing their existing supplemental rebate agreements to what is available under the model and analyzing the impacts of standardized criteria before entering into new model SRAs.

Medicaid drug utilization and spending patterns will also have implications for the Medicaid savings possible under the model. KFF analysis of Medicaid State Drug Utilization Data shows that a relatively small number of drugs account for a large share of Medicaid drug spending (Figure 4). The top five drugs (Biktarvy, Humira, Stelara, Dupixent, and Ozempic) account for 10% of all Medicaid drug spending, and the top 50 drugs account for over one-third of all Medicaid drug spending. Substantial MFN supplemental rebates on the costliest and most utilized drugs for Medicaid programs could result in significant savings (if the drugs are not already subject to sizeable Medicaid rebates), whereas substantial rebates on drugs that are not frequently utilized or only account for a small share of spending would have less of an impact. In addition, the overall number of participating manufacturers and model drugs as well as the number of participating states will affect the magnitude of savings.

A Small Number of Drugs Account for A Large Share of Medicaid Drug Spending (Small multiple pie chart)

Overall, GENEROUS model savings will depend on who participates, both manufacturers and states, and what drugs are included. While these factors remain uncertain at this time, the implications for Medicaid drug costs may become clearer as additional model details become available or if manufacturers respond to recent Senate letters requesting details of the Trump administration’s deals. Looking ahead, substantial state participation will likely indicate the potential for considerable model savings as states may only opt in if they expect the model supplemental rebates to be larger than their current supplemental rebates. States are also currently facing broader state budget pressures and federal Medicaid cuts, which may make some states eager to adopt pharmacy cost containment strategies. Further, manufacturer model participation may increase following the recent announcement of new pharmaceutical tariffs for companies who have not yet entered into MFN deals, though it is not clear what will happen to manufacturer participation when the tariff reprieves end. Once implemented, the GENEROUS model could also have implications for Medicaid prices on drugs from non-participating manufacturers or for the broader drug market, including changes in international prices or manufacturer participation in international markets.

Louisiana v. FDA: Access to Mifepristone Back at the Supreme Court

Published: May 6, 2026

On May 14, 2026, the U.S. Supreme Court blocked a lower court order in Louisiana v. FDA that would have restricted the nationwide distribution of mifepristone. The Court’s action leaves current FDA rules in place, allowing the drug to be prescribed via telehealth and dispensed by mail or at retail pharmacies while the litigation continues, pending a final decision by the Supreme Court.

Medication abortion—most commonly a two-drug regimen of mifepristone and misoprostol—has become a central legal battleground in the years since Dobbs v. Jackson Women’s Health Organization. Over 25 years ago, the U.S. Food and Drug Administration (FDA) approved mifepristone, along with a requirement that the drug be dispensed in person by a physician. In 2023, after reviewing research that continued to demonstrate its safety even when dispensed through telehealth, the FDA eliminated the requirement that the drug be dispensed in person, enabling the drug to be mailed or dispensed by retail pharmacies. This change has allowed clinicians to dispense the drug via mail and enabled tens of thousands of patients to access medication abortion in states where the provision of abortion is outlawed.

Multiple lawsuits filed after 2022 have focused specifically on the FDA’s role in regulating mifepristone: whether the agency can set conditions of use through its Risk Evaluation and Mitigation Strategy (REMS), whether courts can override the agency’s scientific and administrative judgments, and how state abortion bans interact with federal drug regulation. In 2024, the Supreme Court ruled in Alliance for Hippocratic Medicine v. FDA, that a group of doctors, associations and organizations that oppose abortion lacked legal standing to challenge the FDA’s approval of mifepristone, but did not reach the merits of their claims.

Three state-led lawsuits are now claiming that FDA policy enabling remote prescribing and mailing of mifepristone harms states by undermining their abortion restrictions and generating downstream costs. Several provider- and organization-led cases argue the opposite: that FDA restrictions – which allow mifepristone to be mailed, but only through certified prescribers and pharmacies — remain unlawfully burdensome given the drug’s safety record. This brief reviews the case now before the Supreme Court, Louisiana v. FDA, provides an overview of the other pending litigation involving mifepristone, and the mounting tension between states seeking to protect abortion and states banning the provision of abortion.

Overview of the Case

In October 2025, Louisiana filed a lawsuit against the FDA claiming the agency violated the Administrative Procedure Act (APA) when it approved the 2023 REMS for mifepristone. Louisiana also claims that the 2023 REMS violates an 1873 anti-obscenity law, the Comstock Act, which prohibits the mailing of any medication used for abortion. The 2023 REMS no longer required that the drug be dispensed in person to patients and enabled the medication to be mailed or dispensed at retail pharmacies like most prescription drugs. The state of Louisiana alleges that this revised dispensing requirement has harmed the state and interferes with their ability to regulate abortion in their own state (Louisiana bans the provision of abortions). The Trump Administration defended the FDA and said that an internal review was already underway to examine the 2023 approval decision and the medication’s safety record in light of the updated dispensing policies. In April 2026, the U.S. District Court for the Western District of Louisiana paused the litigation for six months to give the FDA time to continue its review of the drug’s safety. Louisiana appealed this decision to the 5th Circuit Court of Appeals.

On May 1, 2026, a three-judge panel of the U.S. Court of Appeals for the Fifth Circuit granted Louisiana’s request to roll back the FDA rules that enabled remote prescribing, mailing and retail pharmacy dispensing while the appeal proceeds. This decision, which required mifepristone to be dispensed only in person, took effect immediately with implications for abortion access nationwide, not just in states where abortion is banned. That evening Danco Laboratories, one of the drug manufacturers and an intervenor-defendant, filed an emergency motion asking the Fifth Circuit to pause its decision for one week to allow them time to appeal to the U.S. Supreme Court. The following day, after the Fifth Circuit did not respond to Danco’s motion, Danco and GenBioPro (another mifepristone manufacturer) filed emergency appeals to the Supreme Court. Justice Alito granted a one-week administrative stay of the Fifth Circuit’s decision.

On appeal the Supreme Court will first consider whether the Plaintiffs have legal standing to bring this case; without legal standing, the case does not proceed. Both the district court and the Fifth Circuit Court of Appeals found Louisiana has legal standing because it has shown injury caused by the FDA’s 2023 REMS that can be alleviated by a court decision. However, in a similar case, Alliance for Hippocratic Medicine v. FDA, the Supreme Court ruled that a group of doctors and health providers did not have standing for multiple reasons including that they could not show a concrete injury resulting from the updated FDA mifepristone dispensing requirements.

In this lawsuit Louisiana asserts that the policy of allowing mifepristone to be mailed has harmed the state’s ability to enforce its abortion ban and has caused the state to spend state Medicaid funds on patients who took mifepristone received by mail and then needed emergency care for complications. Specifically, “Louisiana identifies $92,000 it paid in Medicaid costs from two women who needed emergency care in 2025 from complications caused by out-of-state mifepristone.” 

The FDA, and the drug manufacturers (GenBioPro and Danco), claim that Louisiana has failed to show legal standing. The FDA regulates drugs and has no oversight over states or providers. They assert that the FDA’s 2023 REMS does not implicate the State’s sovereign “power to create and enforce a legal code.” The Defendants further argue that Louisiana’s alleged Medicaid-based economic harm is too “attenuated” to establish standing, suggesting Louisiana cannot show that the FDA’s policy of allowing mifepristone to be mailed led to their alleged economic harm. Patients could obtain mifepristone out of state, bring it back to Louisiana, and suffer the same complications, they argue.

The Defendants (the FDA, Danco and GenBioPro) assert that if Louisiana is granted standing in this case, a state could challenge any federal policy alleged to have caused a visit to a doctor or a hospital for which the state pays the bills. The Defendants cite to the examples the Supreme Court provided in the decision denying the Alliance for Hippocratic Medicine standing: “EPA roll[ing] back emissions standards for power plants,” “[a] federal agency increas[ing] a speed limit from 65 to 80 miles per hour,” and the federal government “repeal[ing] certain restrictions on guns.”

KFF infographic explaining who regulates mifepristone, showing four entities and their roles: the U.S. Food and Drug Administration approves and regulates medications for safety and effectiveness; state legislatures pass laws that can restrict or protect access; courts rule on legal cases affecting regulation; and Congress can pass federal legislation influencing regulation.

The FDA’s Tightrope Walk—Defending Agency Authority While Not Defending the 2023 REMS

The FDA’s posture in this case and the related litigation has been complicated by the transition from the Biden administration, which embraced reproductive rights, to the Trump administration, which supports abortion restrictions. The agency is defending the lawsuits. But, after pressure from anti-abortion organizations and state attorneys general, in September 2025, the FDA announced it was conducting a comprehensive review of mifepristone, including the 2023 REMS. When announcing the review, the FDA wrote, “HHS’s decision to do so is informed by the lack of adequate consideration underlying the prior REMS approvals, and by recent studies raising concerns about the safety of mifepristone as currently administered.” The Fifth Circuit relied on that statement in its ruling, finding that the FDA conceded in the September 2025 letter that it did not comply with the Administrative Procedures Act when removing the in-person dispensing requirement.

The Trump administrations FDA position contrasts with the administrative record underlying the FDA’s 2023 REMS decision, which shows the FDA’s removal of the in-person dispensing requirements was grounded in more than two decades of experience with mifepristone. The 2023 analysis included: its 2021 review of published literature; safety information submitted during the COVID-19 pandemic; more than five years of adverse data; a separate one-year assessment report for the REMS; and information provided by advocacy groups, individuals and manufacturers. The FDA found that all this information supported the safety of the REMS modification in 2023. The FDA further found there were “no new safety concerns” related to the removal of the in-person dispensing requirement. It therefore concluded that, “[r]emoving the in-person dispensing requirement will render the REMS less burdensome to healthcare providers and patients and provided all other requirements of the REMS are met, including the additional requirement for pharmacy certification, the REMS will continue to ensure that the benefits of mifepristone for medical abortion outweigh the risks.”

Enforcement of a Court Decision Directed at the FDA

A court order to rescind the 2023 REMs would again require in person dispensing, resulting in regulatory pressure on Mifepristone manufacturers (Danco, GenBioPro and Evita Solutions) to ensure that providers certified to dispense mifepristone comply with a court order, and only dispense the medication in person. Furthermore, it is unprecedented for a federal court to compel the FDA to modify the REMS for an approved medication and could potentially undermine the agency’s authority to determine and regulate drug safety.

Even if the Court suspends the FDA’s policy of dispensing mifepristone by mail, that will not end the use of telehealth in the provision of medication abortions. Some clinics and providers will respond by switching to a misoprostol-only regimen. While this single drug regimen is less effective (approximately 80-100% depending on the regimen and pregnancy duration) than using the highly effective mifepristone and misoprostol regimen (between 91.9 to 99.7% depending upon the gestational duration and route or interval of misoprostol administration) it is still a demonstrated protocol that is used in many countries. While misoprostol alone is effective, it can also cause abortion patients to experience more side effects, including greater pain, bleeding, and gastrointestinal effects than the regimen with mifepristone and misoprostol combined.

Conflict Between States

Interstate Conflict and the Rise of Shield Laws

Many states that are protective of abortion rights have implemented so-called “shield laws.” These laws are designed to protect telehealth providers prescribing and mailing mifepristone in their state from criminalization across state lines. As GenBioPro and Danco highlight in their appeals to the Supreme Court: “Louisiana’s filings below made clear—the alleged frustration of Louisiana’s laws occurs because other “states have enacted ‘shield laws’ to protect medical practitioners in their states from extradition for prescribing” mifepristone. This difference in state policies is yet again a natural result of this Court “return[ing]” abortion policy to the states,” in the Dobbs ruling.

Under shield laws, in June 2025 approximately 55% of telehealth medication abortions were provided to people living in states with abortion bans or telemedicine bans according to the #WeCount project of the Society of Family Planning. That month, 45% of telehealth medication abortions were mailed to patients in states without restrictions reflecting the uneven availability of abortion even in states where abortion is not restricted. Texas and Louisiana have attempted to prosecute or fine telehealth providers in California and New York but have been blocked by shield laws.

New State Laws Regulating Mifepristone

Beyond regulating abortion generally, states have implemented new laws specifically targeting medication abortion. These laws often prohibit the prescription, dispensing, or mailing of abortion-inducing drugs within state borders, and in some instances impose criminal penalties on providers. Such measures operate in direct tension with the FDA’s policy set forth in the REMS for mifepristone. For example, Mississippi recently passed a new law (effective July 1, 2026) that makes it unlawful to manufacture, distribute, dispense or prescribe abortion medication. Anyone who violates the law may face civil liability and up to 10 years in prison. Louisiana has enacted a law that classifies mifepristone and misoprostol as controlled substances, which limits the appropriate storage and dispensing; however, this law is currently being challenged in state court. Texas also passed a new law that allows private citizens to sue individuals or entities that provide, mail, or transport abortion medication to or from Texas.

Proposed Federal Bills and Investigations

Dissatisfied with the pace of the FDA review, Senator Josh Hawley, an anti-abortion leader, has introduced a bill that would rescind the FDA’s approval of mifepristone. He has also launched an investigation into mifepristone manufacturers Danco Laboratories and GenBioPro seeking information about adverse events associated with the drug, claiming that the drug is risky based on the results of the same study cited by HHS officials. In April 2026, Indiana Senator Jim Banks sent a letter to the Federal Trade Commission Chairman urging the Commission to investigate abortion drug manufacturers for allegedly engaging in deceptive trade practices and promoting misleading safety claims.

Other Cases Involving the FDA and Medication Abortion

In recent years, courts have issued conflicting rulings on the FDA’s 2023 decision to eliminate the in-person dispensing requirement. (Table 1) In July 2025, a federal court in Washington upheld the REMS revisions, while in October 2025, a federal court in Hawaii (Purcell v. Kennedy) ruled that the FDA violated the Administrative Procedure Act, “by failing to provide a reasoned explanation for its restrictive treatment of the drug ”when it maintained restrictions on access to mifepristone in 2023. The court has ordered the FDA to review evidence it allegedly overlooked including, “the wealth of peer-reviewed evidence proving mifepristone’s safety, including when delivered by telemedicine as well as how FDA’s restrictions burden patient access.”

Additional lawsuits underway brought by states, Missouri, Idaho, Kansas, Florida, and Texas, also challenge either the FDA’s original approval or subsequent modifications to the REMS. Florida and Texas have agreed to pause their litigation while the FDA continues its internal review.

Whole Woman’s Health Alliance, an independent abortion provider in Virginia with other independent abortion providers in Montana and Kansas, filed a lawsuit (Whole Woman’s Health Alliance v. FDA) against the FDA in May 2023, asserting that the FDA violated the Administrative Procedure Act when imposing REMS on mifepristone. The lawsuit seeks to remove all the REMS for mifepristone. This case is ongoing, and the Plaintiffs have opposed a stay to allow the FDA to continue its internal review.

Separate litigation is also testing whether the FDA’s regulation of mifepristone preempts state restrictions. In GenBioPro v. Raynes (West Virginia), GenBioPro challenged the state’s near-total abortion ban; in July 2025, the Fourth Circuit affirmed dismissal of the case, holding that the FDA’s mifepristone policies do not preempt West Virginia’s ban. In a second case, brough in North Carolina, Bryant v. Stein(formerly Bryant v. Moore), a physician argues that the FDA’s dispensing framework for mifepristone preempts additional state-law restrictions—on the theory that the FDA considered more stringent limits on mifepristone and chose not to adopt them, so states may not impose those same restrictions. There is a Louisiana state court challenge brought by Birthmark Doula Collective to Louisiana’s law which classifies mifepristone and misoprostol as controlled substances, subjecting these medications to controlled-substance storage, prescribing and dispensing rules. This case tests whether a state can use controlled-substance classification to restrict access to medication.

Litigation Challenging State and Federal Regulation of Mifepristone, as of May 4, 2026 (Table)

Looking Ahead

The return of mifepristone to the Supreme Court underscores how many questions remain post-Dobbs about how state authority to regulate abortion intersects with federal authority to regulate drugs. In Louisiana v. FDA, the immediate question before the Court is whether Louisiana has legal standing to challenge FDA’s 2023 REMS. Justice Alito issued an administrative stay of the Fifth Circuit’s decision until May 11, 2026. How the Supreme Court handles the emergency appeal once that stay expires will determine whether the rollback of the 2023 REMS remains in effect while the case proceeds and will signal how the Supreme Court views deference to the FDA on matters of drug approval and safety.

While the Supreme Court considers this case, related lawsuits are pulling in opposite directions—other state-led challenges also seeking to restore the in-person dispensing requirement for mifepristone, provider-led cases arguing the current REMS are unlawfully burdensome, and preemption cases testing whether states can restrict an FDA-approved drug. At the same time, state shield laws offer protections to providers who prescribe and mail medication abortion to patients in states with abortion bans, intensifying interstate conflict and raising new questions about enforcement and jurisdiction.

Regulation of AI in Prior Authorization and Claims Review: A Look at Federal and State Consumer Protections

Published: May 6, 2026

Introduction

Rapid technological developments in artificial intelligence (AI) have resulted in growing public attention to the potential benefits and challenges of these developments as they relate to health care. The Trump administration recently released A National Policy Framework for Artificial Intelligence (“AI Framework”), a set of legislative recommendations that could jump-start congressional activity on the application of AI across a variety of policy areas, not just health care. A core part of the AI Framework emphasizes establishing federal AI policy that preempts many state AI laws to reduce barriers for deploying AI applications. Preemption could nullify state consumer protections governing the use of AI in health coverage, such as prior authorization, and claims review and appeals. This Issue Brief discusses the types of consumer protections for use of AI in prior authorization and claims review, describes the Trump administration’s AI Framework, and highlights areas to watch as Congress considers AI legislation.

Use of AI in Prior Authorization and Claims Review

The use of AI technology has been embraced by all participants in the claims review cycle: patients, providers, and insurers. The box below describes current uses of AI technology for each party involved in prior authorization and claims review. Prior authorization and claims review are related but distinct steps in the coverage review and reimbursement process (claims review cycle) where AI might be used. Prior authorization is a managed care tool that evaluates whether an item or service is covered by a health plan prior to a patient’s receipt of the care. Claims review is often associated with a determination by an insurer after care is provided about whether and how much to pay for the item or service. Both involve similar decision-making and consumer appeal rights.

The claims review cycle includes health plan decisions made before a patient receives care (prior authorization review), after the care is received (often called retrospective or post-claim review), and while a patient is receiving the care (called concurrent review). Where the medical necessity of a service is involved, the term “utilization review” is often used to describe this process (definitions differ across state and federal requirements).

Parties Involved in the Prior Authorization and Claims Reviews Process and Their Use of AI

Insurers

Health insurers and other third-party administrators (TPAs), such as pharmacy benefit managers (PBMs), use some form of automation to process the millions of health care claims they review each year. Automation broadly includes the use of algorithms. One definition describes algorithms as a “procedure or set of rules that is applied to a dataset to achieve a certain function or purpose.” Such algorithms, or decision trees, have been used to generate approvals for treatment and have existed in the health care administration for some time.

AI has gained attention in recent years for its use to improve the speed and efficiency of existing automated processes, learn from historical claims outcomes (i.e., claims information an insurer has from its enrollees), and predict coverage determinations based on past patterns. Technology companies are vying for insurers and TPAs to adopt their AI-related products with the promise of faster, more accurate claims review. According to a recent National Association of Insurance Commissioners (NAIC) survey of 93 insurance companies in 16 states, 84% of responding insurers across health care insurance product lines use AI or machine learning for a broad range of tasks such as utilization management practices, disease management programs, and prior authorization processes.

Health Care Providers

Providers—hospitals and clinicians—use AI to enhance their ability to prepare and submit health insurance claims for reimbursement from insurers and TPAs. AI tools are being added to health system “revenue cycle management” (RCM)—the processes used to manage health system financial operations and improve functions such as coding, insurance eligibility checks, and billing. For example, generative AI allows clinicians to create patient encounter summaries (using ambient scribes) that are automatically included in the patient’s electronic health record and moved across interoperative systems and generate content to accelerate the prior authorization and claims review process. The use of AI to create electronic records of patient visits can also allow providers to maximize payments for services by assigning billing codes that command higher rates.

Patients

These same AI systems can assist patients (and their doctors) in appealing a prior authorization or claim denial by, for example, using a patient’s medical information, health plan documents, and clinical guidelines to generate appeal letters and other documentation needed in the appeal process. Various entities are promoting these tools directly to patients; some services charge a fee, and others do not. In addition, recent efforts to enhance data interoperability have encouraged the industry to develop apps that patients can use to consent to the sharing of their health information for multiple purposes, including to help with prior authorization review.

Connection to Interoperability. Developments in technologies to enhance interoperable systems (electronic data sharing among plans, providers, and patients) may make data more readily available for the application of AI technology in prior authorization and claim decision-making. Federal regulations will soon require some health plans to implement application programming interfaces (APIs) to collect and share data among patients, plans, and providers in an effort to streamline and expedite prior authorization review. While this may be helpful to patients and providers, increased data sharing could also result in data being captured inappropriately and used for purposes that might not be allowed under current interoperability agreements, for example, for commercial sale and/or to train new AI tools.

Risks to consumers include the potential for inaccurate or biased outcomes and privacy breaches. AI systems can help insurers and TPAs triage and make coverage decisions, often without any human involvement in the process. Yet the nature of much of this decision-making requires an individualized, sometimes clinical, review of a patient’s unique circumstances. The use of an AI-based algorithm to aid in these decisions may limit full review of a claim when no human judgment is applied. Many insurers made a voluntary pledge in 2025 to have medical professionals review prior authorization denials that involve clinical issues. Still, the use of AI by an insurer or TPA in claims review, even for purely administrative, nonclinical tasks, might lead to incorrect predictions and decisions if the AI model’s data input is incorrect or missing key information. In the past few years, patients have brought class action lawsuits challenging the use of specific algorithms in claims denials, arguing that their denials were improper due to a failure to perform an individual assessment and a lack of transparency about the algorithms and underlying data used to train the AI tool. These cases are still moving through the courts.

Data used in AI tools, either obtained through a patient’s electronic medical record or uploaded by the patient, could create privacy and security risks that may not be protected under the Health Insurance Portability and Accountability Act of 1996 (HIPAA). HIPAA applies only to health plans, health care providers, and health care clearinghouses, not to the technology companies and other third-party entities that access health information. Patient information obtained through an interoperable electronic health records (EHR) system has been the topic of recent litigation, with an EHR technology company claiming that another company obtained patient data under false pretenses and sold it.

Furthermore, the reliability of an AI tool can be compromised when trained on biased data. For example, one study found that algorithms using health care costs as a proxy for health care needs greatly underestimated the needs of Black patients compared to White patients. Health care costs are often lower for Black patients because they have less access to care, not because they have less clinical need. In this case, treatment decisions based on such algorithms may exacerbate health disparities.

The Trump Administration’s AI Framework

Promoting AI development. While the Trump administration’s AI Framework contains few details and no recommendations specific to health care or insurance claims review, it broadly recommends expanding the use of AI and imposing only limited federal restrictions through existing agency structures and “industry-led standards.” For example, the AI Framework recommends legislation that would prevent the U.S. from “coercing technology providers, including AI providers, to ban, compel or alter content based on partisan or ideological agendas.” It also recommends that Congress authorize resources to make federal datasets accessible to “industry and academia” for training AI systems.

Preempting some state law protections while keeping others. A prominent part of the AI framework is its proposal that Congress develop national standards that preempt “cumbersome” state AI laws. This would create federal legislation that aims to stop or prevent these state laws from being implemented. The Administration suggests that these state laws result in a patchwork of different requirements that could restrict U.S. competitiveness. The AI framework also says that states should not be allowed to penalize AI developers for a third party’s “unlawful conduct” involving their models.

At the same time, the Administration recommends that any legislation “respect the principles of federalism” and not preempt traditional state policy power that allows states to enforce general state laws against AI developers and users, including laws to “protect children, prevent fraud, and protect consumers.”

This framework is consistent with earlier Trump administration actions, including a December 2025 Executive Order restricting state AI regulation and establishing a Department of Justice litigation task force to challenge state AI laws that conflict with federal policy. A July 2025 AI Action Plan (stemming from another Executive Order) included a recommendation that federal agencies not allow AI-related federal funding to states with “burdensome AI regulations.” The AI Action Plan was released shortly after congressional Republicans’ unsuccessful attempt to include a 10-year ban on state regulation of AI in the 2025 budget reconciliation law.

Significantly changing the previous administration’s policies. The Trump administration’s actions mark a significant shift in priorities and approach to AI policy from those of the Biden administration, which sought to establish federal safeguards for the use of AI in health care. The Trump administration rescinded a Biden-era Executive Order that set out an agenda for the development and use of AI “to protect American consumers from fraud, discrimination, and threats to privacy” and “promote safe and responsible” use in health care.

Federal and State Efforts to Regulate AI Use in Prior Authorization and Claim Review

Federal Regulation and Oversight of AI

Few federal standards apply specifically to the use of AI in the prior authorization and claims review process, but all coverage decision-making for both public and private coverage includes general standards intended to ensure reviews are fair, substantive, and timely. These standards are fragmented across federal agencies with separate oversight responsibilities for different health coverage markets.

For private employer-sponsored plans, the federal government, through the U.S. Department of Labor (DOL), oversees claims and appeals process requirements in the Employee Retirement Income Security Act (ERISA). ERISA generally exempts self-insured plans established by private employers from most state insurance laws, including claims review protections, and would likely preempt state AI laws that relate to the claims review process. Most workers with employer-sponsored insurance are in a self-funded plan, meaning that many consumers are not guaranteed state protections related to the use of AI in claims review, where they exist.

These ERISA claims and appeals rules were the basis for reforms applied across all private health coverage in the Affordable Care Act. These reforms established a federal floor of protections for the internal claims and appeals process for those with Marketplace and off-Marketplace private insurance and added an option for all consumers with private coverage to appeal denied claims through an “external review” by an entity independent of the plan.

ERISA requires all employer plan sponsors to ensure the “full and fair” review of all health claims. What “full and fair” means in the context of the use of AI tools in the claims process is yet to be interpreted through guidance or updated regulation. ERISA also contains “fiduciary” rules requiring employers and other fiduciaries to act in the best interest of plan enrollees and monitor vendors’ activities. While these standards might provide some protection to employees related to an employer plan’s use of AI, in practice, fiduciary standards have rarely been applied to employer health plans, and to date, enrollees have not been successful in advancing litigation to challenge employers for breaching their fiduciary duties related to the health plans they sponsor.

Still, one recent DOL case against a large TPA alleged a fiduciary violation and a failure to follow ERISA claims rules when the TPA automatically denied claims in bulk without making an individual medical necessity evaluation for each under the terms of the plan. While these allegations did not necessarily involve AI, the TPA allegedly used an automated process without human review to issue denials. This case was settled with the establishment of a fund to compensate enrollees for improperly denied claims.

Federal guidance specific to AI use in prior authorization and claims review in Medicare and Medicaid has been limited. Both programs have their own claims and appeal consumer protections under federal requirements (and some state standards also apply to Medicaid).

Medicare. 2023 Medicare Advantage regulations and additional 2024 guidance clarify that Medicare Advantage organizations cannot make medical necessity decisions using an algorithm or software that does not consider individual circumstances. Denials based on medical necessity must be reviewed by a health care professional. Regulations proposed in 2024 that addressed bias and discrimination in the use of AI by Medicare Advantage plans were not finalized by the Trump administration. Additionally, the federal government is testing the use of AI to make certain prior authorization decisions for specific services in traditional Medicare through its Wasteful and Inappropriate Services Reduction (WISeR) Model, contracting with AI technology companies to administer this pilot program in six states.

Medicaid. Current Medicaid regulations do not directly address the use of automation in prior authorization. Medicaid managed care regulations require that any managed care organization (MCO) decision to deny services be made by “an individual” with appropriate expertise, but do not explicitly address AI use. Through state managed care contracts (which are reviewed and approved by CMS), states can set requirements for plan performance and reporting, such as requiring plans to disclose the use of AI in prior authorization processes. The Medicaid and CHIP Payment and Access Commission (MACPAC) has recently issued draft recommendations on the use of automation in Medicaid prior authorization.

State AI Consumer Protections in Prior Authorization and Claims Review

In recent years, some states have advanced laws and regulations aimed at protecting consumers from possible harm stemming from algorithmic decision-making systems, such as privacy breaches, inaccuracies, and bias. AI-related legislation continues to be debated in almost every state legislature, with some efforts garnering bipartisan support. Some states have issued regulations and other guidance under existing laws instead of or in addition to new state laws.

State laws specify new and existing AI consumer protections. Some state laws contain wide-ranging protections meant to cut across different sectors of the economy and apply to a broad range of entities, such as developers and those who deploy or use the technology for business purposes. Other state laws are specific to industry sectors (e.g., health care), topics (e.g., employment, civil rights, education), or uses, such as utilization review in health insurance.

Broad state laws include those that prohibit unfair or deceptive acts and practices. All 50 states have broad consumer protection laws that prohibit unfair or deceptive acts or practices. These laws are enforced by state attorneys general, and sometimes also allow a consumer to sue directly for a violation of the law (a “private right of action”) instead of relying on the state alone to enforce it. Colorado and Utah are examples of states that have amended their consumer protection laws to provide for general AI consumer protections.

Depending on the specific state law, these broader consumer protection laws might be used to address consumer harm resulting from the use of AI in prior authorization and claims review. Additionally, a growing number of states have updated longstanding state health insurance standards for managed care related to utilization review to clarify how these rules apply to AI (Figure 1). Almost all of the laws are focused on the decision-making process of utilization review, sometimes defined under state rules as individualized decisions about whether a given service is medically necessary based on the patient’s individual clinical circumstances. These laws do not necessarily include administrative claim review decisions that do not involve a medical necessity determination, such as whether a claim is for care that is excluded under the plan.

State Laws  on AI and Prior Authorization and Claims Review Enacted as of April 28, 2026 (Choropleth map)

Each state law related to the use of AI in prior authorization and/or claim review has its own unique requirements, but major themes include:

  • Human review of claim denials required. Some state laws include a provision that only a licensed health care provider may issue adverse determinations (a denial) and that AI cannot be used as the sole decisionmaker. For example, Illinois law requires that only a “clinical peer” make an adverse determination based on medical necessity and does not allow the sole use of an “algorithmic automated process” to make these decisions.
  • AI tools must take individual clinical circumstances into account. A couple of these states require that any AI tool used for utilization review bases its determination on an enrollee’s unique clinical history. Alabama, for instance, mandates that insurers who use artificial intelligence to make prior authorization determinations ensure that they base these decisions on an enrollee’s clinical history and clinical circumstances.
  • Disclosure of AI use. A few of these states, such as Utah for example, require entities that use AI to conduct utilization review to disclose its use to the public, the state department of insurance, health care providers in their network, and each enrollee.
  • Review of AI tool outcomes. Some state laws also require entities that perform utilization review to periodically review performance and outcomes of AI tools they use in order to check accuracy and reliability. California law requires that an AI tool be periodically assessed and revised to ensure maximum accuracy and reliability.
  • Limits on the use of patient data to protect privacy. Several of these state laws include language that prohibits those conducting utilization review from using patient data beyond its intended purpose and contrary to HIPAA or state law confidentiality protections. Maryland law is one example.
  • AI tools must be open to inspection, including the underlying algorithms. Some of these laws mandate that AI tools for utilization review be open to audit by regulators. In Texas, the commissioner is allowed to audit and inspect a utilization review agent’s use of an automated decision system for utilization review at any time.
  • AI protections against bias and discrimination. A few state laws, such as Washington's, require that AI tools be applied “fairly and equitably” and cannot result in discrimination, either directly or indirectly, against an enrollee.

New state guidance aims to exercise state authority to regulate AI use. Some states have issued guidance to make clear how existing state legal protections apply to AI. For example, in 2024, the Massachusetts Attorney General released a public Advisory explaining how the state’s existing consumer protection, civil rights, and data privacy laws apply to developers, suppliers, and users of AI, and how they could impact consumers in Massachusetts.

Insurance regulators in some other states have taken a similar approach, issuing new guidance to clarify how existing state law applies to AI and provide more specific information to insurers about their obligations concerning the use of AI. As of early April 2026, at least 25 states have issued guidance based on a model bulletin adopted in 2023 by the National Association of Insurance Commissioners (NAIC). The model bulletin applies to all types of state-regulated insurance (not just health insurance) and addresses the use of AI across all aspects of the insurance life cycle, including claims administration and payment, fraud detection, product development, and rating and pricing. It establishes the expectation that consumer-facing decisions made or supported by AI systems comply with existing insurance laws and regulations, including protections against unfair trade practices and illegal discrimination. It also instructs insurers to adopt policies and procedures with specifics about how AI is used and to implement controls to mitigate the risk of adverse outcomes. It specifies that insurance oversight includes the ability of regulators to inquire about the development, deployment, use, and outcomes of any AI system or predictive model used by insurers or their third-party vendors, as well as request information about system validation, testing, and ongoing audits of AI systems.

Issues To Watch

Striking a balance between the advancement of technological innovation that might save time and money and preventing harm to consumers is not a new challenge. It has been at the heart of consumer protection law for decades. AI presents just the latest policy challenge that policymakers, regardless of party affiliation, are faced with. For health insurance claims review specifically, calls for additional transparency and oversight of the process are longstanding and predate the use of AI. Future congressional action on AI will likely be shaped by the following issues:

The role of state-level consumer protections. Whether the federal government can preempt the application of state consumer protection laws in this area is an open question. The Trump administration’s AI framework appears to acknowledge that certain state protections should continue to apply. A key issue in the development of any federal legislation will be deciding what state actions are consistent with states’ traditional role in overseeing health care and insurance and should be preserved, and which ones are best placed at the federal level for uniformity and consistency. Setting a clear framework for when state and federal protections can and cannot coexist will likely be part of the policy debate.

Some federal preemption provisions can create uncertainty and confusion for consumers. Ongoing legal battles, for example, about whether state pharmacy benefit laws apply to self-insured employer plans under ERISA preemption, are in the process of being clarified through court decisions. This leaves consumers in limbo about what protections they have. Given the rapid changes and risks associated with AI technologies, whether federal preemption is a workable approach for state AI laws is another open question.

Benefits and limitations of a national framework. A single federal standard that preempts most state protections could be easier to build consensus around and simpler for the public to understand, but the current deregulatory agenda for the federal government could mean lax oversight of fast-developing technology. A recent proposed interoperability regulation, for instance, would eliminate some federal certification standards for health IT developers, including those related to transparency of AI data sources and audit reporting.

On the other hand, federal agencies that have not played a role in AI and claims review in the past could increase oversight activities. For example, the Federal Trade Commission has some responsibilities for enforcing unfair and deceptive trade practices standards. Also, some have suggested that the Food and Drug Administration (FDA) should regulate the algorithms that health plans use to determine coverage in the same way the agency oversees AI used in medical devices through a premarket review and evaluation of these tools.

Evaluation of the impact of AI tools in prior authorization and claims review. Providers may use AI to enhance their billing and collection capabilities, and plans work in the opposite direction by using AI in claims review and audit to rein in spending, raising the question of how these tools impact costs to the health system overall. The challenge is to evaluate these tools in real time to determine whether the benefits of AI use in this area (and efforts to encourage its development under federal legislation) outweigh the risks. Access to information about the precise mechanisms of these tools is limited, and efforts to obtain information, for instance, about the AI tools involved in the CMS WISeR model, have resulted in litigation.

Assessment of risks to patients. The enthusiasm about AI technology that might assist consumers in navigating the complexity of insurance bills, claims, and appeals is sometimes tempered by concerns about the risks of incorrect information and claim denials, bias, and privacy and security. Privacy is a particular concern, given the limits of the federal HIPAA standards in reaching the technology companies involved in developing or implementing AI solutions. A KFF poll found that 77% of the public is concerned about the privacy of personal health information provided to AI tools.

In addition to the risk involved when a consumer enters their health information into an AI tool, improper access to this information to test or train AI is also a risk. The Trump administration’s AI framework urges Congress to provide resources to make “federal datasets” available to industry and academia. Concerns about the federal government accessing individually identifiable data from federal agencies to train AI models or for other purposes are growing, raising questions about what additional safeguards might be important in protecting consumers.

KFF Health Information and Trust Polling Dashboard

Key insights and trends from KFF’s polling on Health Information and Trust

Last Updated:

May 6, 2026

Trusted Sources of Health Information

Who the Public Trusts For Health Information

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Doctors and other health care providers are the public’s most trusted source of health information, while trust in government health agencies and officials is much more divided. A large majority of adults express at least “a fair amount” of trust in their doctor for reliable information about health issues, while half say they trust the CDC or FDA and fewer than half express trust in their state government officials, HHS Secretary Robert F. Kennedy, Jr., or President Trump.

U.S. Adults Are Most Trusting of Their Own Doctors for Health Information; Fewer Trust Government Health Authorities (Stacked Bars)

Partisanship shapes who the public trusts for health information, especially when it comes to Secretary Kennedy and President Trump. Two-thirds of Republicans, rising to three-quarters among MAGA-supporting Republicans, say they trust Secretary Kennedy and President Trump for reliable health information compared to one-third or fewer independents and Democrats who say the same. On the other hand, Democrats are somewhat more likely than Republicans to trust their state officials for health information, while similar shares of Democrats and Republicans say they trust the CDC or FDA. Individual health care providers are the most-trusted source for health information across partisanship.

Across demographic groups – including age, gender, race and ethnicity, and education – health care providers remain the most trusted source of health information. For other health information sources, trust does not differ consistently across most of these groups, but White adults and those without a college degree are more likely than their peers to express trust in Secretary Kennedy and President Trump for health information.

Confidence in Federal Health Agencies

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Most of the public lacks confidence in agencies like the CDC or FDA to carry out many of their core responsibilities. While Democrats are somewhat more likely than Republicans to have at least “some confidence” in government health agencies to ensure vaccine safety and effectiveness and make recommendations about the childhood vaccine schedule, fewer than half across partisans have confidence in these agencies to make decisions based on science. For more information, see KFF’s January 2026 Tracking Poll on Health Information and Trust.

Fewer Than Half the Public and Partisans Are Confident in Government Health Agencies To Make Decisions Based on Science (Bar Chart)

Less than half of the public and partisans express at least “some confidence” in the CDC, FDA, or EPA to act independently without outside interference. Democrats are somewhat more likely to say they have confidence in the CDC to act independently, with almost half expressing confidence. On the other hand, four in ten or fewer adults and partisans express confidence in the independence of the FDA or the EPA. For more information, see KFF’s April 2026 Health Tracking Poll.

Fewer Than Half the Public Have Confidence in the CDC, FDA, or EPA To Act Independently Without Interference from Outside Interests (Split Bars)

Trends in Trust of Government Health Agencies and Officials

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At the onset of the COVID-19 pandemic, there were high levels of bipartisan trust in information about the new virus from the U.S. Centers for Disease Control and Prevention (CDC). Trust in the agency for information about COVID-19 vaccines, and vaccines more generally, subsequently declined amid widening partisan divisions and large drops in Republican trust. Democratic trust in the agency has since declined significantly following President Trump’s reelection and the confirmation of Robert F. Kennedy Jr. as HHS Secretary. Amid these partisan shifts, half of the public now express trust in the CDC for reliable vaccine information. Keep scrolling to see trends among the public and partisans.  

KFF polling has found trust in vaccine information from other health agencies and officials has also declined amid partisan divisions since 2020, including for the U.S. Food and Drug Administration (FDA), state government officials, and local public health departments. 

Who Parents Trust for Childhood Vaccine Information

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Among parents of children under age 18, pediatricians are the most trusted source of reliable vaccine information. Smaller shares, but still majorities, also trust their local public health department, the CDC, and the FDA. Over half of parents trust their friends and family for vaccine information, while far fewer express trust in Robert F. Kennedy Jr., pharmaceutical companies, or health and wellness influencers. As with the public overall, partisanship plays a role in who parents trust for vaccine information. For more information, see the KFF/Washington Post Survey of Parents.

Among parents, Secretary Kennedy garners trust on vaccines from a majority of Republican supporters of the Make America Great Again, or MAGA, movement (18% of all parents) and supporters of the Make America Healthy Again, or MAHA, movement (38% of all parents). While slim majorities of these MAGA and MAHA parents trust Kennedy for vaccine information, larger shares express trust in their child’s pediatrician.

News, Social Media, and AI

Use and Trust of News Sources for Health Information

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KFF’s Health Misinformation Tracking Poll Pilot measured the public’s consumption of a variety of television, print, radio, and digital news media sources as well as their trust in these sources for information about health issues. Overall, few adults both regularly consume most news sources and trust them a lot for information on health issues, with local and network television news topping the list. Nearly a quarter (23%) of adults say they regularly watch their local TV station and would trust it “a lot” for health information, while a similar share (21%) say the same about national network news. Other news sources, including NPR, CNN, Fox News, local newspapers, The New York Times, digital news aggregators, and MSNBC have trusting audiences that make up between one in ten and one in six of the overall public.    

Stacked bar chart showing percent who say they would trust information about health issues "a lot" and "a little" if they were reported by specific news sources.

Social Media Use for Health Information

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Just over half of adults say they use social media to find health information and advice “at least occasionally,” including larger shares of younger adults, and Black and Hispanic adults. For more information on social media use and trust see KFF’s July 2025 Tracking Poll on Health Information and Trust.

Stacked bar chart showing how often U.S. adults report using social media. Results shown by age gender, race/ethnicity, and party ID.

While just over half of the public report actively using social media to find health information and advice, larger shares report being exposed to such information, with majorities saying they have recently seen content related to weight loss, diet, or nutrition and mental health.

While four in ten social media users say they regularly get information about news and politics from social media influencers, far fewer (15%) say they turn to influencers for health information and advice. Younger adults, Black adults, and more frequent social media users are more likely than their peers to say they rely on influencers for health information. For more information on the relative impact of influencers on the public and health policy debates, see KFF CEO Drew Altman’s column.

Split bar chart showing the share of U.S. adults who report regularly getting health information and advice and news about politics from influencers on social media. Results by age gender, party ID, and social media use.

Trust in Social Media for Health Information

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Across different social media platforms, fewer than half of users say they find at least “some” of the health information they see on these platforms to be trustworthy. Younger users tend to be more trusting than older users of health content on certain platforms including TikTok, YouTube, Instagram, and Reddit.

While few say they trust social media when it comes to health, KFF’s 2023 Health Misinformation Tracking Poll Pilot found that that those who turn to social media more frequently for health information may be more susceptible to health misinformation. Adults who reported using social media at least weekly were more likely than less frequent users to believe at least one false claim related to either COVID-19, reproductive health, or firearms.

Split bar chart showing percent who have heard at least one item of COVID-19 or vaccine misinformation, reproductive health misinformation, and firearm misinformation, by total and frequency of use of social media for health information and advice.

AI and Health Information

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About a third (32%) of the public reports turning to AI chatbots for health information and advice in the past year – rivaling social media as a health information source, but less common than reliance on health care providers or internet search engines (where they may be encountering AI generated results, even if they are not looking for them). The share of adults using AI for health information includes three in ten who say they’ve used these chatbots in the past year for information or advice about their physical health, and one in six who’ve used them for mental health information or advice. For more information, see KFF’s March 2026 Tracking Poll on Health Information and Trust.

Split bar chart showing percent who have sought information or advice about their physical or mental health from specific sources in the past year.

Larger shares of younger adults report turning to AI for either physical health or mental health information in the past year. When it comes to mental health advice, uninsured adults and Black and Hispanic adults are more likely than insured adults and White adults to have turned to AI.

People report using AI for either physical health or mental health information in a variety of ways, most commonly to look up symptoms or general information about health conditions. Fewer say they used AI to help make decisions about whether to seek medical care for either physical or mental health concerns.

Bar chart showing percent who say they have used artificial intelligence tools for information and advice about their physical health in the past year, and whether they have used it for specific reasons.

The most common reason people cite for turning to AI for health advice is wanting quick or immediate support. Many also cite wanting to look up information before seeing a provider or feeling more comfortable looking up health questions privately. One in five cite health care access or affordability issues as major reasons for turning to AI for health questions, including larger shares of younger adults and those with lower household incomes

Among the public overall, few adults say they trust AI tools to provide reliable information about health, but most adults who have used AI for health information and advice say they trust these chatbots to provide reliable health information.

Split bar chart showing trust in AI tools to provide reliable information about health and mental health respectively. Results shown by total adults and by use of AI for different types of health information.

False or Unproven Health Claims

Awareness and Belief in False or Unproven Health Claims

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Exposure to health misinformation is often widespread, but relatively small shares of the public express certainty that many false or unproven claims are true. In fact, most of the public fall in a “malleable middle,” saying these claims are either “probably true” or “probably false.” The public’s uncertainty around false or unproven health claims related to COVID-19 , vaccines , measles  and the purported causal link between Tylenol and autism presents an opportunity for interventions to clear up confusion and deliver accurate information.

Measuring Exposure

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KFF polls have measured exposure to a wide array of false, misleading, and unproven health claims since 2023. Exposure varies widely depending on the topic and prominence of news coverage of the claim. The most widely heard of those tested in KFF polls is that taking Tylenol during pregnancy increases the risk of a child developing autism, a claim cited by President Trump in a widely covered September 2025 press conference.

The Malleable Middle

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Across an array of false or unproven health claims measured in KFF surveys, few adults are certain these claims are “definitely true” while much larger shares consistently say they are “definitely false.” For most claims, at least half express uncertainty, falling into the malleable middle and saying the claims are either “probably true” or “probably false.” The six most recent claims measured in KFF surveys in 2025 are shown below.

While Few Adults Think False or Unproven Health Claims Are Definitely True, Many Express Uncertainty (Stacked Bars)

KFF polling has measured exposure to and belief in false or unproven claims across a wide array of topics. For information on belief in additional claims about COVID-19, reproductive health, and gun violence, see KFF’s Health Misinformation Tracking Poll Pilot.  For information on additional false claims related to COVID-19, see KFF’s May 2022, and October 2021 COVID-19 Vaccine Monitors.

Belief in False or Unproven Health Claims

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KFF polling has found partisanship and education play a substantial role in belief of false or unproven health claims about vaccines, COVID-19 and measles. Republicans and adults without a college degree are consistently more likely than Democrats and college educated adults, respectively, to believe or lean towards believing false claims related to COVID-19, measles, and vaccines.

Beyond partisanship and education, younger adults and Hispanic adults are more likely than their peers to believe or lean toward believing some of these false or unproven health claims but not others. These differences show that susceptibility to health misinformation among some groups can vary depending on the topic, which may reflect different information channels relied upon by these groups (see social media and news sources sections for more info).

Appendix For False or Unproven Health Claims

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KFF polling has sought to examine the public’s exposure to and belief in a wide array of false or unproven health claims. Many of the false or unproven claims measured in KFF surveys have been amplified by or directly made by government officials, while others have been more nebulously shared and spread in public media over the years. Below is a list of sources to document these claims’ inaccuracy.

Table

Vaccine Attitudes

Views on Vaccine Safety Among the Public

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Most U.S. adults, including majorities across partisans, express confidence in the safety of many routine vaccines for children, including MMR, polio, and hepatitis B. Similarly, large majorities of adults ages 50 and over are confident that vaccines for pneumonia and shingles are safe. Views on the safety of COVID-19 and flu vaccines for both adults and children are more divided, with large shares of Democrats expressing confidence compared with smaller shares of Republicans. For more information, see KFF’s January 2026 and April 2025 Tracking Polls on Health Information and Trust.

Parents’ Vaccine Attitudes and Behavior

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In summer 2025, large majorities of parents expressed confidence in the safety of childhood vaccines for polio and measles, mumps, and rubella (MMR), but parents’ views on the safety of flu and COVID-19 vaccines were more polarized. About two-thirds of parents say the flu vaccines are safe for children, while fewer than half say the same about COVID-19 vaccines, with divisions along partisan lines. Beyond partisanship, parents who support the Make America Healthy Again (MAHA) movement (38% of parents), Black parents and parents under age 35 are less likely than their peers to be confident that many routine vaccines are safe for children. For more information, see the KFF/Washington Post Survey of Parents.

Majorities of Parents Are Confident in the Safety of Childhood Polio and MMR Vaccines, but Vaccines for COVID-19 and the Flu Are Divisive (Split Bars)

Most parents report keeping their children up to date on childhood vaccines, but about one in six say they have ever skipped or delayed at least one childhood vaccine for any of their children (excluding seasonal vaccines like flu and COVID-19). Despite strong uptake, many parents express skepticism towards vaccine safety testing and the number of vaccines recommended by the CDC (this survey was fielded prior to recent changes to the childhood vaccine schedule announced by HHS in January 2026). Younger parents and those who identify as Republicans are more likely than their counterparts to endorse vaccine-skeptical attitudes and to report skipping vaccines for their own children. For more information, see the KFF/Washington Post Survey of Parents.

Split bar chart showing percent who say specific false claims about vaccines and diseases are true. Results shown by total parents, parents by vaccine choice, party identification, and support for MAGA.

The KFF/Washington Post Survey of Parents tested belief in several false, misleading, or unproven claims amplified by HHS Secretary Robert F. Kennedy Jr related to vaccines, measles, and autism. While few parents think these claims are true, parents who have skipped or delayed at least one recommended vaccine for their children are at least three times as likely as those who have kept their children up to date to say these false or unproven claims about vaccines or measles are true.

Split bar chart showing percent who say specific false claims about vaccines and diseases are true. Results shown by total parents, parents by vaccine choice, party identification, and support for MAGA.

mRNA Vaccine Safety

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COVID-19 vaccines and some other vaccines currently under development rely on a vaccine technology known as messenger-RNA (mRNA), which has long been the subject of misinformation. While few adults view mRNA technology as unsafe, the technology remains obscure to much of the public, with about half saying they don’t know enough to say. For more information, see KFF’s April 2025 Tracking Poll on Health Information and Trust.

Stacked bar chart showing how safe U.S. adults, by partisanship, think mRNA technology in vaccines is.
Poll Finding

KFF Health Tracking Poll: MAHA and the Midterms

Published: May 6, 2026

Findings

Key Takeaways

  • About four in ten (41%) U.S. adults say they support the Make America Healthy Again (MAHA) movement – a group largely made up of Republicans and supporters of the Make America Great Again (MAGA) movement. Yet, many of the concerns elevated by the MAHA movement about food safety and corporate influence resonate with a larger share of the public beyond those who identify as supporters. Majorities of the public say there is not enough regulation of chemical additives in food (75%) or of pesticides used in agriculture (64%) in the U.S., and most adults express distrust in agricultural, food, and pharmaceutical companies to act in the public’s best interest. At the same time, confidence in the government agencies that are tasked with regulating these industries is low across partisans; about a third of the public express confidence in the FDA (36%) and the EPA (36%) to act independently without outside interference.
  • Even as MAHA issues resonate, the cost of health care is a more prominent focus for voters than issues like food and vaccine policy. Most voters say health costs will have a “major impact” on their decision to vote (55%) and who they vote for (61%) in November, compared to about four in ten who say the same of vaccine or food policy. Even among voters who support the MAHA movement, health care costs are the dominant priority by a wide margin when compared with other areas of health. When asked to select the most important health priority for the federal government, four in ten MAHA voters (42%) choose lowering health costs, twice the share who choose restricting chemical additives in the food supply (21%) and far outranking other MAHA priorities like reevaluating vaccine safety (10%), limiting corporate influence in U.S. food policy (8%), or restricting pesticide use in agriculture (8%).
  • Voters overall give the Trump administration low approval ratings on two key health areas elevated by the MAHA movement; about four in ten approve of the administration’s handling of vaccine policy (38%), and fewer than half approve of the administration’s handling of food policy (46%). The Democratic Party holds the advantage over the Republican Party in who voters trust to handle vaccine policy (41% vs. 25%) and to ensure federal health agencies act independently without corporate influence (33% vs. 24%). Voters are more evenly divided on whether they trust Democrats (31%) or Republicans (27%) to handle the safety of food additives and pesticides, or neither party (31%).

Who Are MAHA Supporters?

Make America Healthy Again (MAHA) is the political and public health movement promoted by the Trump administration and led by Health and Human Services (HHS) Secretary Robert F. Kennedy Jr. The movement has elevated issues such as vaccine safety, the role of corporate interests in federal health agencies, and the presence of chemical additives and pesticides in the food supply. KFF’s latest Health Tracking Poll shows about four in ten adults say they are supporters of the MAHA movement, but some of the concerns elevated by the MAHA movement resonate well beyond its core supporters.

Overall, about four in ten (41%) adults – and a similar share of voters (43%) – say they are supporters of the MAHA movement, with support closely tied to partisanship and support of the Make America Great Again (MAGA) movement. Two-thirds of MAHA supporters identify as Republican or Republican-leaning independents, including about half (52%) who are supporters of the MAGA movement. Far fewer MAHA supporters are Democrats or Democratic-leaning independents (21%) or independents who do not lean toward either political party (10%). Among registered voters who support MAHA, the partisan composition is similar.

Among MAHA-supporting voters, just over half say they “strongly” support the movement (55%) and 45% say they “somewhat” support it, translating to about one in four voters overall as strong MAHA supporters (24%) and one in five who “somewhat” support it (19%).

Stacked bar chart showing the party identification breakdown of those who support the Make America Healthy Again (MAHA) movement. Results shown among total adults and among voters.

When asked specifically why they support the MAHA movement, supporters give a range of answers, with the two most common relating to generally wanting people to be healthier (19%) and removing or regulating harmful substances in food (15%). Some MAHA supporters mention improving nutritional habits for Americans (7%) or lowering obesity rates (7%). Five percent of MAHA supporters cite health care access and affordability as their reasons for supporting the movement, despite the MAHA movement’s lack of focus on these issues. Just 4% of MAHA supporters cite vaccines and medical choice as their reason for supporting the movement. Few MAHA supporters specifically express support for President Trump or HHS Secretary Robert F. Kennedy Jr. (2%) as their reasoning for supporting the movement.

In Their Own Words: What is the Main Reason You Support the MAHA Movement?

“It is morally correct. We don’t want to poison our kids with vaccines they don’t need. We don’t want to eat food that leads to morbidity to make companies’ profit margins higher,” 43-year-old Republican man, Pennsylvania

“America uses far too much harmful ingredients that most other countries ban,” 28-year-old independent man, Indiana

“To get people healthier so taxpayers don’t have to pay for their health care,” 58-year-old Republican woman, Kentucky

“We should be promoting healthier lifestyles in the country,” 29-year-old Republican woman, Iowa

“The MAHA movement gives patients more freedom to choose the doctors they actually want to see…While I support it for now, I'll be waiting to see how these policies are actually implemented,” 39-year-old independent man, Washington

“It’s about making the next generation healthy,” 27-year-old independent woman, Arizona

“I followed Kennedy before he was in office and I’m hoping that he will continue with what he stood for before,” 51-year-old independent woman, Tennessee

“Health care should be a right,” 65-year-old Democratic man, New York

Where MAHA Concerns Resonate With the Public: Food Safety, Pesticides, and Industry Influence

Among the public, there is broad, bipartisan agreement that there is not enough government regulation of chemical additives in food and pesticides in agriculture in the U.S. Three-quarters of adults say there is not enough government regulation of chemical food additives, and about two-thirds (64%) say the same about pesticides used in agriculture. Majorities across partisan lines and among both MAHA supporters and those who do not support the movement agree, suggesting this is an area where MAHA’s concerns align with broader public sentiment.

Stacked bar chart showing the shares of the public who say there is not enough, about the right amount, or too much regulation of chemical additives in food and pesticides used in agriculture in the U.S. Results shown among total, by party identification, and by support for the Make America Healthy Again (MAHA) movement.

Some food industry groups and public health experts have argued that restrictions on approved food ingredients could limit access to affordable groceries for families in the U.S. When those who originally said there is “not enough regulation” of chemical additives in food or pesticides used in agriculture are presented with the caveat that increased regulation could lead to higher food prices for consumers, most still support increased regulation. But the possibility of increased costs does move some people. The share saying there is not enough regulation of food additives drops by 13 percentage points after hearing that increased regulation could increase costs (from 75% to 62%), with a similar drop in the share who maintain that there is not enough regulation of pesticides (from 64% to 52%).

Split bar chart showing share of public who say there is not enough government regulation of chemical additives in food and pesticides used in agriculture. Follow up question shows share of adults who say there is still not enough regulation even after hearing increased regulation could lead to higher food prices. Results shown among total.

The public perception that there is not enough regulation may be rooted in broader skepticism toward the industries themselves. Most U.S. adults do not trust pharmaceutical companies, food and beverage companies, or agricultural companies to act in the public’s best interest. One in four or fewer adults say they trust food and beverage companies (25%) or pharmaceutical companies (21%) “a great deal” or “a fair amount” to act in the public's best interest, while a somewhat larger share (40%) trust agricultural companies on this measure. Very small shares – fewer than 5% – trust each of these groups “a great deal” to act in the public’s best interest.

By contrast, seven in ten adults say they trust doctors and health care providers at least “a fair amount” to act in the public’s best interest.

Stacked bar chart showing the level of trust the public has in doctors/health care providers, agriculture, food/beverage, and pharmaceutical companies to act in the public's best interest. Results shown among total.

Across partisanship, and among those who do and do not support the MAHA movement, fewer than half trust agricultural companies, food and beverage companies, or pharmaceutical companies to act in the public’s best interest. However, most Democrats (80%), independents (69%), and Republicans (67%) have a great deal or a fair amount of trust in doctors and health care providers to act in the public’s best interest.

Split bar chart showing share of adults who say they have a great deal or a fair amount of trust in doctors/health care providers, agriculture, food/beverage, and pharmaceutical companies to act in the public's best interest. Results shown among total, by party identification, and by support for the Make America Healthy Again (MAHA) movement.

Confidence in the government agencies with major responsibilities for food safety and public health is low across partisans. Four in ten or fewer adults say they have “a lot” or “some” confidence in the U.S. Centers for Disease Control and Prevention (CDC) (40%), U.S. Food and Drug Administration (FDA) (36%), or the U.S. Environmental Protection Agency (EPA) (36%) to act independently without interference from outside interests. Democrats are slightly more likely than Republicans or independents to express confidence in the CDC (47% vs. 37% and 38%, respectively), but partisan differences largely disappear when it comes to confidence in the FDA and EPA.

U.S. adults who support the MAHA movement and those who do not are similarly skeptical. Four in ten MAHA supporters say they are confident in each of these agencies to act independently, leaving six in ten MAHA supporters who have “a little” or “no confidence at all.”

Split bar chart showing share of public who say they have a lot or some confidence in the CDC, FDA, or EPA to act independently without interference from outside interests. Results shown among total, by party identification, and by support for the Make America Healthy Again (MAHA) movement.

MAHA and Other Health Care Issues in the Election

Despite the resonance of these issues elevated by the MAHA movement, health care costs overshadow these concerns for voters heading into the 2026 midterm elections. Previously released findings from the April 2026 KFF Health Tracking Poll show health care costs remain a primary economic concern for the public. Reflecting that, costs are voters’ top health concern heading into the 2026 midterm elections, outweighing policy areas elevated by the MAHA movement, such as vaccines or food safety.

More than half of voters say health care costs will have a “major impact” on their decision to vote (55%) or which party’s candidate they will support (61%). When it comes to vaccine policy and food policy, about four in ten voters say these issues will have a “major impact” on their decision to vote or which party’s candidate they will support, at least 15 percentage points lower than the share who say the same about health care costs.

Stacked bar chart showing the shares of registered voters who say specific issues will have a major impact, minor impact, or no impact at all on their decision to vote or which party's candidate they would support in the 2026 midterm elections.

While the issue of health costs is more salient for Democratic voters than for Republicans, larger shares across partisans say health costs will have a major impact on their voting decisions than say the same about vaccine policy or food safety. For example, about half of independent voters (52%) say health care costs will have a major impact on their decision to turn out in November, compared to about four in ten who say the same about vaccine policy (39%) and food safety (38%). Patterns are similar for Republican voters (48%, 30%, and 34%, respectively) and Democratic voters (64%, 52%, and 40%, respectively).

Among voters who say they support the MAHA movement – a majority (56%) of whom identify as MAGA Republicans – at least half say the cost of health care will have a major impact on their decision to vote (51%) or which party’s candidate they support (56%). Despite the MAHA movement elevating issues such as vaccine and food safety, fewer MAHA voters – about four in ten – say vaccine policy or issues of food safety will majorly impact their voting decisions.

Stacked bar chart showing the shares of registered voters who say specific issues will have a major impact on their decision to vote or which party's candidate they would support in the 2026 midterm elections. Results shown by party identification and among voters who support the Make America Health Again (MAHA) movement.

MAHA-supporting voters express broad enthusiasm for federal action across the movement’s core agenda, but once again, health care costs remain the dominant priority. Nearly three-quarters of MAHA voters (73%) say lowering the cost of health care, including prescription drugs, should be a top priority for the federal government. This is followed by restricting the use of certain chemical additives in the food supply (68%) and limiting corporate influence on U.S. food policy (56%). Half of MAHA voters say reevaluating the safety of vaccines currently approved for use or restricting the use of pesticides in agriculture should be top priorities. Across all five items, one in ten or fewer MAHA voters say any of these efforts are “not too important” or “should not be done.”

Stacked bar chart showing priority levels of different health and food policy issues. Results shown among total registered voters who support the Make America Healthy Agan (MAHA) movement.

When asked to choose the single most important health priority to them, about four in ten MAHA-supporting voters (42%) choose lowering the cost of health care, including prescription drugs—twice the share who say the same of restricting chemical additives in the food supply (21%). Fewer cite reevaluating vaccine safety (10%), limiting corporate influence in U.S. food policy (8%), or restricting pesticide use in agriculture (8%) as their single top priority.

The cost of health care tops the list of health care priorities for MAHA voters regardless of partisanship. Among voters who support the MAHA movement, six in ten Democrats (57%) and four in ten independents (43%) and Republicans (40%) say lowering the cost of health care is the most important priority. For each of these groups, health care costs rank at least 14 percentage points ahead of restricting the use of chemical additives in food, and even further ahead of issues like reevaluating vaccine safety and restricting pesticide use.

Split bar chart showing the most important priority to registered voters who support the Make America Healthy Again movement when it comes to what the government could do in health and health care. Results reported among total MAHA voters and by party identification of MAHA voters.

Trump Administration Approval and Party Preference on MAHA Health Issues

Voters give the Trump administration low approval ratings on two key policy areas elevated by the MAHA movement. Just under half (46%) of voters approve of the administration’s handling of food policy, and a larger share (54%) disapprove. Just a few months after the changes made to the childhood vaccine schedule by HHS, about four in ten voters approve of the administration’s handling of U.S. vaccine policy (38%) and six in ten (61%) disapprove, including about half (47%) who “strongly disapprove.”

As the head of the MAHA Commission and Secretary of Health and Human Services, Robert F. Kennedy Jr. is the spokesperson for many of the administration’s federal health policies. About four in ten voters say they approve of the way Secretary Kennedy is handling his job (39%) and six in ten disapprove, including nearly half (46%) who “strongly disapprove.”

Stacked bar chart showing scale of approval of the way the Trump administration is handling areas of health and health policy and the way RFK is handling his job as HHS secretary. Results shown among total registered voters.

Unsurprisingly, voters are split along partisan lines, with the Trump administration receiving high approval ratings from Republicans on food and vaccine policy as well as Secretary Kennedy’s handling of his job at HHS, and most Democrats disapproving.

Split bar chart showing share of adults who say they approve of the way the Trump administration is handling areas of health and health policy and the way RFK is handling his job as HHS secretary. Results shown by party identification among registered voters.

Despite Secretary Kennedy’s leadership at HHS and recent changes focused on MAHA priorities, approval of the administration’s handling of issues like vaccine policy is far from unanimous among the movement’s supporters. About seven in ten MAHA voters approve of the administration’s handling of food policy (72%) and vaccine policy (67%), and Secretary Kennedy’s handling of his job as Health Secretary (69%). On each of these areas, just a third “strongly” approve of the administration and a similar share disapprove: tepid ratings for a group that aligns with Kennedy’s signature movement.

Stacked bar chart showing scale of approval of the way the Trump administration is handling areas of health and health policy and the way RFK is handling his job as HHS secretary. Results shown among total registered voters who support the Make America Healthy Again movement.

With about six months until the 2026 midterm elections, the Democratic Party has a strong edge over the Republican Party when it comes to who voters trust to address vaccine policy, and a narrower edge on ensuring federal agencies act independently. Voters are split over who they trust to do a better job ensuring food additives are safe. Democrats have a double-digit advantage over Republicans when it comes to who voters trust to handle vaccine policy (41% vs. 25%). While the Democratic Party has a smaller advantage among voters on which party they trust to ensure that federal health agencies act independently without corporate influence (33% vs. 24%), nearly four in ten (37%) say they trust “neither party.”

Similar shares of voters say they trust the Democratic Party (31%), the Republican Party (27%), or “neither party” (31%) more when it comes to doing a better job ensuring food additives and pesticides in the U.S. are safe.

Stacked bar chart showing which political party, the Democrats or the Republicans, the public trusts to do a better job in areas of health and health policy. Results shown among total registered voters.

Methodology

This KFF Health Tracking Poll was designed and analyzed by public opinion researchers at KFF. The survey was conducted April 14 – April 19, 2026, online and by telephone among a nationally representative sample of 1,343 U.S. adults in English (n=1,251) and in Spanish (n=92). The sample includes 1,023 adults (n=81 in Spanish) reached through the SSRS Opinion Panel either online (n=999) or over the phone (n=24). The SSRS Opinion Panel is a nationally representative probability-based panel where panel members are recruited randomly in one of two ways: (a) Through invitations mailed to respondents randomly sampled from an Address-Based Sample (ABS) provided by Marketing Systems Groups (MSG) through the U.S. Postal Service’s Computerized Delivery Sequence (CDS); (b) from a dual-frame random digit dial (RDD) sample provided by MSG. For the online panel component, invitations were sent to panel members by email followed by up to three reminder emails.

Another 320 (n=11 in Spanish) adults were reached through random digit dial telephone sample of prepaid cell phone numbers obtained through MSG. Phone numbers used for the prepaid cell phone component were randomly generated from a cell phone sampling frame with disproportionate stratification aimed at reaching Hispanic and non-Hispanic Black respondents. Stratification was based on incidence of the race/ethnicity groups within each frame. Among this prepaid cell phone component, 140 were interviewed by phone and 180 were invited to the web survey via short message service (SMS).

Respondents in the prepaid cell phone sample who were interviewed by phone received a $15 incentive via a check received by mail or an electronic gift card incentive. Respondents in the prepaid cell phone sample reached via SMS received a $10 electronic gift card incentive. SSRS Opinion Panel respondents received a $5 electronic gift card incentive (some harder-to-reach groups received a $10 electronic gift card). In order to ensure data quality, cases were removed if they failed two or more quality checks: (1) attention check questions in the online version of the questionnaire, (2) had over 30% item non-response, or (3) had a length less than one quarter of the mean length by mode. Based on this criterion, no cases were removed.

The combined cell phone and panel samples were weighted to match the sample’s demographics to the national U.S. adult population using data from the Census Bureau’s 2024 Current Population Survey (CPS), September 2023 Volunteering and Civic Life Supplement data from the CPS, and the 2025 KFF Benchmarking Survey with ABS and prepaid cell phone samples. The demographic variables included in weighting for the general population sample are gender, age, education, race/ethnicity, region, civic engagement, frequency of internet use and political party identification. The weights account for differences in the probability of selection for each sample type (prepaid cell phone and panel). This includes adjustment for the sample design and geographic stratification of the cell phone sample, within household probability of selection, and the design of the panel-recruitment procedure. Initial coding for open-ended questions was done using BT Insights AI Platform and then reviewed, edited, and finalized by KFF researchers.

The margin of sampling error including the design effect for the full sample is plus or minus 3 percentage points. Numbers of respondents and margins of sampling error for key subgroups are shown in the table below. For results based on other subgroups, the margin of sampling error may be higher. Sample sizes and margins of sampling error for other subgroups are available on request. Sampling error is only one of many potential sources of error and there may be other unmeasured error in this or any other public opinion poll. KFF public opinion and survey research is a charter member of the Transparency Initiative of the American Association for Public Opinion Research.

GroupN (unweighted)M.O.S.E.
Total1,343± 3 percentage points
Registered voters1,107± 4 percentage points
Party ID  
Democrats420± 6 percentage points
Independents450± 6 percentage points
Republicans372± 6 percentage points
MAGA Republicans/Republican leaning independents326± 7 percentage points
MAHA Supporting Voters  
Voters who support the MAHA movement504± 6 percentage points
Voters who do not support the MAHA movement595± 5 percentage points
News Release

MAHA Health Concerns Resonate Broadly but Lag Behind Health Care Costs Even for MAHA Voters

MAHA Voters Are More Supportive Than Other Voters of the Trump Administration's Food and Vaccine Policy Though Less Than 1 in 3 "Strongly Approve"

Published: May 6, 2026

Chemical food additive and pesticide concerns associated with the Make America Health Again (MAHA) movement are shared broadly across the public. But when it comes to voters, health care costs are a higher priority and bigger motivator, even among MAHA supporters, a new KFF Health Tracking Poll finds.

When asked to identify their most important health priority for government to address, far more MAHA-supporting voters identify lowering the cost of health care (42%) than other issues more closely associated with the movement, such as restricting the use of chemical additives in the food supply (21%), reevaluating the safety of vaccines (10%), limiting corporate influence on food policy (8%), or restricting the use of pesticides in agriculture (8%).

At least half of MAHA voters also say that the cost of health care will have a “major impact” on their decision to vote (51%) and which party’s candidate they will support (56%) in the upcoming midterms. That’s more than say the same about vaccine policy (36% say it will impact their decision to vote, 40% say it will impact which candidate they will support) or food safety (43% say it will impact their decision to vote, 45% say it will impact which candidate they will support), two issues closely aligned with MAHA, a movement promoted by the Trump administration and by Health and Human Services (HHS) Secretary Robert F. Kennedy Jr.

Overall, about four in ten (41%) adults—and a similar share of voters (43%)—say they are supporters of the MAHA movement, with support closely tied to partisanship and support of President Trump’s Make America Great Again (MAGA) movement.

Among voters who support MAHA, about half (52%) identify as Republicans, 29% identify as independents, and about one in seven (15%) identify as Democrats. A majority (56%) of MAHA voters identify as Republican or Republican-leaning and support the MAGA movement. The pattern of prioritizing health costs ahead of other MAHA issues is consistent across these partisan subgroups.

Other MAHA Health Concerns Are Shared by the Broader Public
Majorities of the public say there is not enough regulation of chemical additives in food (75%) or of pesticides used in agriculture (64%)—including majorities across partisans, among MAHA supporters, and those who do not support the movement.

Most of the public—across partisans and MAHA supporters—also share a distrust of federal health agencies and food and drug industries:

  • Four in ten or fewer adults say they have at least some confidence in the U.S. Centers for Disease Control and Prevention (CDC: 40%), the U.S. Food and Drug Administration (FDA: 36%), or the U.S. Environmental Protection Agency (EPA: 36%) to act independently without interference from outside interests. Democrats are more likely than Republican or independent adults to trust the CDC, but partisan differences largely disappear for the FDA and EPA.
  • Less than half of U.S. adults trust agricultural companies (40%), food and beverage companies (25%), or pharmaceutical companies (21%) to act in the public’s best interest. Low levels of trust in these industries are also present across partisans.  

About 1 in 3 MAHA Voters Strongly Approve of HHS Secretary Kennedy’s Job Performance  
MAHA voters approve more than other voters of how the Trump administration is handling food policy, including chemical additives and pesticides (72% vs. 27%), and vaccine policy (67% vs. 17%), as well as how HHS Secretary Robert F. Kennedy Jr. is handling his job (69% vs. 17%). However, less than one-third of MAHA voters “strongly approve” of the Trump administration on food policy (32%) and vaccine policy (29%) and of the HHS Secretary’s handling of his job (32%)—while similar shares of MAHA voters disapprove in all three areas. This is a fairly tepid rating for a group that aligns with Kennedy’s signature movement.

Overall, voters are evenly divided on whether they trust Democrats (31%) or Republicans (27%) to handle the safety of food additives and pesticides, and a similar share (31%) gives neither party the advantage on this issue. Meanwhile, the Democratic Party holds the advantage over the Republican Party in who voters trust to handle vaccine policy (41% vs. 25%) and ensure that federal health agencies act independently without corporate influence (33% vs. 24%).

Designed and analyzed by public opinion researchers at KFF, this survey was conducted April 14-19, 2026, online and by telephone among a nationally representative sample of 1,343 U.S. adults in English and in Spanish. The margin of sampling error is plus or minus three percentage points for the full sample. For results based on other subgroups, the margin of sampling error may be higher.