In this interview from theCUBE + NYSE Wired: MedTech Unplugged, Sap Sinha of Panoramic Health joins Doug Pickett of Adonis to talk with theCUBE + NYSE Wired's Gemma Allen about how AI is reshaping both clinical care and the healthcare revenue cycle. Sinha explains how AI is lifting administrative burden off physicians, with ambient AI summarizing patient encounters so clinicians can return to direct care rather than charting. Pickett highlights how Adonis helps provider teams move from reactive dashboards to real-time trend detection, filling gaps left by legacy EHR platforms like Epic and Athena. Both note a reversal in adoption patterns, with frontline doctors and operators now driving AI demand rather than traditional IT leaders.
The conversation also explores the regulatory gray area forming around agentic AI, as insurers could easily bar automated negotiation while providers push toward autonomous claims resolution. Sinha points to a recent debate over a major insurer's $1 billion increase in payouts as evidence that AI is often correcting, not inflating, what should already be paid. Pickett details how Adonis trains its models exclusively on de-identified data to sidestep the PHI risks of shadow AI use inside hospitals. Sinha also addresses the rural care gap, noting AI's potential to extend specialist-level expertise to underserved regions where patients may travel hundreds of miles for treatment. Pointing to radiology's unexpected growth despite early predictions of disruption, Pickett argues AI will expand rather than replace specialties like nephrology. Both predict a full convergence of AI and healthcare within the next decade, offering a grounded view of how automation can restore humanity to patient care while closing the industry's workforce gap.
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Sap Sinha, Panoramic Health & Doug Pickett, Adonis
In this interview from theCUBE + NYSE Wired: MedTech Unplugged, Sap Sinha of Panoramic Health joins Doug Pickett of Adonis to talk with theCUBE + NYSE Wired's Gemma Allen about how AI is reshaping both clinical care and the healthcare revenue cycle. Sinha explains how AI is lifting administrative burden off physicians, with ambient AI summarizing patient encounters so clinicians can return to direct care rather than charting. Pickett highlights how Adonis helps provider teams move from reactive dashboards to real-time trend detection, filling gaps left by legacy EHR platforms like Epic and Athena. Both note a reversal in adoption patterns, with frontline doctors and operators now driving AI demand rather than traditional IT leaders.
The conversation also explores the regulatory gray area forming around agentic AI, as insurers could easily bar automated negotiation while providers push toward autonomous claims resolution. Sinha points to a recent debate over a major insurer's $1 billion increase in payouts as evidence that AI is often correcting, not inflating, what should already be paid. Pickett details how Adonis trains its models exclusively on de-identified data to sidestep the PHI risks of shadow AI use inside hospitals. Sinha also addresses the rural care gap, noting AI's potential to extend specialist-level expertise to underserved regions where patients may travel hundreds of miles for treatment. Pointing to radiology's unexpected growth despite early predictions of disruption, Pickett argues AI will expand rather than replace specialties like nephrology. Both predict a full convergence of AI and healthcare within the next decade, offering a grounded view of how automation can restore humanity to patient care while closing the industry's workforce gap.
Sap Sinha, Panoramic Health & Doug Pickett, Adonis
Sap Sinha
CEOPanoramic Health
Doug Pickett
Chief Revenue OfficerAdonis
In this interview from theCUBE + NYSE Wired: MedTech Unplugged, Sap Sinha of Panoramic Health joins Doug Pickett of Adonis to talk with theCUBE + NYSE Wired's Gemma Allen about how AI is reshaping both clinical care and the healthcare revenue cycle. Sinha explains how AI is lifting administrative burden off physicians, with ambient AI summarizing patient encounters so clinicians can return to direct care rather than charting. Pickett highlights how Adonis helps provider teams move from reactive dashboards to real-time trend detection, filling gaps left by leg...Read more
exploreKeep Exploring
How true is it that AI is disrupting the healthcare industry in terms of efficiency, cost, and innovation?add
How are legacy electronic health record (EHR) vendors approaching AI, and where do you see opportunities for other companies to fill gaps created by their AI roadmaps?add
What is AI allowing teams to do, and what initial short-term impacts are you seeing?add
How quickly can a technology like yours be deployed in healthcare systems (e.g., Athena, Epic), and how soon can clients realize ROI?add
What reluctance or skepticism do hospitals and providers have about using LLMs/AI in clinical workflows, and where are the main risks?add
Sap Sinha, Panoramic Health & Doug Pickett, Adonis
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(INTRO)
Gemma Allen
>> Welcome back to the CUBE studio here at the New York Stock Exchange. I'm Gemma Allen, co-host of NYSE Wired's MedTech Unplugged. And now we're going to talk about how healthcare has a strange two-sided problem. On the one side, you have the doctors actually delivering care, and on the other, you have an incredibly complicated machine responsible for getting that care paid. And increasingly, AI is being inserted into both sides of that fight. Today, we're looking at those two worlds from very different angles. Adonis, which is using AI to tackle the healthcare revenue cycle, and Panoramic Health, which is working with nephrologists to transform how kidney care is delivered and managed. Joining me now in conversation on exactly that is Doug Pickett from Adonis and Sap Sinha from Panoramic. Welcome, guys.
Doug Pickett
>> Thanks for having us, Gemma.
Gemma Allen
>> I'm sorry that I can't quite say nephrologist. I'm sure you're an expert at it, but okay, so let's kick off and let's start with an easy question for you, Sap. So we talk a lot about AI, right? AI can solve all our problems. AI is the magical solution to the future. But in the healthcare industry, we know there's a lot of legacy technology, a lot of contracts, and a lot of tech vendors that have been in place for a very long time and for a justifiable reason, right?
Sap Sinha
>> Yeah.
Gemma Allen
>> How true do you think it is that AI is truly disrupting this world of healthcare from the perspective of efficiency, cost, innovation?
Sap Sinha
>> Yeah, I think you are— it depends on what kind of EMR operating system, everything you have. I think the larger EMRs are— if you think about them as an operating system for running a practice, they are recognizing that their customers, which are clinicians and patients and people who are running the business, are demanding that it has to be more open so that we can use technology in a differentiated way. So wherever there is an EMR which is open to that, then you are seeing a lot of change which is happening. I think where things are going, we have a lot of administrative tasks for our physicians. So physicians have been made into data operators in our healthcare system. They go, you go into a doctor's office, they're sitting looking at charting and all that, instead of talking to the patients. So AI has the potential of changing that. Second piece of it is, Unfortunately, because we have access to our doctors through our portals and everything, we send them messages, we have prescriptions to be refilled. So then after they see the patients, all they do is answer these things. And that has a potential for AI to be used because they have to go into a chart, look at the patient, think about this. An AI can summarize a lot of the things so they can move faster through that. Because what is unfortunate in the healthcare system, our physicians and clinicians are overburdened. With all these administrative tasks beyond seeing the patient. So there is a lot of potential right now which is being seen in how do we remove this administrative burden so that we can bring back the humanity of healthcare, that is physicians talking to the patient. And so that's where a lot of technology and AI are there, and there is a lot of potential of doing that slowly and steadily, and there's a lot of testing and piloting that has to be done and also making the physicians not extra burdened. Now that AI is— it's not a productivity angle, it's how do you kind of reduce the burden from them.
Gemma Allen
>> So on opportunity, let's talk about Adonis for a second and let's level set, right? We all know in the world of healthcare and some of these traditional industries, folks are used to leveraging IT or using IT when something is broken, right? Like your typical experience as a nurse or as anybody in a hospital is probably like, oh God I've got to ring the help desk, right? That dreaded moment. Now we're in a space where everyone is a technologist, everyone can be somewhat of an innovator, and people can bring ideas and hopefully create some sort of a technical workaround to make those ideas possible. Yeah, there's still a lot of legacy tech here though. You've got companies like Epic, right? Companies like Cerner that are very difficult to quite frankly disrupt. Talk a little bit about what you're seeing from the perspective of those inherited legacy tech solutions and how easy it is to convince buyers and convince folks across hospitals that, listen, there's a vertical play here, it's AI native, and here is how your team can get value from it quickly. What happens in those conversations?
Doug Pickett
>> Yeah, I think many people in the industry are familiar with the innovator's dilemma, right? And there is— I think that's on the mind of a lot of the legacy EHRs, electronic health record systems, Epic, Athena, other large ones around. I don't think that they're— they're not standing still, right? They are definitely pushing forward an AI roadmap. It's very expansive, it's innovative, and it's making real impacts, right? Where we see opportunity is to still fill in the gaps that are created through this very expansive AI roadmap. So, you think about what an EHR, electronic health record system, would go after first, it's likely the clinical workflows, right? It's leading into revenue cycle and providing impact there, but it's likely starting a little more upstream in the claims lifecycle or patient lifecycle. And so, one of the big key areas that we're seeing changes, I would say, in a buying need is historically you've been able to track performance, right, through dashboards, through a very highly skilled and highly aware analyst team, right? And then been able to maybe take direction from an executive like Sap or one of his team members to say, hey, we think we're seeing this trend. What should we do about it? Let's go identify it. Let's go do something. And let's organize around that. But it's a hunch that then needs to be supported by data. What AI is allowing teams to do and what Adonis is focused on is helping teams identify issues as they're happening and then come up with a recommendation on what you should do, which could include automations, but it also could include, hey, you need to fix an upstream issue around a coding issue or a provider credentialing issue, but starting to identify it before it turns into a larger issue is where we're seeing a lot of the initial short-term impact of AI. And it's around analysis and around short-circuiting the time that otherwise it would take to identify a growing trend.
Gemma Allen
>> And if we think about the shifting executive mindset for a second, right, because like you said, people aren't sitting still. It's not like Epic or Cerner or any of these folks are just like, we'll just wait to see what happens here. They're actually building plugins and accelerators too, right? But we know that now the conversation around what should our tech roadmap look like at an executive level of any hospital, that that decision-making function is increasing, right? Because AI is suddenly putting a huge amount of focus on this. And if we think about the world of cloud, like when people were competing for the Microsoft stack or the Google stack, again, it was the same challenge in some respects, right? You don't want vendor lock-in, but it's an easy play to just continue what you have.
Sap Sinha
>> Yeah.
Gemma Allen
>> So what do you think is happening or what do you think would need to happen for one of your technology leaders to come to you, Sap, and say, we need to just completely rethink this. We need to really think vertically here. Think about an application that can just solve for a lot of problems. How quickly do you think you'd say, okay, yeah, I can see why you would make that call or make that purchasing decision?
Sap Sinha
>> Yeah, I think AI is one of the weirdest technology shifts which I have seen, is that it's not the technology leaders. It's the operating team, the people who are actually working on the front line, people including doctors who are hearing from their friendshow easy it is to do certain things. And the growth of ambient AI has been very much a focus where people are now back to having conversations and things are getting written from an ambient AI perspective. So interestingly enough, it's the teams that are coming in saying listen, I can do this better. I can do it better with AI and then forcing the CIOs of the world and the executives of the world to think about how do you redo your workflows, redo your org design, and then bring in technology whereas in the digital transformation of the late 2000s or the tech IT, the internet transformation, it was the tech experts. But AI is so intuitive. For many people, right? As you look at the new LLMs, etc., that people are experimenting on their own and saying, oh, I can do this better. I need this to be enabled. So there's a lot of groundswell leadership push rather than this tech, some techie and geeky person—
Gemma Allen
>> abstract—
Sap Sinha
>> who is abstract, who's coming in saying, oh, you need to use this and I'll be doing that. And that's that intuitive, that language aspect of it does make it easier from an implementation perspective. Because I have never seen anything get implemented with doctors faster than some of the technology which I've seen.
Doug Pickett
>> OpenEvidence.
Sap Sinha
>> Yeah, like OpenEvidence, a lot of the other things where if you look at the history of EMRs, it took a long time to get people onto EMRs, but physicians are absolutely using AI, not using obviously PHI and everything, but looking at, okay, I have to treat this patient, these are the complications, what are the 3, 4 different ways I could look into it? And they are experimenting and looking at, oh, what are the latest drugs? So from their angle, because they want to be better, they want to take care of their patients, and this is enabling them to do that. So it's a very interesting kind of mindset shift where teams are asking rather than the tech people are asking.
Gemma Allen
>> And from your perspective, that's opportunistic, right? Because if we think about it, a lot of the healthcare industry and the banking industry didn't actually go to the cloud. Yeah, a lot of them never really made it there. Right. So it's a very different buyer's market. Talk a little bit about, you go in, you prove a level of, I guess, immediate efficiency with a technology like this or some level of ROI. And then again, it comes down to really if you can deploy it. But how quickly can you plug and play a technology like yours and how quickly can you realize ROI on it?
Doug Pickett
>> Yeah, no, it's a great question. financial impact for the client side is always top of mind, especially now when you have all types of possibilities at your fingertips and your operating teams can use Copilot, if you're talking about the Teams which is prevalent in the healthcare industry or Google Gemini or other types of LLMs. One of the things obviously Adonis does is we're gated behind a business associate agreement and your HIPAA regulations. And so what that requires us to do is go through a more typical B2B kind of sales process with executive buyers on the healthcare provider side. So it's a longer buying process than maybe like your just LLM adoption rate would allow you to do. But what that allows you to do is then access a greater dataset that enables your team on the provider side to actually think about trend analysis, think about where to apply automations, and then rapidly put those to work to drive that client financial impact. In terms of implementation time post-contract, we're typically depending on the system for systems that are on, or groups that are on, like Athena, which is the system that Sap's team is on. Typically that's around 30 days. We go live. So we have a direct database connection and we have a long history of client base on that system. For Epic, typically you have a little bit longer. It's around 6 to 8 weeks that we're seeing the initial go-live. And depending on the client's needs and challenges and ROI model that supports it, we're either layering in our intelligence platform, which is a data analytics kind of alerting platform, or the agents first to automate some of the voice AI needs, calling payers on their behalf or going out to payer portals and collecting or submitting information on their behalf.
Gemma Allen
>> So if we stay on your product for a second.
Sap Sinha
>> Yeah.
Gemma Allen
>> What it really is doing, right, is it's prioritizing where time should be spent on particular types of payments that might have been denied or claims that are sitting there for a very long time, etc. So it's creating a new level of efficacy in the overall workflow for, FP&A at hospitals.
Doug Pickett
>> Yep.
Gemma Allen
>> But in the agentic, on the tech side of that, it's like this kind of two-sided economy of agents, right? Because you're developing agents that do a certain level of arguing or, again, like reward-driven activity, and so are insurers, right? So those two things are happening at the same time. How close are you or are we as an industry to having a fully agentic, healthcare system arguing with insurer level scenario for at least a percentage of those, outstanding claims? Do you guys think and how much do you think that's spin versus, real world activity?
Sap Sinha
>> I think the rules have to be defined right now. Yes, there is agentic use in the provider side, there's agentic use in the insurance side, but insurance could easily put it in a contract saying you cannot use agentic AI to have a discussion with us. Right. Which— so it can completely change the playing field. So I think there has to be a more kind of architecture and a rule base across the board, where it can be used, etc. Also, the way documentation works, what is required for certain billing codes, all those elements, they are not fit for the AI era, right? Where now I was talking about Ambient AI, you can basically record the whole conversation and have every detail of that conversation from a patient care perspective into the note. Which makes the notes extremely detailed, which can, from a submission and claim perspective, could have a certain different coding style. You could identify certain diagnosis, et cetera, right? So the insurance company will have to pay for it from the way things are defined from a rules perspective. So there is a lot of who creates the rule, what the rules are, Right now everyone's seeing this AI transformation, but there's a structural question which people need to ask. For example, there was a recent article that Blue Cross Blue Shield Association said that, oh, they had to pay about $1 billion more because of AI, which is the wrong thing to say. Basically, now they are paying the appropriate thing for the patient, which they didn't have to pay. So if you look at it from whose perspective It's somewhat different. So we have to somewhat— this technology is moving so fast, but the rules are not catching up.
Gemma Allen
>> And when we think about the world of LLMs, shadow AI, which we hear about a lot, folks taking their work home and maybe running it through an LLM or whatever, asking for a friend here, whatever might be happening. We know that healthcare, it's a very regulated space, right? And data retention is a huge challenge, I'm sure. So it means that some of these conversations that circulate that, ChatGPT or Claude is just going to do everything for everyone, 10 years from now, it's probably quite unrealistic when we think about it from the perspective of healthcare. I'm interested to understand from you, Doug, what sorts of, I guess, reluctance or skepticism do you see and hear? Where is the real risk parameter?
Doug Pickett
>> On the provider side?
Gemma Allen
>> Yeah, on the hospital side.
Doug Pickett
>> Yeah.
Gemma Allen
>> When you think about how it's being used within
Doug Pickett
>> workflows.Yeah, first and foremost, at Adonis, when we're building models, when we're deploying models outside of LLMs, we're not using PHI to train models, right? That's common in the
Gemma Allen
>> industry.Big issue. And for those who are, right?
Doug Pickett
>> What'd you
Gemma Allen
>> say?It's a big issue that folks are,
Doug Pickett
>> right?It is. Yeah. And sometimes folks are doing it out of kind of policy, if you will, at provider orgs, because they're just doing it to run a quick check, not exposing the patient, but maybe exposing a piece of data that would be considered PHI in a normal context. So how we're training the models is we're de-identifying everything and then de-identifying all data, all sensitive data, and then running it through a process to then get to the end result, to actually get to a trend or get to a payer behavior set or a payer trend that then we can use to actually inform an automation that we want to do or a recommendation back to the provider side. To Sap's point, we're seeing a lot of AI on AI communications, especially on the voice side, as well as ongoing what we on the hospital front call managed care. The idea is payer negotiations are starting to be inclusive of, hey, you have to expose information at a certain level. Do you do it through voice AI? Do you put that in a contract or do you open up a new API? From the payer side to actually give the information in the appropriate way to the provider. At the end of the day, healthcare is heavily regulated, but it's controlled by contracts, right, between networks and providers for the most part. What that is, is sometimes up for interpretation, and the interpretation is maybe more difficult on either side. Of what a true fee is and what should be paid. To Sap's point around, hey, if you're charging and coding in the appropriate way based on what the contract says, maybe your revenue goes up, but maybe it was always supposed to be there. Yeah. And that's the part that we're very early in the cycle, like what the effect is on contract, what the effect is on payer negotiations. I'm really optimistic that that'll hopefully reduce some of the friction over the next 3 to 5 years.
Gemma Allen
>> Okay, so to close, I want to ask you guys both the same question. We in tech always talk about convergence, right? About mass integration. The person whose product or workflow touches the customers' lives the most wins the game, right? It's the Google world or the Microsoft world and now the Anthropic or the OpenAI world. From the perspective of healthcare, it's not really the case though, right? Because those systems, a lot of people still have desktops, right, in their lives in this industry, which is different to many others. What are your thoughts from the perspective of whether or not you think there will be some level of convergence for technology and healthcare in this next frontier of AI? Or do you expect that there will be a certain amount of segregation or kind of siloed activity and folks will build brilliant products that are vertically integrated, you know, for the foreseeable? Where do you see it headed?
Sap Sinha
>> I think First of all, healthcare provider groups and everything take PHI very seriously. So a lot of it is de-identified from that perspective, and people are creating these large data warehouses where they are running their experiments. And it doesn't matter if you are a large organization. I don't— I think most of the large organizations are really experimenting. I think healthcare has to converge towards AI just because we don't We have a huge problem right now where we don't have enough people to serve in every aspect of healthcare. We don't have enough doctors, we don't have enough nurses, we don't have, uh, for the— to support the population. our healthcare system is maybe designed for 200 million people, but we are 350 million people. So we have a severe shortage. And, you are— we are in New York City sitting here, a lot more access. But now think about rural healthcare, right? In certain places in Idaho, Wisconsin, certain parts of Wyoming, you don't— you can't find doctors for 300 miles, right? So AI does create this opportunity of can you take healthcare with the human aspect of it? Because I don't think AI is a magic panacea where patients will talk to an AI and we will get— because health— healthcare is so personal that you have to talk to a human, right? Unless you get that reassurance, you'll not be able to do that. But AI can enable that conversation in a differentiated way which has more data, etc., so that we can collect the data and create better diagnoses and treat patients and move away from what our healthcare system in many cases is a sick care system to a true healthcare system where we are actually caring for patients better and moving them into a better life. And I think there has to be a convergence where AI and healthcare more and more across the board will converge. And it won't happen in the next 2 to 3 years. I think people are discounting how complicated healthcare is, but in the next 10 years I could see that.
Gemma Allen
>> And that's so important, right? That people discount how complicated these industries are, and it happens all of the time. It happens in every industry, right? Banking too, and many others. But healthcare is so unique in that sense. What are your thoughts, Doug, in terms of where this industry is headed broadly? Do you think we're going to live in a fully AI-native world 10 years from now? How realistic do you think that is?
Doug Pickett
>> I don't know. Humans are very bad at forecasting. But I think if you look at— just take radiology, for example, right? 5 years ago, 6 years ago, pre-COVID, folks were forecasting, hey, radiology is going to be greatly affected in a negative way for a radiologist. And like what you've seen is almost exactly the opposite, right? The industry, that specialty as a whole has just kind of taken off and they've allowed that radiologist, partly due to a shortage, but also partly due to being able to be enabled by AI, by diagnostic tools, to allow them to kind of go deeper in their profession and also stretch. And I think that you're seeing that across other specialties such as nephrology. Sap and I on the way here, were talking about all the different changes that he's seeing and why he's excited by the specialty in that field coming from the GI world. And what Adonis is doing obviously is we're sitting more on the administrative side. But let's say 18 months ago, we got a lot of focus on, hey, should we be focusing on cost reduction? And now that focus is— it's still there because like there's still potential hope that, hey, you can drive out cost to collect by pushing automation. But on the other side, it's like, how can we increase yield, right? How can we increase our revenue? How can we increase our scope? And that's the part that most of our clients now are starting to more shift towards and think about because it's one, it's more positive as a whole. It's also thinking about like, how can we increase the sum of the parts and how can we use AI to enable the entire administrative stack, including the executive team? And that's where I'm seeing a lot of positive changes and evolution starting to occur, through that heightened focus on that area, I would say.
Gemma Allen
>> Well, I think we all want a world where we spend less time with heart attacks when we see letters come through the door from insurers about a visit to a hospital like I shared with you guys earlier. And I just think more efficiency, right? And again, just better treatment for Americans overall. So, folks, thank you so much for joining us on NYSE Wired.
Doug Pickett
>> Thank you so much.
Sap Sinha
>> Thank you.
Gemma Allen
>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is NYSE Wired's MedTech Unplugged. Thanks for watching.
Gemma Allen