Podcasts about prior authorization

Health systems are no longer asking whether to deploy ambient AI. They are asking how to scale it across roles, connect it to revenue cycle, and avoid building another layer of fragmented tools. Kenneth Harper, General Manager of Dragon at Microsoft, joins Megan Antonelli to unpack what separates the organizations actually moving the needle from those stuck in pilot mode, what ambient AI's expansion from physicians to nurses and radiologists has revealed about clinical workflow design, and how CIOs should think about building a coherent clinical AI architecture in 2026. Kenneth Harper, GM, Dragon Product, Microsoft Megan Antonelli, Chief Executive Officer, HealthIMPACT Live

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In this episode of The Dish on Health IT, Tony Schueth, CEO of Point-of-Care Partners (POCP), welcomes Pooja Babbrah, Executive Vice President of Strategy and Industry Alignment at NCPDP, and Anna Taylor, Associate Vice President of Population Health and Value-Based Care at MultiCare Health System and Steering Committee member of the HL7 Da Vinci Project, for a discussion on the relationship between standards development and policymaking.  Using the CMS “Interoperability Standards and Prior Authorization for Drugs” Proposed Rule (CMS-0062-P) as a backdrop, the conversation explores how standards communities, implementation accelerators, pilot programs, and industry collaboration influence healthcare interoperability long before requirements appear in federal regulations. Tony opens the discussion by asking how organizations should think about the relationship between standards development and policymaking today. Pooja and Anna explain that organizations such as the HL7 Da Vinci Project and NCPDP Standards are often viewed as technical standards bodies, when in reality they serve as collaborative forums where providers, payers, vendors, pharmacists, regulators, and other stakeholders work through real-world operational challenges. The conversation then shifts to the value of participating early. Tony asks what organizations miss when they wait for final rules before becoming involved. Anna discusses the operational, strategic, and financial advantages organizations can gain by participating in standards development activities, implementation guide development, pilots, testing events, and implementation communities. As part of that discussion, Tony and Anna touch on the growing body of production implementations supported by Da Vinci. Organizations interested in understanding how these implementation guides are being deployed across the industry can explore the Da Vinci In-Action Implementation Tracker, which documents real-world adoption efforts and implementation progress. Pooja expands on the importance of creating opportunities for broader industry participation. She describes NCPDP Collab, an interactive forum open to both members and non-members that provides a venue for discussing workflow challenges, implementation barriers, and emerging industry needs before formal standards development begins. The discussion naturally progresses into the CMS “Interoperability Standards and Prior Authorization for Drugs” Proposed Rule (CMS-0062-P), which directly references standards and implementation approaches developed by both NCPDP and Da Vinci. As Tony guides the conversation toward implementation, Anna discusses how Da Vinci's collaborative testing model and initiatives such as Trebuchet help organizations evaluate interoperability workflows in real-world settings before widespread adoption. The discussion then turns to one of the central themes of CMS-0062-P: the convergence of pharmacy and medical benefit workflows. Pooja explains that while patients and providers simply want access to treatment, healthcare organizations continue to operate within separate medical and pharmacy benefit structures. She argues that future interoperability efforts must focus less on the underlying standards and more on creating workflows that deliver a seamless experience for providers and patients regardless of where coverage resides. Building on that theme, Tony asks how healthcare organizations should think differently about workflow design. Drawing on her background in human factors engineering, Anna argues that healthcare has historically allowed technology to dictate workflows rather than designing technology around how people actually work. She advocates for starting with desired outcomes and user experience, then working backward to determine how standards, automation, and technology can support those goals. The conversation then moves to trust, adoption, and data quality. Tony observes that interoperability is no longer simply about moving data but about delivering the right information at the right time and within the right workflow. Anna discusses the importance of consistency and reliability in building trust, while Pooja shares examples of how incomplete implementations can undermine provider confidence even when standards and technology are technically available. Together, they argue that adoption depends as much on usability and trust as it does on technical capability. Returning to CMS-0062-P, Tony asks where organizations should focus their feedback beyond timelines and compliance concerns. Both guests encourage stakeholders to look closely at the broader strategic questions embedded throughout the proposed rule, particularly the requests for information that may signal future policy priorities. Rather than focusing solely on implementation challenges, they encourage organizations to use the comment process as an opportunity to help shape how healthcare workflows should function in the future. The episode concludes with Tony's signature question: what should healthcare stakeholders think differently about or start doing differently tomorrow? Pooja highlights the expanding role pharmacists can play in care coordination, medication management, and prior authorization workflows, arguing that pharmacists remain an underutilized resource within the healthcare ecosystem. Anna closes with a call for broader participation across healthcare, encouraging providers, employers, patients, vendors, and other stakeholders to engage with standards communities and implementation efforts. She emphasizes that meaningful progress happens when stakeholders move beyond identifying problems and actively participate in building solutions. Throughout the discussion, Tony reinforces a central theme: the future of healthcare interoperability is not being shaped solely through regulation. It is shaped through the collaboration, testing, implementation, and problem-solving taking place every day within standards organizations, implementation accelerators, pilot programs, and stakeholder communities. Organizations that want to influence the future of healthcare should not wait for final rules to arrive. They should participate in the conversations that help create them.  

In this episode of Revenue Cycle Optimized, Christina Harkins, Director of Customer Success at Infinx, discusses how prior authorization workflows can be optimized through automation, custom queues, workflow discovery, and ongoing operational support. She explains how the right mix of AI, human oversight, reporting, and implementation planning can reduce manual work, prevent avoidable denials, and improve revenue cycle performance.

Send us Fan MailAmerican hospitals now spend nearly $2 on administrative overhead for every dollar that touches direct patient care. Insurers earn billions in float by sitting on claims for weeks, providers borrow money just to stay liquid, and patients open bills for visits they barely remember.Don Peterson, Founder and CEO of PIM Health, joins host David E. Williams to discuss why healthcare's payment system is working exactly as it was designed to work, and how real-time claims adjudication at the point of care could eliminate prior authorization as it currently exists, cut administrative overhead from 12 to 15 percent down to 2 to 3 percent, and return hundreds of billions of dollars in waste back to patients, providers, and plan sponsors.

Smoother Schedules Start With Smarter Prior Authorization Lora Pada, VP of Customer Success at Infinx, host a practical conversation with Danelle Newman, Director of Patient Access at OSS Health, on how orthopedic practices can take a smarter approach to prior authorization workflows. The discussion will focus on the real operational grind patient access teams face every day, including payer complexity, manual follow-up, documentation gaps, scheduling pressure, and the challenge of keeping patients moving without overwhelming staff. Find all of our network podcasts on your favorite podcast platforms and be sure to subscribe and like us. Learn more at www.healthcarenowradio.com/listen/

Prior authorization and eligibility verification remain too variable, payer-specific, and operationally complex to rely on automation alone. In this episode, David Byrd and Navaneeth Nair explain how Infinx Patient Access Plus orchestrates AI agents, automation, payer connectivity, analytics, and human expertise to reduce administrative burden and keep care moving.

Special discounts up for AIE Melbourne (LS discount) and AIE World's Fair (group discounts up to 25% - CFPs still open for Autoresearch and Vertical AI) Cya there!Abridge did not start as an “GPT wrapper”. It was founded in 2018, years before the Cambrian explosion of AI application layer companies. OpenAI launched ChatGPT publicly on November 30, 2022 and by then, Abridge had already spent years doing the unglamorous work of building trust for one of the highest context, most important workflows in healthcare: the conversation between a patient and a clinician.Abridge's original wedge was clinical documentation. Listen to the visit, generate the note, reduce the clerical burden, and let clinicians spend more time with patients instead of the EHR. By focusing on how doctors actually document, how health systems actually buy, how EHR integration actually works, how clinicians verify outputs, and how missing context during a visit turns into downstream friction across billing, prior authorization, quality, and follow-up, the adoption of LLMs became a force multiplier on a workflow already optimized for sensitive context gathering.The company has scaled fast: Abridge says it is projected to support 80M+ patient-clinician conversations this year across 250 large and complex U.S. health systems, with support for 28+ languages and 50+ specialties. It raised $300M at a $5.3B valuation in June 2025, after a $250M round earlier that year.Today, Janie Lee and Chaitanya “Chai” Asawa of Abridge join us for another crossover pod with Redpoint's Jacob Effron (who is on the board of Abridge) to dive into how Abridge is building the clinical intelligence layer for healthcare starting with ambient documentation, then expanding into clinical decision support, prior authorization, payer/provider/pharma workflows, and eventually real-time agents that act before, during, and after the patient conversation. We go inside the product, data, infra, evals, workflow, privacy, and org design choices behind bringing AI into one of the highest-stakes enterprise environments from 100M+ medical conversations and specialty-specific evals to real-time alerts, EHR integration, de-identification, clinician-scientist teams, and why healthcare may solve some of the hardest AI problems first.We discuss:* Why Abridge started with clinical documentation, “pajama time,” and saving clinicians 10–20 hours a week* The transition from ambient scribe to clinical intelligence layer: save time, save money, and save lives* Why conversations between patients and clinicians may be the most important workflow in healthcare (patient visit summary feature)* Chai's “healthcare-coded Glean” framing: context is king, but healthcare raises the stakes on safety, evals, and rollout* Why Abridge wants AI to feel like “air conditioning”: always in the background, but only interrupting when it truly matters* The prior authorization example: turning a denied MRI weeks later into real-time guidance while the patient is still in the room* Why payer policies, EHR data, medical literature, and hospital-specific guidelines make the problem hard, and also create the moat* How Abridge thinks about ambient form factors: mobile, desktop, in-room devices, nursing workflows, multimodality, and future AR* The multi-sided healthcare customer: CMIOs, CFOs, CIOs, clinicians, patients, payers, and pharma* The hardest AI problem at Abridge: high-quality, low-latency, low-cost real-time support in a high-stakes clinical setting* When Abridge uses frontier models vs proprietary models, and why its unique data from medical conversations matters* Why “every agent is a coding agent underneath,” and how the EHR can be thought of as a filesystem for healthcare agents* How Abridge approaches personalization across individual doctors, specialties, and health systems* Why “AI slop” is AI without context, and how edits, memories, and clinician preferences create a data flywheel* Abridge's eval stack: LFDs, LLM judges, in-house clinicians, third-party evaluators, specialty-specific evals, and progressive rollout* HIPAA, PHI, de-identification, one-way anonymization, customer contracts, and learning from healthcare data safely* What changes when you operate at 100M+ conversations: reliability, cost, post-training, model routing, and infrastructure optimization* Why the same clinical conversation can serve doctors, patients, payers, pharma, and future clinical-trial workflows* How Abridge works with EHRs, and why deep interoperability is table stakes for clinician adoption* Why healthcare AI has regulatory tailwinds, why 80/20 does not work here, and why high-stakes domains may drive AI forward* Why Abridge embeds “clinician scientists” into product and eval teams* What Chai learned from Glean about search, quality, and durable AI infrastructure* Why the future of AI infra may look like context layers, event-driven systems, Kafka, Temporal, sockets, CRDTs, and tools built for humans* Why Janie changed her mind on “PRDs are dead,” and why crisp written clarity matters more in complex AI products* How Abridge uses Claude Code, Cursor, and coding agents internallyAbridge:* Website: https://www.abridge.com/* X: https://x.com/AbridgeHQJanie Lee:* LinkedIn: https://www.linkedin.com/in/janiejleeChaitanya “Chai” Asawa:* LinkedIn: https://www.linkedin.com/in/casawaTimestamps00:00:00 Introduction and what Abridge does00:02:05 From ambient documentation to clinical intelligence00:04:04 Clinical decision support and context as king00:06:57 Alert fatigue, proactive intelligence, and prior authorization00:12:36 Ambient AI form factors and healthcare customers00:16:59 The hardest AI problems in healthcare00:18:26 Frontier models, proprietary data, and model strategy00:21:07 The EHR as a filesystem for agents00:24:03 Personalization, memory, and clinician preferences00:30:40 Evals, LLM judges, and progressive rollout00:36:47 HIPAA, de-identification, and privacy00:39:21 100M conversations and operating at scale00:44:10 EHR integration and the clinical intelligence layer00:46:39 Healthcare regulation, latency, and high-stakes AI00:50:11 Clinician scientists and long-tail quality00:53:04 Lessons from Glean and durable AI infrastructure00:57:03 The future of agentic healthcare workflows00:57:34 PRDs, product clarity, and building serious AI products01:03:11 AI coding tools at Abridge01:04:06 OutroTranscriptIntroduction: Abridge, Clinical Intelligence, and the Latent Space x Unsupervised Learning CrossoverSwyx [00:00:00]: Okay. This is a special crossover Latent Space Unsupervised Learning pod.Jacob [00:00:07]: Very excited to do this.Jacob [00:00:08]: At this point, we get together once a year.Swyx [00:00:10]: Once a yearJacob [00:00:11]: And this is a fun occasion to get to do it on.Swyx [00:00:13]: I really wanted to talk to Abridge but I felt very underqualified because healthcare is not something we cover very intensely. It just so happens that Redpoint's our big investors and supporters of Abridge.Jacob [00:00:27]: Anytime you want to have a portfolio company on your podcastJacob [00:00:29]: Please, by all means.Swyx [00:00:31]: So we'll introduce our guests. Chai and Janie, welcome to the pod.Janie [00:00:34]: Thanks for having us.Chai [00:00:35]: Thank you.Janie [00:00:35]: We're excited to be here.Chai [00:00:36]: Thank you.Swyx [00:00:36]: So for listeners, what do you guys do, just to situate you guys in the company?Janie [00:00:42]: Abridge is a clinical intelligence layer for health systems. We really started with documentation and building for clinicians and as we think about reducing the burden that clinicians have, they're spending 10 to 20 hours a week on documentation. There's a massive doctor shortage in the country. We also think that conversations between patients and clinicians are probably the most important workflow in healthcare. It's where care is given and received but if you think about the 20% of our GDP that goes towards healthcare, almost everything is a derivative of that conversation, whether it's the claim, the payment, the actual diagnosis given, the treatment. And we've started with a conversation to reduce the burden for doctors on documentation but we're really excited about the path ahead as we become this broader clinical intelligence layer.Chai [00:01:34]: I'm Chai. I work on clinical decision support at Abridge.Swyx [00:01:37]: Yes.Chai [00:01:37]: And so as Janie said, we're uniquely situated where we started off with the clinical note. What I'm really excited about and where we're expanding towards is what are all the things you can do before the conversation, during the conversation and after the conversation if you did have access to all the context about patients, payer guidelines, medical literature and put that together and to serve, how healthcare could look fundamentally different.Swyx [00:02:01]: And that's the context engine that you guys have?Chai [00:02:04]: Yes.Swyx [00:02:04]: Is that what it's called? Okay.Swyx [00:02:05]: So historically, as I understand it, the company started in 2018. A lot of people would be familiar with the AI voice notes form factor that doctors would be “Well, do you consent to being recorded?” It replaces handwriting and what have you. But it sounds like more recently there's been a big transition in the company. Tell me about the broader transition.From Documentation to Clinical Intelligence: Save Time, Save Money, Save LivesJanie [00:02:26]: So from a transition perspective, we really think about our journey as The first act was: how do we help save time? And that's where a lot of that original product was.Swyx [00:02:37]: By the way, one of those interesting statsSwyx [00:02:39]: On your landing page was, doctors spend time after hours.Janie [00:02:43]: They call it pajama time.Swyx [00:02:44]: Why is that pajama time?Janie [00:02:46]: Doctors after work in their pajamasSwyx [00:02:48]: In their pajamas. OhJanie [00:02:49]: At home are just writing and catching up on their notes every day.Janie [00:02:53]: Some of our favorite customer love stories, we have a Slack channel called Love Stories. We have clinicians telling us, “Abridge has helped us, from retiring early or we're now finally able toJanie [00:03:06]: go home and eat dinner with our kids for the first time.”Chai [00:03:08]: Save the marriage in some cases.Swyx [00:03:10]: One of the quotes was “We're not divorcing anymore.”Swyx [00:03:12]: I'm asking, “Why?”Swyx [00:03:14]: Because they're working too much.Janie [00:03:16]: But, in terms of where we're going and where we're expanding, we really think about our second and third acts around how do we help health systems save and make more money. Health systems are operating with record-low operating margins. It's getting harder and harder to serve patients and they have regulatory, some tailwinds but also a lot of headwinds coming their way and AI is ripe for helping on the saving and make-more-money piece. And then ultimately, how do we help save lives? The fact that our software and our product is open millions of times a week before, during and after a patient walks in the room, gives us massive opportunity with products like clinical decision support, which Chai is building but so many others to improve patient outcomes and probably one of the most important workflows and problems to be going after right now.From Glean to Healthcare: Context Is KingJacob [00:04:04]: One thing that's interesting, Chai, is you came over to Abridge from Glean and clinical decision support, which for our listeners is, in the context of a visit, helping a doctor figure out the right type of care. It's really a search problem in many ways, going through lots of different data sources. Very analogous to your previous role as one of the earliest engineers over at Glean. I'm sure a lot of our listeners are curious what's similar about the problems that you're going after now and what feels different, now that you're in healthcare.Chai [00:04:33]: Very similar. Taking a step back, with every wave, there's a lot of very similar patterns that happen across different products. A lot of social networking products look the same. A lot of credit-based products look the same. And we're seeing that very similar in the agent era with many companies, of course, in Redpoint's portfolio and so forth. And the key insight between both companies is that you have amazing models but context is king. Context is what puts them to work. So I see it in a lot of ways, a lot of similarities in this is a healthcare-coded version of Glean but the differences are really interesting. A couple things that come to mind. First and foremost, the rigor of the setting we're in. The downside risk is extremely high here in healthcare. It can be fatal in some cases. You prescribe something that the patient is allergic to for example. Whereas at Glean, it's “Oh, you got the question wrong.” It wasn't the end of the world in most cases. And so what does that mean? That shapes our evaluation strategy, both offline evaluation, progressive rollout and there's a lot more we could go into there. Second thing that comes to mind is, vertical versus horizontal. In both cases, there's a large variance but when Glean is, it's a much more horizontal company, there's a variance of personas, companies that you're working with. We also have a variance of personas, different types of specialties, different hospital systems. But the variance is a little more narrow. So from a product perspective, you're able to focus far more, especially when you have a maturing technology and you're building new products that never existed before. It lets you go after them much more easily and especially in healthcare where so many problems were solved with labor and process, that it's extremely ripe for AI to keep helping augment and enable. And the final thing that's really interesting, Abridge specifically compared to many other companies in the AI area, is the modality we started with where we're ambient and we're always listening in the background. And many more AI products will go that way but it's how we started. And that's the greatest form of AI we can create, AI that's seamless. You're not looking at your screen. It's always there. It's always helping you out and being proactive. The Jarvis vision that, every hackathon I went to over the past decade, there was always a Jarvis competitor. But Abridge very much started from the opportunity and continues to go that way.Ambient AI and Alert Fatigue: When Should the Product Interrupt?Jacob [00:06:57]: One thing that is super interesting then from a product perspective is you have this always-on seamless in the background and then you have to decide when you break the wall almost and say, “Hey, clinician, you might not have thought about X,” or whatever it is that you want to do. And in healthcare traditionally there's been this idea of alert fatigue and a million pop-ups and then a doctor just ignores all of them. It's probably a pattern that a lot of builders are thinking through now. How do you think about the right way to intervene or to pop up in a doctor visit?Janie [00:07:26]: It's such a good question. Alerts are notorious in healthcare specifically. Over 90% of alerts are ignored. The first and most important thing is context is everything, as Chai alluded to and I also think about how do we go from being reactive alerting to really proactive intelligence at the point at which it matters most. One thing we like to say is we want our product to feel like air conditioning. It should be in the background just making things better and if there is something that has great clinical risk and we're acutely aware that intervening now and not later is incredibly important, we should decide to act. But if you think about proactive versus reactive, instead of alerting a clinician during a visit when they're with their patient having a pretty serious and sensitive conversation, how do we prep a clinician before they walk into the room with that patient? And so historically, clinicians might have to manually go through charts with a patient that they've had over the course of months or years and they'll try to suss out what are the things they should be doing. You can imagine a world with Abridge. We'll summarize all of the most recent context for you, tell you based on the reason for a visit the patient is coming in for the types of things you should be discussing. And so you're going into that conversation prepped rather than walking in cold to that patient visit and then having this product interrupt you five or 10 times throughout the visit. And there might be times where it's really important to interrupt. We have a product called Prior Authorization and so this is when you may go into a doctor's office with knee pain. They'll prescribe you an MRI and so many of us have had this experience before, where in four weeks you'll get a call saying, “Hey, Sean, that MRI that you were prescribed wasn't approved and why don't you come back in? We'll figure it out.” In a world with Abridge, we might choose to quietly but still alert a doctor in that visit. And alert is probably not even the word we would want to use. Before a patient leaves, we would want to tell the doctor, “Hey, Doctor, before Sean leaves, you should ask him, has he had physical therapy and has his pain lasted for more than six weeks? Because the Aetna plan that he's on in California requires six things. We've already confirmed four of them have been met ‘cause we have all the context. But these two last criteria, if you can address with Sean before he leaves the room, we could guarantee that your MRI is approved before you leave.” And so when you think about clinical usefulness, impact to the patient, there are instances in which if we can catch a doctor while the patient is still in the room, as we think about save time, save money, save lives, we get to check all of those boxes. But when doctors have 15 minutes between visits, we have to be really thoughtful about when it matters.Prior Authorization: Reducing Latency in CareChai [00:10:23]: There's this interesting product opportunity AI has is reducing latency in the world. For example, prior authorization is an example of where care gets delayed and so great AI can reduce that. And the problem with alerts before partially is a technical problem: the quality of your alerts really matters. They're going to get ignored if you get alerts that... Similarly in engineering, where they're noisy alerts that you can't act on. But if you can make really high-quality alerts with both the context, as Janie said, and really high-quality models, then you can create a whole other game.Janie [00:10:53]: And I really like that experience because it starts to tease apart, what makes this so hard and unique. One, to make that prior authorization example possible, think about all the data that you need to have. You need to integrate with the electronic health record to know all of the patient context. Do we have access to your previous labs, previous imaging? And then to match you and to know that you're on Aetna, we have to collect all of the different payer policies and they vary by state. Some of these payer policies live on websites. Some of them live in unstructured 50-page PDF files.Jacob [00:11:31]: I thought this episode wasJacob [00:11:31]: To make sure we didn't scare people from healthcare.Janie [00:11:34]: But when you think about the things that make it hard, it also gives you the moat.Janie [00:11:39]: And then the second is the AI and the model quality we need to be able to hang our hat on. And so the bar, similarly when I worked at Opendoor, I worked on pricing models. Every outlier wiped out the margins of 30 and so similarly here in healthcare, the bar for accuracy is so high. And then I'd say the last is workflow is everything. If insurance companies deploy AI, it typically happens too late and this is when you have the notorious comical examples of AI just fighting each other when it's too late. But if we can pull forward the use of both the AI but also the ability to solve problems when the patient's in the room, you can start to collapse what typically takes weeks or months after your visit, ideally down to minutes or real-time. And it's where healthcare is both very difficult but also extremely rewarding if you can crack it.Product Form Factors: Mobile, Desktop, In-Room Devices, and ARSwyx [00:12:36]: Just to get some baseline on the form factors, because I've seen some videos on your website and stuff. You guys talk a lot about ambient AI. Is it primarily on the phone? Is there any other form factor that people get Abridge in? Is there an Abridge room setup where it's always on? I don't know.Jacob [00:12:55]: An Abridge podcast studio.Janie [00:12:58]: Primary form factor is mobile and desktop. UsuallyJanie [00:13:00]: Clinicians are walking in and out of rooms with mobile but at the end of the day, when they're closing out their notes or wanting to prep for the day ahead, they might use desktop. We have been having a lot of really interesting partnership conversations with a lot of these in-room device companies as you think about the power of multimodality and even more data, as you think about all of what is not captured today. It is fascinating to think about, especially even as we go into building and scaling our nursing product. It's one where nurses constantly, as they're walking in to check in on a patient for two minutes or maybe even 30 seconds,Janie [00:13:43]: Starting an Abridge experience is probably going to take longer than the visit. And so what can we do with in-room devices that are always on starts to raise really interesting and fun product questions.Swyx [00:13:54]: I was thinking, the way in tech companies we have all these Google MeetSwyx [00:13:58]: And other things, we might as well set up entire rooms with just Abridge tech.Chai [00:14:02]: Very much. AR glasses and related form factors are also relevant: how do we bring the information to the clinician in real-time without a screen, while still letting them focus on the patient?Swyx [00:14:18]: Do you think they want that? I'm skeptical of AR, but I'm curious what you've tried.Chai [00:14:26]: Admittedly, it's not a near-term product roadmapChai [00:14:29]: By any means. I'm being far-fetched.Jacob [00:14:31]: There's some sick AR stuff for surgeries.Swyx [00:14:33]: Really?Jacob [00:14:33]: When people are trying to visualize, you're about to make an incision but you want to see, what the cut might look or what the body might look like inside and they can layer in imaging.Swyx [00:14:43]: That's cool.Chai [00:14:45]: At some point in the future.Janie [00:14:46]: But there are a lot of our largest customers and at the largest health systems integrating already and so even as we think about building into it, unlocks a lot of product capabilities.Swyx [00:14:57]: And just to establish the terminology. Sorry, and I know I'm asking basic questions somewhat for myself but also for the audience who might beHealth Systems, Buyers, Clinicians, Patients, and PayersSwyx [00:15:05]: Less integrated. When you say health systems, it's like the Johns Hopkins, the Kaiser Permanentes.Janie [00:15:09]: Mayos, the Kaisers of the world.Swyx [00:15:10]: These are your customers, right? And the outcome that you deliver for them is happier doctors, reduced cost of processing, reduced mistakes. It's weird in a sense that I feel like there's also, a secondary customer, the customer of the customer and I don't know if you — do you think about it that way?Janie [00:15:28]: The other interesting and complex part of building product is we have our buyers, who are the chief medical information officersJanie [00:15:39]: The chief financial officers, the CIOs of these large health systems. Our users today are clinicians but if you think about who downstream is impacted, it's patients. And so as we build, with every product in mind, we think about who we're building for, who the secondary user is and what does that mean either in terms of experience, security compliance, ROI that we have to make tangible. And so like you said, time savings is one of them. But for CFOs, they care a lot more than just time savings. We have to show for every dollar you put into Abridge, because you have more compliant documentation or because you have fewer queries coming from your billing team, we save or add real dollars to your bottom line or top line, are things that we're constantly thinking about because of the dynamic across all three sets of users.Chai [00:16:32]: There's a whole other axis too with the payers and pharmaChai [00:16:35]: as well. Connecting all these three big stakeholders in healthcare isSwyx [00:16:39]: Do the payers ever see your data? Sorry, the payers meaning the insurers, right?Chai [00:16:44]: Yes.Swyx [00:16:44]: They also see Abridge data?Chai [00:16:47]: NoSwyx [00:16:47]: Like the direct integration to you guysChai [00:16:48]: They wouldn't see the raw Abridge data but when you're working together on something like prior authorization, whatever information they need, we'd communicate to them.Jacob [00:16:59]: That's cool. I would love to dig into the AI side. You still have a lot of problems on the AI side. And so maybe to start at the highest level, what's one of the hardest problems you have to solve in AI at Abridge today?The Hardest AI Problems: Quality, Latency, and CostChai [00:17:11]: To make things simple, let's take, building off the prior auth example. So one thing Janie talked about is okay, this data is all over the place and there's this combinatorial explosion of procedures, payer policies and even sometimes different health systems. There can be some cross-product of all of these different considerations you have to take into account. But what's really hard about this problem is doing it real-time in the conversation. So, in any AI product, usually the three KPIs you care about are quality, latency and cost. Now, what we're saying is we want you to do this real-time in the conversation, guiding the clinician. How do we do it in a way that does not break the bank? But we're using — But we also need very intelligent models because you're working with this cross-product of data and this, all this context layer as well. So you need high intelligence and high-quality because you don't want the alert fatigue but you also need to be fast and cost-effective. And so that's where a lot of clever engineering goes. It's okay, without getting into all the details here, can you model these policies in some intermediate representation or other things that you can do that can make this problem tractable? And of course, the Pareto frontier is always changing but we are also trying to do this now.Model Strategy: Third-Party Models, Proprietary Data, and Medical ConversationsJacob [00:18:26]: What implications has that had for what you take off-the-shelf and say, “ what? We don't need to be world-class at X. We'll just take this from the model providers or from some infrastructure player,” and what you're “No, this is where we spend most of our time focused on”?Chai [00:18:38]: This is, the fun challenge in AI?Jacob [00:18:42]: It changes every three months? SoChai [00:18:42]: Of course, with the shifting landscape, we try to be extremely thoughtful on predicting the trends of where third-party models are going and where we can uniquely go. And, sometimes when you talk about AI models, we're the models are just going to get infinitely better. But I don't think... It may be in the grandness of time you could say that but, within every month, every quarter, there's specific ways they're getting better. They're training on a lot more, coding data to be better coding agents, for example. And soChai [00:19:14]: We have to think about where are the things that won't — unique data that we're uniquely training on or to step back a little, where is a proprietary model bringing advantage to us is if it can give higher quality or lower cost and latency for similar quality, very similar to many other companies. And when we can do that is when we have proprietary data. So, for example, we have on the order of eighty million or hundreds of millions now getting close to of medical conversations.Jacob [00:19:44]: It's insane.Chai [00:19:45]: This is a unique data set. And this data set, it's very interesting because this data set is effectively a large part of the trace between the patient and the provider. That's where the quote-unquote debugging happens in healthcare. We have these traces at scale, as in as, our CEOs even called it, an exhaust that comes out of our product. And so when you have these traces, that's how you can train better agents on certain use cases, whether it's your transcription diarization use cases or so on or like note generation models and we can do that much cheaper and faster. But we're always also working with these third-party model providers. We closely collaborate with them and that's how we predict where the trends are going. The thing that I think about a lot is that, I know that the model providers are going to train much more on agentic workflows and so forth, so that's great, so that you have a better agentic harness. But the other thing that's interesting is that the model providers, because a large class of the consumer model providers is healthcare queries, that they might, optimize to train a lot of healthcare data to encode the knowledge in its weights. And this is just a great thing for us as well, where the off-the-shelf models can keep bett-getting better at general healthcare information, such that what our strategy is, we have a constellation of models, we can use something for this, that and, we only care about, at the end of the day, the best product experience.EHR as File System: Agentic Workflows and Real-Time InterfacesJacob [00:21:07]: And, you have, overall capabilities improving. I'm curious, as these models get better, is there something you look at and you're “, three months ago, we really couldn't do that but God, the the latest models really allow us to do it”?Chai [00:21:19]: So here's something interesting that I've, been toying with. So all models are... This wasn't super obvious a year ago but now it's become clear and clear that almost every agent is a coding agent underneath the hood? So you give it whatever file system, it can write its own code and so forth. So when you think about within healthcare and the use case that we have, you can think of the EHR effectively like a file system. It's just — it's a storage of all this information. It's a lot of information there that cannot fit into the context window, at least of today's models and you want to use that context effectively for all these product use cases we're talking about. And so if you have better agents that can, manipulate data, read that data, treat it as a file system as we see they're going and we know model companies are investing this way, then that very directly benefits us.Swyx [00:22:09]: Yeah. Okay, cool. Again, just establishing basic things. But we're going back to the model stuff. I'm really interested in double-clicking more on the real-time, element, which is pretty important for both of you. Is it — Is real-time just batches of every one minute, every five minutes? Is that how we do it? Or is there some more native, genuinely real-time in the sense that OpenAI has a real-time API or Gemini has a real-time API?Chai [00:22:35]: Yeah. Yeah. So today it is more on the on the batch basis but there's interestingChai [00:22:41]: Prototypes that we have that we're still not fully, full time, voice in text out or in that sense. But, can you trigger your models, your agents or agentic workflows, depending on the right times in the conversation?Chai [00:22:58]: And so you can imagine, different techniques to bring this latency down and, you want to bring the feedback loop down as much as you can. And so a lot of clever engineering there without fully... Maybe one day we'll do full voice in and text out, train a model to do something like that.Swyx [00:23:15]: You do — People don't want voice in voice out?Chai [00:23:18]: Now we aren't creating experiences that are, during the conversation, inter — It's almost likeSwyx [00:23:25]: Might be too disruptiveChai [00:23:26]: Too disruptive until, who knows, maybe eventually you could have full voice agents once we — the quality and we improve the comfort of the technology. But right now gra — that change is much more gradual and it's more text focus, text out.Janie [00:23:42]: And so much of currently what our product is trying to do is allow a clinician to focus on their patient and maybe at some point but right now patients, clinicians don't want a third voice, at least in a literal voice in that room. And so how do we be there with all the contacts and information ready at hand when there's the right moment?Personalization: Individual Doctors, Specialties, and Health SystemsJacob [00:24:03]: Jenny, one thing I'm curious about is how you think about, personalization in the product. I imagine, every doctor is a special snowflake in their own way, has their own way they like to do things. There are probably a bunch of different approaches you could take to doing that, both within the model layer itself but then also just with clever prompting or engineering. How do youJacob [00:24:20]: Deliver on that?Janie [00:24:21]: It's such a good question. Personalization is massive for us. We think about personalization at three levels. The first is at the individual, the second is at the specialty level and then the third is at the health system or the organization level. To your point, there are a lot of individual preferences. You-When a note is produced, it almost is a reflection that is so deeply personal of a doctor's work and how they give care. And so do they have preferences on things like style? They might want bullets versus paragraphs, really concise versus comprehensive. They also might have phrases that they really like to use or the templates that they want every note to be structured. And, we see it in our feedback all the time. We want two spaces in between sentences or I refuse to use this tool. And so that's something that we've had to build in. And the tricky part is how do you make sure that stylistic preferences don't interrupt accuracy and quality and that's something that we've really had to refine and hone over time. Second is at the specialty level. A cardiologist note or workflow is going to look very different from a dermatologist workflow.Jacob [00:25:32]: I assume cardiology notes are the highest stakes for you guys, given your CEO is a cardiologist.Jacob [00:25:36]: It's “Oh my God, make sure we get this one.”Janie [00:25:37]: Shiv, our CEO, is still a practicing cardiologist. He rounds once a month. And so, first call when we want just quick and easy user feedback too.Janie [00:25:46]: But, specialties require a lot of personalization, both in terms of what does the product look and so we make sure that as new users onboard, we catch that and the product proportionally reflects that. But also on the back end, evals at the specialty level, they are hard-earned to calibrate and get. What does a really great dermatology note look like? What makes it complete? What makes it compliant and billable is very different than a primary care doctor. And so it's not just about what does the product experience look but on the back end tuning and really deepening our understanding for the specialists. What does great output look like? And that's, a problem that we need to calibrate internally, externally, online, offline but, takes lots of cycles but is necessary in a high-stakes environment. And then at the health system level, for products like clinical decision support, you have health systems who've spent years or decades refining their best practices and they want to know, “Hey, we love your clinical decision support product but how do we embed our own hospital guidelines into them to inform clinicians before, during or after a visit what brest — best practices should look like?” And as you think about, deepening moats as well, when health systems, trust us with that data, allow us to productize it and directly into the clinical workflow, makes us a really great partner to health systems who want to build something that truly meets their needs, their practicing guidelines.AI Slop, Memory, and Product Data FlywheelsChai [00:27:23]: And I want to add onto that. The for the clinical documentation problem, it's very similar to AI writing that doesn't feel like your own and then we call that slop. But the way I describe one framing of slop is like AI without context. But we have all that context and both the clinicians, can have it and can guide it. And so part of the other interesting exhaust for us is, memory is, one of these new systems recordsChai [00:27:49]: Almost.Janie [00:27:50]: And we also have all the edits people make on our product and when you think about a data flywheel and how we get better over time becomes really powerful as a mechanism to just going deeper in personalization.Jacob [00:28:04]: It's interesting. I love this idea of working with systems on the guidelines they built up over a long time. I feel like so many of the best AI app companies today are... The question is: How do you take the expertise that a law firm or a bank has built up over many years and then add that as context and also a special sauce over, a an AI tool? And so seems like y'all are really doing that very effectively.Janie [00:28:24]: We're now starting to have our customers ask, “What are other customers doing?”Janie [00:28:28]: “And how are they doing it?”Janie [00:28:30]: And as we think about having visibility across such a large set of care being delivered right now, a really interesting place we could also partner.Swyx [00:28:40]: I'm just curious. I — This may be a nothing question but, how different are health system guidelines from each other? Don't they all converge to the same thing? And if not, where do they differ?Chai [00:28:52]: At a really high level, they're going to talk about very similar things but the difference is probably in some more of the details. “Oh, you should refer to specialists only when XYZ conditions are met,” or so forth and maybe different organizations have different practices and guidelines around that. But high level, talking about similar things but the details are what, of course, that shapes the context and the decisions you make.Swyx [00:29:15]: And this all goes into the context engine and it might affect the notes but maybe not.Chai [00:29:21]: The — For these local pathways, we're definitely thinking about it a little more for our clinical decision support product.Chai [00:29:26]: So yeah.Swyx [00:29:27]: Which is your stuff, yeah.Swyx [00:29:28]: And then the memory which you raised, let's just tell us more about that. What have you tried in memory? What's the structure of the memory? What works? What doesn't work?Chai [00:29:38]: There's, of course, many different ways you could do memory, where it's okay, can you bake it into the model weights or can you do it in some external store? For us, what's interesting is, of course, when you think the models are rapidly changing, whether it's in-house or third-party, baking into the model weights, sometimes you worry that it could be a little throwaway. And so, how do you... You need to find a way that you decompose the problem, the preferences from the underlying models and so forth. The thing we're right now most both that's easiest to start with and we're excited about is having, a separate store for memory, where you have, for example, a memory sub-agent that's, working in the background, figuring out what are the important parts of the clinician's actions that we want to remember for the long term. And then you can also imagine, other things where in the — you have background jobs that are running that are collating these, memories similar to Sleep, of course and what other pattern, patterns products do as well. Learning over all these action, all the action data we have, again, note edits, the conversations they did and the actual transcripts.Evals: LFD, LLM Judges, and Clinical SafetyJacob [00:30:40]: What about evals? How in the world do you... It is such a complex product surface area. We would love to hear you riff on that and also how has that evolved? I'm sure you've gotten better at it, so any learnings along the way.Janie [00:30:50]: From an evals perspective, we, from day one when we build any new product or feature, we think about, what does good look like? And there are table stakes things like clinical safety but then you start to get deeper into what does good quality look like. And when you go into something like our core product, there's stuff like style and completeness and there's things like does this note become something that can be billable, which is very high stakes for a health system. We have a number of ways in which we get confidence for this. We have, internal in-house clinicians who do what we call an LFD process to give us our very first pass at is this or isn't this a good enough output, look at the effing data.Jacob [00:31:41]: LFD?Chai [00:31:42]: That's why I was smiling. I was “Is Janie going to mention what it stands for?”Jacob [00:31:46]: I was not... There's like a million acronyms.Jacob [00:31:48]: How am I supposed to know that I don't? So “Oh yeah, of course, an LFD.”Swyx [00:31:51]: I've never heard of LFDs.Chai [00:31:53]: It's a bridge for sure.Janie [00:31:55]: I got through three days and then I had to ask someone.Janie [00:31:58]: I thought it was just me that didn't knowJanie [00:32:01]: It's our internal process.Swyx [00:32:02]: But look at the data as a meme in ML, ‘cause you tend to not look at it. You just want to look at number go up.Chai [00:32:06]: Exactly.Swyx [00:32:07]: But yes.Janie [00:32:08]: But so, we make sure we look at the data and then as we think about all of the components of good output, we, one, create LLM judges across all of these and we make sure with annotated data and either internal or external evaluators, we feel like these judges are calibrated. And then depending on the stakes, we also work with in-house and third-party evaluators across all of these before we ship any big change. And the goal is, in terms of evolution, how do you go from this process taking months, down to weeks, down to days? Some of it is, a true science and ML problem. A lot of it's also just, hard operational work. Have you planned ahead in terms of what you need? Have you really optimized the capacity that you need across all of the different specialties you need? Have you gotten a really good sense of which third parties are great to work with for what use cases? This takes a lot of domain, expertise and, lots of mistakes and errors in figuring that out. And so as much of it is an ML problem, so much of it has also been operational gains that are hugely important, where domain-specific expertise is everything.Specialty-Level Evaluation and Progressive RolloutsJacob [00:33:23]: But it's funny, ‘cause I feel like people talk about healthcare like it's one giant market and the reality isJacob [00:33:26]: It's, dozens and dozens of sub-markets. And so it feels like in your evals you have to build that up across the board, probably.Swyx [00:33:34]: And is specialization the primary cardinality at... That's the word that comes to mind.Janie [00:33:40]: Sometimes, depending on the product or the use case. And so if we're making a note improvement or feature for a particular specialty, definitely but we have products that are for nurses. We have products that, are really aimed at making the document or the output a lot more billable. And so we'll want to work with coding teams and not necessary clinicians. And so likeJacob [00:34:05]: Coding meaning healthcare coding.Janie [00:34:06]: Yes. Yes.Jacob [00:34:07]: NotChai [00:34:07]: Yes. I see you.Swyx [00:34:07]: Other kinds.Janie [00:34:09]: But is this output proportional to the work that was delivered? Is there sufficient documentation to justify the amount that a health system may end up charging? And so, specialty sometimes but also domain, very different across all of the different products that we're working for. And building out that network is, not easy and is where a lot of our operational investments have gone into.Chai [00:34:35]: And I view a lot of analogies to self-driving cars here, where, part of it is we really want progressive rollout of features to test in the real world is this useful? Is this going to work? One big difference compared to past lives is before I'd build a product, maybe I'd alpha it and then I'd like GA it the next week, ‘cause I'm “Go, move fast, ship,” and whatnot. But the mentality is like you... I want to make contact with the reality as quick as possible but I want a progressive rollout. Because as much as I get as large of an offline eval set, I want the distribution of that to match real-life distribution. And over time, by rolling out early, similar to Waymo has a tagline, “The world's most experienced driver,” another thing that can, at least linearly increase for us is, both the size of our evaluation offline and online, that and it all feeds back.Janie [00:35:25]: Something that's been earned over time, speaking of evolution, is just the trust we've gotten with customers. Historically, a lot of these health systems, when they bring on new vendors, their release cycles are quarters, sometimes twice a year. We've gotten our customers onto monthly release cycles, which is pretty fast for health systems but what is more exciting over the last, call it, few quarters, has been, a subset of our customers have said, “We want to innovate with you. We trust you,” and we have a pretty, decent chunk of our customers who say, “We'll develop with you outside of these monthly release cycles. We have a higher tolerance. We know that the stakes are very high but we want to be the first ones using these products, giving you feedback.” And so for a pretty substantial set of our customers, we've been able to convince them to be able to ship, in this gradual way before GA. Something we talk about a lot internally is, trust is earned in drops, earned in buckets and so we still can't do what I used to do when I worked at Loom. We had 30 million users. I'd just be, rolling out experiments left and. The bar is still quite high for iterative rollout but because of the trust we've earned, we're able to learn at pretty high volume very quickly.Privacy, HIPAA, and De-IdentificationSwyx [00:36:45]: Your scale is still pretty huge.Swyx [00:36:47]: One thing I want to... We were going to go into scale? In a sec. One thing I wanted to call up, follow up on evals, which, again, just coming from a generalist engineer point of view, just thinking through what would people be scared of in doing this, the privacy and HIPAAJacob [00:37:00]: Elements of this. I have zero experience in that. What do you have to do? What is surprisingly not that bad?Chai [00:37:06]: So one thing that's really important here from a compliance perspective is very much that any of the data we use needs to be de-identified, any real-world data we use as a basis of online eval sets we're learning from. And so you have to — And there's, very clear, government guidelines, what counts as PHI. And so we've even have built models that can take, for example, a clinical transcript and remove all the key PHI indicators and so you have a scrubbed/de-identified version. And then once you... And so one thing that's important is first you've got to get confidence in that model in the first place? And prove that out. Because, now you have, multiple probabilistic systems on top of each other.Chai [00:37:46]: But once you have that, then you can train on it use it for evaluation and so forth, provided one of the cool things also that you can do from a business side is the right data contracting as well with your partners.Jacob [00:37:57]: Is the anonymization one way? Once it's done, you cannot undo it? Or is there someoneChai [00:38:01]: YesJacob [00:38:02]: Who holds the master key that can... Yeah, okay. So it's one way.Chai [00:38:05]: It's one way. Yeah.Jacob [00:38:06]: That's how it works. I just wanted to... Because, there's a lot of this, learning from feedback and everything that, you would want to debug more but you can't because you just physically don't allow yourself to.Janie [00:38:17]: Some of it's also written in our customer contracts in terms of who can or can't access PHI data, how long do we retain it,Jacob [00:38:27]: Very goodJanie [00:38:27]: Before it gets de-identified. And so we have a pretty high bar for who can access that PHI data, just to make sure that we always respect our customer data and privacy. But that's something that we partner with our customers on too, to make sure that as we want full, as close to precision as possible in that qualityJanie [00:38:48]: We can still use it.Jacob [00:38:50]: But it'll be fascinating to see how that space evolves? Because you think about, I used to work at a company that, did a lot of healthcare data in the cancer space and if you asked, the average cancer patient, “Hey, do you want people, do you want other patients to be able to learn-”Chai [00:39:03]: Take it.Jacob [00:39:03]: “... Learn from your experience?”Chai [00:39:04]: Take it all.Jacob [00:39:05]: They're “Please.”Jacob [00:39:06]: “I'd love, nothing more than for other people to be able to learn fromJacob [00:39:10]: The experience that I had.” And so in the past it was a lot harder to do that learning. But with this technology, that might really be practical and so it'll be fascinating to see how that continues to evolve.Chai [00:39:21]: There's so much in our data set of 100 million conversations.Chai [00:39:26]: You can imagine things like insights that you can give to the clinician. How could you, oh, how could you have reacted to this? In coaching or insights around, which treatments are effective or, like... Because you have this, again, this data source that was never captured before but that's, where, intuition or experience is created from, going back to this idea that the conversation is the agent of truth.Operating at Scale: Reliability, Cost, and Token EfficiencyJacob [00:39:46]: Back to the 100 million conversations, I feel like you have this insane scale that maybe only a few other AI app companies have and everyone else dreams of. So not everyone has had to confront this yet but maybe just talk about some of the challenges of operating at that scale and what, our listeners have to look forward to if they ever get to this level of scale.Chai [00:40:05]: At large and larger in scale, so of course there's a general, infrastructure reliability. When you... In any given startup, you're building the plane while it's flying. So there's some notion of that. But what gets interesting on the AI and ML side for sure is this, as you get at more and more scale, so one, you have the data to first and foremost do this. But, you start thinking about costs or infrastructure in a whole different way at scale versus, a prototype.Chai [00:40:34]: You can use the most expensive model, you can burn as many tokens as you want but when you're doing 100 million conversationsJacob [00:40:41]: Token max on leaderboards are less upsetting than that context.Chai [00:40:45]: . When you're doing that and so that comes for we have the data and we also have the team that's able to post-train based on this and you can optimize for efficiency, especially in areas where you believe that maybe a lot of the quality headroom is less so and you don't expect the other off-the-shelf models to go that way, such that you want to do, efficiency maximization, in terms of compute and tokens.Jacob [00:41:08]: I feel like you guys live in the future in some way where most use cases today are really just in use case discovery mode, where it's “God, I really hope I can find something that can get to scale,” and so you're always going to use the most powerful model. And then the few things that do get to this level of scale, you start to do those optimizations.Chai [00:41:22]: It's a natural trajectory where it's like zero-to-one, we're not talking about any of these optimizations.Chai [00:41:26]: But when maybe we're in the one-to-100 or so forth, then we're in optimization mode and, what works out really well is you've got all this data from zero-to-one that lets you do this.What Comes Next: The Conversation as the Shared Healthcare PlatformJacob [00:41:36]: That's fascinating. I feel like one thing that's so interesting about the Abridge footprint is that you're in the doctor-patient visit in real-time. I always like to say, there's like probably 50 years' worth of product you could build on top of that. What gets each of you, I don't know, what are you most excited about building, either in the short term or medium term or even, long down the line?Janie [00:41:53]: Something that I get really excited about is that the same conversation can serve so many stakeholders. If you think about the conversation, a doctor needs to know what is the documentation, how do I make sure that this fully represent the care I gave? A patient needs to know, “What the heck just happened? This was really overwhelming. What are my next steps?” A payer needs to know, was this the proper and appropriate care given? A pharma company might want to know why isn't this drug being properly used or is there a good candidate for this clinical trial that I'm about to run? And where I get excited is that our product and our platform and our infrastructure can be the same product across all of those things and start to what's today, separate, very expensive, complex systems that serve each one of these stakeholders in very different ways, start to collapse all of that into a singular platform that enables not just more efficiency across the board but also better outcomes for everyone. And, all of us experience healthcare in probably very painful ways and knowing that there is a world in which we can simplify a lot is really exciting to me and it all starts with the conversation.Chai [00:43:15]: It's interesting. Of it very similar to going back to the KPIs that any AI product cares about. How do you increase quality of care? How do you reduce latency to care? And how do you reduce costs? Which is a huge, in healthcareJacob [00:43:28]: They call it the triple aim in healthcare.Chai [00:43:30]: But very similar to building AI products and the thing that really excites me is when we talk about that latency piece, we talked about one example earlier of prior authorization, can you reduce the latency to care? But you can imagine so much more. Oh, as soon as the lab value gets updated, do you have like a background agent that, kicks off and uses all the context to be “Oh, hey, the patient should do this next,” for example. And of flagging that to the clinician who's always in the loop but reducing that latency, to care. And then you can imagine this is much further down the road but it's like even connecting that to the direct patient and the consumer. And so how can you, how can you build a bridge to all of these things?EHR Partnerships and the Clinical Intelligence LayerJacob [00:44:10]: Very cool. The connections piece is just an ever-growing thing. And one of the key partners is the EHR and I wonder what that relationship is like. Will they, look at this as, something that is valuable enough that they want to own someday?Janie [00:44:29]: Our partnerships with the EHR is, we know that we have to be extremely close partners with all the EHRs who we partner with. Being able to not only pull and push all of the data into the right places is, not only table stakes, if we can't do that, health systems don't want to use us. The second and the reality of today is clinicians spend a lot of their days in the EHR. So much of what allowed us to win in the largest health systems was pretty direct and, very close partnerships with some of the largest electronic health records that allowed us to pull and push data with APIs that weren't ready out of the box. And clinicians want to save clicks. Anytime we introduce a new product that, adds two clicks for them in their day, they're “We're not going to use it.”Janie [00:45:21]: They have 15-minute back-to-back appointments with their patients. They're spending, hours during pajama time doing documentation. Every second and every minute counts and so we really think about being deeply integrated into the EHR as also table stakes to getting real usage and adoption. And anything that we build or introduce, we really talk about earn the right internally a lot, which is we have to provide so much value or save so much time that people will use us. But those are the two things that are close to us, is we know that the product won't be used unless it is deeply interoperable.Chai [00:46:01]: And strategically, to your point, it's like what does EHR want to own versus us? EHRs are really focused on the clinical workflows and so forth but some of the things that we're talking about here, I do these traditionally are outside of the domain where it's oh, connecting pairs and providers together with provider policies or the clinical trial matching, as Janie brought up. And so these are, entirely — we position ourselves as building this entirely new intelligence, clinical intelligence layer across, again, providers, pharma and, payers.Chai [00:46:33]: And so that's a it's a whole different ballgame that we try to playChai [00:46:36]: In combination with them.Jacob [00:46:37]: But it's like a different layer of scope.Healthcare AI Regulation, Technical Depth, and What Changed Their MindsJacob [00:46:39]: I'm curious, you are both relatively newcomers to healthcare. People have these, there's lots of futuristic healthcare AI takes of “Oh, everything will look different.”, now that you've been in healthcare for a bit, you live at the edge of AI, what have you, changed your mind on around this, as you think about what healthcare looks like in ten, 20 years? Any updates to your mental model from the time being close to the problems?Chai [00:47:02]: One thing that IChai [00:47:04]: Was hesitant about before and it's a common thing when I'm trying to recruit engineers that people ask me around, is definitely oh, healthcare, heavily regulated space. And it is, rightfully so. You want to keep, the patients at the end of the day safe. But one of the interesting things that, is a that surprised me how much it is coming to the company is there's a lot of really favorable regulatory tailwinds as well. Where you think about, government really wants interoperability between all these systems that we talked about and so agents can access this information. The government just in January, the FDA released updated guidance on clinical decision support, what I work on in such a way that they used to have guidance from like 2022 that required you to have, mention all these options and do all these other things but it's a very forward and forward-looking way. And so for me, what's been really cool to work on is this, there's this very special moment both in AI in general, we all know that but there's a special moment also regulatory in healthcare as well.Janie [00:48:05]: One thing I would call out is for the very reasons things are higher stakes or, potentially considered more difficult in healthcare, it's where some of the hardest AI problems will get solved first, just because the bar is so high. When I first joined, I was “Oh, this is where we'll be on the tail end of where, all of the AI innovation will be able to be applied.” But when you think about, zero error evals or multi-step workflows that have really low tolerance, a lot of the innovation will happen here just because we have to or else we can't ship.Jacob [00:48:42]: ‘Cause like in other domains, you'd much rather just solve the 80%-is-good-enough problems firstJanie [00:48:46]: 80/20 doesn't work hereChai [00:48:48]: And building off that, traditionally, there was a bit of stigma that, oh, healthcare companies are not that interesting from a technical perspective or I've seen that or faced that myself. But these are really hard and fun problems from a pure technical perspective beyond just the impact. How do you bring the latency of this thing down and make it really high-quality?Reducing Latency: Clinical Workflows, Agents, and Implementation RealityJacob [00:49:07]: How do you bring the latency of things down?Chai [00:49:10]: Yeah. Yeah. Yeah. So okay, let's answer the latency question. And maybe hopefully not too redundant with some of the things I've said earlier but some part of it is with any latency, you have to like what is, what is really your bottleneck. In a lot of workflows, it's sometimes it's the model itself. And so that's where like our data flywheel, our post-training team and so forth come in so that can you make the models far more efficient. So that's one aspect of latency. But there's whole other aspects of latency where it's okay, on top of that, if you use a constellation of different models, can you use — can you first use like a — it's like thinking fast and slow. Can you use a cheap, fast model that triages and hands it off to a larger model where you get more intelligence and so forth and so all theseChai [00:49:56]: Clever tricks to make it work.Chai [00:49:58]: And by the way, we are totally — we also realize that the parameter frontier is changing and so these tricks will — may not get us to where we want to be in five years but we need to if we want to build a useful product right now.Jacob [00:50:11]: Should we go to the quick-fire or you want to ask more about Abridge? We can stuff everything that's not Abridge into the quick-fireSwyx [00:50:16]: I don't mind. I was — I feel like Janie was on the topic of more long tail stuff, which isSwyx [00:50:21]: Not the eighty/twenty thing and that really matters. And I'll —, if you have any tips or cool stories or just general approaches that have worked for you that's interesting to dig into.Janie [00:50:32]: One of them is even just how we staff our teams looks different than a traditional software engineering team, I'd say.Swyx [00:50:40]: Let's go.Clinician Scientists, Edge Cases, and Evals at ScaleJanie [00:50:41]: We have a bunch of folks with different roles who are clinicians and so we have this role called the clinician scientist and I heard one of our leaders refer to them as mutants recently. But they are people who've had clinical backgrounds, so MDs typically, who are also deeply technical, somewhere, on the spectrum of like a full stack engineer all the way to like extremely scrappy prompter. But having each of these people embedded within our teams instantly raises the bar for everything that we build because not only are they determining, is this product clinically useful but they're deeply embedded in our whole evals process. And so when we talk about LFDs, when we talk about what is our actual evaluation criteria, you don't want Chai or me creating what those are because we don't have clinical background. But is probably unique to Abridge but has been game changing. And when you think about where the puck is going, you have people build with clinical backgrounds who are technical and where AI tools are going, they just becomeJanie [00:51:53]: More and more, critical and like the killers of the team. And so that's one. And then the second is just the scale at which we do evals to catch that long tail up front before anything ever gets into production is something that we've pretty much like really started to fine-tune, both from a scale but when do we know we need to get several hundred versus several thousand offline responses, what helps us make that quick decision and make this less of an art and as much of a science as possible. But that's also been something we've had to tune over time.Swyx [00:52:27]: And you have partners who opted in to give you those evals.Janie [00:52:31]: So we work either internally or with third-party for offline evals and then we have customers who also agree to give us, whether it's like thumbs up, thumbs down to like choose this or that, a lot of data to get us to what is as close to fully confident as possible.Swyx [00:52:51]: The term that comes to mind isSwyx [00:52:53]: Like active learning on things where you're weak. I feel like it's a lost artSwyx [00:52:58]: Is a lot of the polish that comes into doing something like this.Janie [00:53:02]: Really.Chai [00:53:03]: Hundred percent.Lessons from Glean: Technical Foundations and AI App InfrastructureJacob [00:53:04]: Maybe, on a totally unrelated note, Chai, you had a very, storied run at Glean b

Same-day cancellations, delayed procedures, and scheduling disruption can create major strain for orthopedic practices. In this Office Hours Takeover, Lora Pada, VP of Customer Success at Infinx, speaks with Danelle Newman, Director of Patient Access at OSS Health, about the role prior authorization workflows play in keeping schedules moving, reducing preventable delays, and giving patient access teams more breathing room.Brought to you by www.infinx.com

United Healthcare, the nation's biggest insurer, announced that it's cutting back on its requirements for prior authorization by 30%. Prior authorization is when your doctor orders a medical procedure, test, or drug, but you can't get it before the insurance company's approval. For insurers, it's a way to cut costs. For doctors and patients, it's a source of massive frustration. Plus, we check in on the state of Iran's wartime economy.

United Healthcare, the nation's biggest insurer, announced that it's cutting back on its requirements for prior authorization by 30%. Prior authorization is when your doctor orders a medical procedure, test, or drug, but you can't get it before the insurance company's approval. For insurers, it's a way to cut costs. For doctors and patients, it's a source of massive frustration. Plus, we check in on the state of Iran's wartime economy.

Ryan and Dana talk about UnitedHealthcare eliminating prior authorization for about 30% of services by the end of 2026.See omnystudio.com/listener for privacy information.

Ryan and Dana talk about UnitedHealthcare eliminating prior authorization for about 30% of services by the end of 2026.

In this episode of the ASC Podcast with John Goehle, we discuss the latest news and information including Prior Authorization changes, a recent lawsuit related to billing, CON Changes, Fire Extinguisher Requirements, Medication management of High Alert Medications with Reversal Agents, and Supply Storage outside of the ASC. In our focus segment, we will discuss a recent Quality Safety & Oversight memo  clarifying the roles of Accrediting Organizations and State Survey Agencies when an ASC's deemed status is temporarily removed. This episode is sponsored by Surgical Information Systems, RFX Solutions, Medserve and  Ambulatory Healthcare Strategies.   Notes and Resources from this Episode: Announcing our upcoming Administrator's bootcamp - May 26-29 For More Information, go to: https://conferences.asc-central.com/bootcamps/ Ambulatory Surgery Center News - April 21 Article About Prior Authorization: https://ascnews.com/2026/04/unitedhealth-group-defends-prior-auth-doubles-down-on-value-based-care-strategy/?spMailingID=195484&puid=4098726&E=4098726&utm_source=newsletter&utm_medium=email&utm_campaign=195484 Maine CON Law Changes - April 24th ASC Focus https://www.ascfocus.org/ascfocus/content/articles-content/articles/2026/digital-debut/maine-eases-certificate-of-need-requirements?utm_medium=email&utm_source=Act-On+Software&utm_content=email&utm_term=Maine%20Eases%20Certificate%20of%20Need%20Requirements&utm_campaign=Maine%20Eases%20Certificate%20of%20Need%20Requirements&cm_mmc=Act-On%20Software-_-email-_-Maine%20Eases%20Certificate%20of%20Need%20Requirements-_-Maine%20Eases%20Certificate%20of%20Need%20Requirements ASC News- April 22, 2026 by Robert Holly  https://ascnews.com/2026/04/allstate-accuses-surgery-partners-florida-ascs-of-fraudulent-billing-scheme/?spMailingID=195484&puid=4098726&E=4098726&utm_source=newsletter&utm_medium=email&utm_campaign=195484 QSO 18-12-Deemed Providers/Suppliers REVISED Clarification of the Accrediting Organizations (AOs) Role when a Provider or Suppliers Deemed Status has been Temporarily Removed https://www.cms.gov/medicare/provider-enrollment-and-certification/surveycertificationgeninfo/policy-and-memos-to-states-and-regions-items/qso-18-12 INFORMATION ABOUT THE ASC PODCAST WITH JOHN GOEHLE ASC Central, a sister site to http://ascpodcast.com provides a link to all of our bootcamps, educational programs and membership programs! https://conferences.asc-central.com/ Join one of our Membership Programs! Our Patron Program: Patron Members of the ASC Podcast with John Goehle have access to ASC Central - an exclusive membership website that provides a one-stop  ASC Regulatory and Accreditation Compliance, Operations and Financial Management resource for busy Administrators, nurse managers and business office managers.  More information and Become Member The ASC-Central Premium Access Program A Premium Resource for Ambulatory Surgery Centers including access to bootcamps, education programs and private sessions More Information and Become a Premium Access Program Members Today! Important Resources for ASCs: Conditions for Coverage: https://www.ecfr.gov/cgi-bin/text-idx?c=ecfr&rgn=div5&view=text&node=42:3.0.1.1.3&idno=42#se42.3.416_150 Infection Control Survey Tool (Used by Surveyors for Infection Control) https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/downloads/som107_exhibit_351.pdf Updated Guidance for Ambulatory Surgical Centers - Appendix L of the State Operations Manual (SOM) https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/downloads/som107ap_l_ambulatory.pdf https://www.cms.gov/medicareprovider-enrollment-and-certificationsurveycertificationgeninfopolicy-and-memos-states-and/updated-guidance-ambulatory-surgical-centers-appendix-l-state-operations-manual-som Policy & Memos to States and Regions CMS Quality Safety & Oversight memoranda, guidance, clarifications and instructions to State Survey Agencies and CMS Regional Offices. https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/SurveyCertificationGenInfo/Policy-and-Memos-to-States-and-Regions Other Resources from the ASC Podcast with John Goehle: Visit the ASC Podcast with John Goehle Website Books by John Goehle Get a copy of John's most popular book - The Survey Guide - A Guide to the CMS Conditions for Coverage & Interpretive Guidelines for Ambulatory Surgery Centers

In this episode of The Dish on Health IT, Tony Schueth is joined by Dr. Thomas Keane, National Coordinator for Health IT at ONC, along with Alix Goss and Janice Reese. The conversation moves between policy, standards, and real-world implementation, with Tony often grounding the discussion in the practical friction points the industry continues to face. Tony opens by noting that “ONC is ONC again,” setting a lighter tone while also framing the broader conversation around where federal health IT policy is headed. He highlights Dr. Keane's unusual background spanning engineering, clinical practice, and federal leadership, asking how that path shaped his perspective on impact. Dr. Keane explains that his transition into policy was driven by exposure and opportunity, but importantly, he continues to practice medicine. Tony picks up on that point, noting how rare it is for a National Coordinator to still be actively practicing, reinforcing the value of having a policy leader grounded in real-world care delivery. Interoperability at the “Speed of Trust” Tony then shifts the conversation to one of his core themes: interoperability as infrastructure. He references Dr. Keane's framing of interoperability needing to operate at the “speed of trust,” and pushes on the tension between that vision and the reality of legacy systems still dominating the market. Dr. Keane responds by walking through ONC's dual-track approach. On one hand, rulemaking like HTI-5 is pushing toward a FHIR-based, API-driven future. On the other, ONC recognizes that legacy standards are deeply embedded and must continue to be supported. He also points to the CMS Health Tech Ecosystem initiative as a powerful example of how government can accelerate progress by convening stakeholders rather than relying solely on regulation. Tony brings Janice Reese into the discussion to ground this vision in implementation reality. Janice emphasizes that the biggest barriers are not the APIs themselves, but the underlying trust infrastructure. She outlines identity, security, consent, and directory services as the key gaps preventing interoperability from scaling nationally. Imaging as a Case Study in Misaligned Incentives Tony pivots to diagnostic imaging, framing it as a clear example where standards exist but adoption lags. He references the continued reliance on physical media like CDs and asks whether the issue is less about technology and more about incentives and certification. Dr. Keane agrees and shares a detailed example from his time as a radiologist, describing how consolidating imaging workflows improved efficiency and reduced turnaround times. He uses this to illustrate the broader point: the technology exists, but economic and operational incentives often work against seamless data exchange. He also notes that ONC's recent RFI is intended to better understand these barriers and inform future rulemaking. Tony keeps the tone light with a quick aside about McDonald's and queue efficiency, but uses it to reinforce a serious point. Even when better systems exist, organizations sometimes stick with less efficient models because they are familiar or expected. Prior Authorization: Progress, but Still Fragmented Tony then moves into prior authorization, referencing CMS-0057 and Da Vinci use cases as signs of progress, particularly on the medical side. He contrasts that with the ongoing fragmentation in pharmacy prior authorization and asks how ONC is thinking about bridging that gap. Dr. Keane emphasizes that standards alone are not enough. Real progress depends on making those standards usable in practice. He points to ongoing work with EHR vendors, PBMs, and intermediaries to ensure that real-time prescription benefit tools deliver complete and accurate information that clinicians can trust. Tony and Alix build on this by connecting real-time benefit checks to broader price transparency efforts, suggesting that combining these capabilities could fundamentally change how patients and providers make decisions together at the point of care.  Price Transparency: Still Not Patient-Friendly Tony directly challenges the current state of price transparency, asking how the industry moves beyond “check-the-box” compliance to delivering something that is actually usable for patients. Dr. Keane acknowledges that while progress has been made, much of the data remains too complex and not sufficiently tailored to individual patients. He notes that CMS continues to iterate on requirements, but that making cost information actionable at the point of care remains an ongoing challenge. AI: From Hype to Real Utility Tony transitions to AI with a callback to a joke Dr. Keane made about AI either transforming healthcare or reducing it to three bullet points. He uses that setup to ask whether AI can realistically make complex healthcare data usable for patients and clinicians. Dr. Keane answers with a firm yes, pointing to existing use cases in radiology and clinical workflows where AI is already improving accuracy and efficiency. He shares examples of AI identifying stroke patterns, highlighting abnormalities in imaging, and even summarizing clinical reports. Tony then brings the conversation back to risk, asking about overreliance on AI and how policy should address bias and accountability. Dr. Keane is clear that responsibility still sits with the clinician, noting that physicians are trained to recognize bias and must independently validate AI-driven insights. Janice and Alix add that AI's success ultimately depends on the quality and standardization of the underlying data. Without consistent, trusted data, AI will simply amplify existing gaps. Information Blocking and Enforcement Tony closes the main discussion by turning to information blocking, asking what message ONC has for organizations that continue to restrict data access under the guise of technical or legal constraints. Dr. Keane outlines a range of enforcement mechanisms, from corrective action plans to potential financial penalties. He emphasizes that while ONC prefers to work with organizations to resolve issues, the expectation is clear: data must flow. Final Call to Action: Data Liquidity As always, Tony ends with a call-to-action question. If there were one thing the industry could do starting tomorrow, what would it be? Dr. Keane's answer is direct: make data liquid. He ties this back to reducing administrative burden, improving price transparency, and enabling better patient decision-making. The goal is a system where data flows seamlessly, at the direction of the patient, to support care and operations. Janice and Alix close by reinforcing that the industry does not lack standards or policy direction. The real challenge is aligning stakeholders and scaling adoption.  

Health Affairs Publishing's Jeff Byers welcomes Senior Editor Michael Gerber back to the pod to discuss recent federal action on prior authorization, including CMS's proposal to expand response-time requirements to prescription drugs. The conversation also covers insurer efforts to reduce prior auth volume, the promise of AI and prior authorization, and what increased transparency could reveal about costs.To learn more, check out our recent Insider trend report on the current prior authorization landscape. And if you haven't already, join Insider today to get access to exclusive events, newsletters, cheat sheets, and reports.Related Links:CMS proposes new deadlines for prior authorizations for drugs (Healthcare Dive)2026 CMS Interoperability Standards and Prior Authorization for Drugs Proposed Rule (CMS)A Rule by the Centers for Medicare & Medicaid Services on 02/08/2024 (Federal Register)Health Plans Reduce Prior Authorization, Support Continuity of Care and Enhanced Consumer Communications (Blue Cross Blue Shield)AI speeds up prior auth, coding while driving higher costs for health systems: PHTI report (Fierce Healthcare)Sign up for our free Health Affairs newsletters to stay up to date on health policy news and analysis.

This week in the Breakroom, Jeffrey Davis joins Maddie News to break down what led up to CMS's release of the prior authorization for drugs proposed rule and to discuss other prior authorization action from the Trump Administration.

Episode Summary In this episode of the MedCity Pivot Podcast, host Arundhati Parmar sits down with Javier Gonzalez (Abarca Health) and Tanvi Patel (Amazon Pharmacy) to unpack one of healthcare's most frustrating processes: prior authorization. The conversation explores how outdated systems, lack of transparency, and fragmented communication are eroding patient trust and delaying care. From policy complexity and data gaps to operational risks, the guests break down why prior authorization remains such a challenge—and what it will take to modernize it at scale. They also highlight the critical role of transparency, interoperability, and consumer expectations in shaping the future of healthcare. With insights from both payer and pharmacy perspectives, this episode paints a clear vision of a more patient-centered system where access to medication is faster, clearer, and more trustworthy Links & Resources Connect with Arundhati Parmar aparmar@medcitynews.com  https://twitter.com/aparmarbb?lang=en https://medcitynews.com/ Keywords prior authorization healthcare transparency patient trust interoperability digital pharmacy healthcare innovation medication adherence health tech ePA patient experience healthcare systems PBM reform Amazon Pharmacy Abarca Health Episode Highlights 00:00–00:25 – Introduction to prior authorization challenges and patient frustration 00:01–00:49 – Overview of modernization efforts in healthcare systems 00:01:27–00:03:31 – The three core challenges: policy complexity, data quality, operational risk 00:04:11–00:04:29 – Real-life impact: delays in critical care (cancer case) 00:04:36–00:05:22 – How prior authorization erodes patient trust 00:05:59–00:07:00 – Medication adherence begins before the first dose 00:07:00–00:07:18 – 20–30% drop-off due to prior authorization failures 00:07:18–00:07:39 – Transparency as the key to patient engagement 00:08:20–00:09:45 – Benefits of electronic workflows (60–70% efficiency gains) 00:11:18–00:12:24 – What should be eliminated in a redesigned system 00:12:47–00:13:55 – Employers' role in improving benefit transparency 00:15:23–00:16:21 – Rise of modular PBM models and industry shifts 00:20:46–00:22:11 – Misaligned incentives across healthcare stakeholders 00:23:51–00:24:45 – Consumer expectations reshaping healthcare timelines 00:25:14–00:26:11 – The future: invisible, frictionless prior authorization

The Friday Five for April 17, 2026:   [00:57] CMS 2027 MA and Part D Final Rule [02:09] Google Announces New Chrome Features [04:56] March 2026 CPI/2026 FOMC Calendar [07:03] Protective Life Corporation Study on Gaps in Medicare Knowledge [10:17] CMS 2026 Prior Authorization Proposed Rule   Get Connected:

MEDICARE ADVANTAGE MINUTE:                                                                       MEDICARE ADVANTAGE INSURERS MADE NEARLY 53 MILLION PRIOR AUTHORIZATION DETERMINATIONS IN 2024.                                                    AVERAGE DENIAL RATE OF 7.7% BUT SOME COMPANIES ENFORCED A MUCH HIGHER LEVEL OF DENIALS THAN OTHERS. FINALLY, ENJOY THE LAST CHAPTER OF MEDICARE ENROLLMENT GUIDE!          YOU CAN DOWNLOAD THE COMPLETE BOOK FROM MY WEBSITE: www.MedicareForTheLazyMan.com.   Contact me at: DBJ@MLMMailbag.com (Most severe critic: A+)                   Visit us on: BabyBoomer.ORG Inspired by: "MEDICARE FOR THE LAZY MAN 2026; SIMPLEST & EASIEST GUIDE EVER!" "MEDICARE ENROLLMENT GUIDE!" (Free download from site below) "MEDICARE DRUG PLANS: A SIMPLE D-I-Y GUIDE" For sale on Amazon.com. After enjoying the books, please consider returning to leave a short customer review to  help future readers. Official website: https://www.MedicareForTheLazyMan.com.

In this Episode of the Secure Your Retirement Podcast, Radon Stancil and Murs Tariq discuss emerging Medicare policy changes with Medicare specialist Sean Southard, focusing on a new Medicare pilot program introducing prior authorization into Original Medicare. This important conversation highlights how Medicare prior authorization could reshape retiree healthcare, especially for those relying on Medicare and Medigap plans for flexibility and simplicity in their retirement financial plan.Listen in to learn about how Medicare changes in 2026 may impact Healthcare in retirement, including new Medicare coverage rules and the evolving Medicare approval process. The discussion explains prior authorization explained in simple terms and explores how these changes may affect Medicare costs, access to care, and long-term retirement planning strategies for those looking to retire comfortably and secure your retirement.In this episode, find out:What Medicare prior authorization is and how it changes the current structure of Original MedicareDetails of the Medicare pilot program launching in select states and what it could mean nationwideHow Medicare and Medigap plans may be impacted by new Medicare policy changesWhich procedures may require approval under new Medicare coverage rulesHow these changes could affect your retirement checklist and overall plan for retirementTweetable Quotes:“Prior authorization is about checking before a procedure happens instead of paying first and reviewing later—and that's a big shift for Original Medicare.” – Murs Tariq“Even if this starts as a small pilot program, the writing on the wall suggests it could expand and impact how retirees experience Medicare nationwide.” – Radon StancilAs Medicare for retirees continues to evolve, understanding changes like prior authorization is essential for building a strong retirement financial plan. While the goal of these Medicare policy changes is to reduce fraud, waste, and rising Medicare costs, they may also introduce new administrative steps, potential delays, and added complexity in accessing care.For those focused on planning retirement, staying informed about healthcare in retirement is just as important as managing investments. Whether you rely on Original Medicare, supplement with Medigap plans, or are evaluating options, being proactive about these changes can help you better plan for retirement, update your retirement checklist, and continue retiring comfortably with confidence.Resources:If you are in or nearing retirement and you want to gain clarity on what questions you should be asking, learn what the biggest retirement myths are, and identify what you can do to achieve peace of mind for your retirement, get started today by requesting our complimentary video course, Four Steps to Secure Your Retirement!To access the course, simply visit POMWealth.net/podcast.

In this episode, Jakob Emerson, Associate News Director, Becker's Healthcare, breaks down the latest Medicare Advantage rate updates, policy changes targeting upcoding, and ongoing efforts to reform prior authorization amid transparency challenges.

Driving Radiology Growth Through Smarter Prior Authorization Independent radiology centers face growing competition from hospital systems, where speed to schedule and authorization turnaround directly impact patient access and referral retention. In this Office Hours session, Heidi Simpson, Operations Manager at Advanced Diagnostic Radiology, shares how leveraging prior authorization services as a strategic advantage has helped her organization compete, grow, and modernize operations—while maintaining a patient-first approach. Find all of our network podcasts on your favorite podcast platforms and be sure to subscribe and like us. Learn more at www.healthcarenowradio.com/listen

Prior authorization has changed dramatically over the last decade, but not every solution has evolved in the same way. In this episode, Jason Lewis explores the history of prior authorization automation and helps healthcare leaders understand the models, gaps, and realities behind today's competitive landscape.

In this episode of "Medical Matters Podcast," Dr. Peter Brier and Nurse Practitioner Kelly McCormick discuss Prior Authorization, the process by which a healthcare provider must obtain approval from the insurance provider. The Cleveland Clinic outlines the full definition here.Through experience, the doctors outline the process of prior authorization. They further cite the so-called "hidden" and administrative costs.

In this episode, Jakob Emerson, Associate News Director, Becker's Healthcare, breaks down new prior authorization transparency rules and the growing scrutiny of insurer vertical integration, exploring what these trends could mean for costs, policy, and the future of the payer landscape.

Independent radiology centers face growing competition from hospital systems, where speed to schedule and authorization turnaround directly impact patient access and referral retention. In this episode, Heidi Simpson, Operations Manager at Advanced Diagnostic Radiology, shares how leveraging prior authorization services as a strategic advantage has helped her organization compete, grow, and modernize operations—while maintaining a patient-first approach.Brought to you by www.infinx.com

Free Guides Mentioned in This Episode: 2026 Margin Protection Playbook: https://natrevmd.com/margin-playbook Eligibility & Billing Verification Guide: https://natrevmd.com/eligibility-billing-verification/ Prior authorization has officially changed and medical practices need to act now.As of January 1, 2026, new CMS prior authorization rules are in effect, including faster Medicare Advantage decision timelines, new prior auth requirements for 17 traditional Medicare services, and a shift away from fax-based workflows toward electronic APIs.In this episode, we break down the three biggest prior authorization changes for 2026 and share a simple 3-step action plan to help OB/GYN, urgent care, and specialty practices reduce denials and protect cash flow.

Jonathan Aguiar, Senior Solutions Engineer, walks through prior authorization verification and demonstrates how the Follow Up AI agent works within the workflow. You will see how cases move through entry and validation, triage, payer review, pending initiation outreach, exception handling, and final status. The demo highlights how the follow-up AI agent monitors payer portals, tracks authorization status, and escalates exceptions, while outreach specialists coordinate with referring providers when initiation is missing. Together, automation and human intervention create structure and accountability in a process that is often fragmented and manual. Find all of our network podcasts on your favorite podcast platforms and be sure to subscribe and like us. Learn more at www.healthcarenowradio.com/listen

Pharmacy prior authorization is far more complex than most teams realize, especially across long-term care, specialty, compounding, and infusion environments. In this episode, Derek Taylor explains how those workflows really function, where the operational friction lives, and why getting the process right is critical to faster medication access and cleaner coordination between pharmacies, payers, and prescribers.

Healthcare documentation is no longer written for a single audience. Today, the medical record must simultaneously meet federal regulatory requirements and the coverage expectations of individual payers.While these systems often overlap, they originate from different authorities and serve different purposes. One governs compliance and program integrity; the other determines whether services are approved and reimbursed.As prior authorization expands and audit scrutiny intensifies, hospitals are increasingly navigating both systems at once. Understanding the distinction between regulatory documentation standards and payer-driven requirements is becoming essential for aligning clinical workflows, documentation practices, and operational strategy in today's healthcare environment.During the next live edition of the venerable Monitor Monday, the  live Internet broadcast, senior healthcare consultant Penny Jefferson returns to the broadcast to explain what many today are calling the new face of healthcare.Broadcast segments will also include these instantly recognizable features:Monday Rounds: Ronald Hirsch, MD, vice president of R1 RCM, will be making his Monday Rounds.The RAC Report: Healthcare attorney Knicole Emanuel, partner at the law firm of Nelson Mullins, will report the latest news about auditors.Risky Business: Healthcare attorney David Glaser, shareholder in the law offices of Fredrikson & Byron, will join the broadcast with his trademark segment.Legislative Update: Adam Brenman, senior legislative affairs liaison for Zelis, will report on current healthcare legislation.

In this episode, host Sandy Vance chats with Frank Toscano, the new Senior Vice President of Product and Engineering at Amplify. They talk about the continued relevance of fax technology in healthcare, the challenges of interoperability, and how Amplify aims to streamline workflows to improve patient care. Frank highlights the importance of integrating fax technology with modern systems to enhance efficiency and reduce friction. In this episode, they talk about: Fax remains an important part of healthcare communication Many interoperability challenges come down to integration and mapping Prior authorizations often still depend on fax How Amplify supports healthcare organizations of all sizes Streamlined patient referrals can improve care delivery Healthcare is an interconnected ecosystem that affects outcomes Maximizing existing technology boosts operational efficiency AI helps connect data for better decision-making Effective solutions start with understanding real workflows Eliminating legacy technology isn't always the best option The future blends proven methods with modern technology A Little About Frank: Frank Toscano is a nationally recognized product and technology leader with more than 20 years of experience modernizing how healthcare organizations exchange documents, automate workflows, and connect systems through AI-driven interoperability. As Senior Vice President of Product & Engineering at Amplify, he serves as the company's public-facing technology voice and strategic advisor, guiding product innovation, engineering excellence, and enterprise integrations. Previously, as Vice President of Product Management at Consensus Cloud Solutions (eFax Corporate), Frank led the transformation of legacy fax into cloud-native, HIPAA-compliant interoperability services, delivering FHIR integration, TEFCA-aligned exchange, AI-powered document processing, and large-scale workflow automation used by thousands of healthcare organizations. A named inventor with multiple U.S. patents in secure communication and intelligent document workflows, Frank has also held senior leadership roles at Cellebrite, Cleo, and Retarus, consistently bridging deep technical architecture with real-world clinical and operational needs to reduce manual burden and improve care coordination.

In this episode, Jonathan Aguiar, Senior Solutions Engineer, walks through prior authorization verification and demonstrates how the Follow Up AI agent works within the workflow. You will see how cases move through entry and validation, triage, payer review, pending initiation outreach, exception handling, and final status. Learn how automation and human expertise work together to deliver real time visibility, reduce cancellations, and accelerate scheduling.Brought to you by www.infinx.com

In this episode, Elizabeth Crawley, Vice President for Clinical and Care Management Solutions at EXL, explores how AI driven workflows and agentic automation are transforming prior authorization. She discusses balancing efficiency with clinical oversight, scaling decision support across the enterprise, and why data readiness and change management are critical to success.This episode is sponsored by EXL.

In this episode of The Dish on Health IT, host Tony Schueth is joined by co-host Alix Goss and special guest Amy Gleason, Strategic Advisor to Centers for Medicare & Medicaid Services (CMS) and Administrator of the U.S. Department of Government Efficiency (DOGE) Service, for a wide-ranging discussion on how health IT modernization is evolving under a pledge-driven, incentive-backed federal strategy.The conversation begins not with policy, but with lived experience.From Emergency Room to Interoperability AdvocateAmy shares how her early career as an emergency room nurse exposed the dangers of fragmented information. Providers were expected to make critical decisions without access to complete patient histories, while patients, often in pain or distress, were unrealistically asked to recall complex medical details.That professional frustration became deeply personal when her daughter went more than a year without diagnosis for a rare autoimmune disease, juvenile dermatomyositis (JDM). Multiple specialists saw pieces of the puzzle, but no one could see the full picture across charts and settings. Amy reflects that if today's AI tools had been applied to her daughter's complete longitudinal record, the condition may have surfaced sooner.That experience shaped her philosophy. Technology must converge with policy and trust in ways that tangibly improve care.Why Pledges Instead of Rules?Tony presses on a central theme. Amy has argued that we cannot regulate our way to success. Why pursue voluntary pledges instead of federal rulemaking?Amy explains her frustration returning to government in 2025 to find interoperability policies she helped draft in 2020 still not fully effective until 2027. Seven years is an eternity in technology. Meanwhile, the industry had technically complied with numerous mandates including Meaningful Use, Cures Act APIs and CMS interoperability rules, yet many workflows still felt broken.In her view, regulation created a floor but not always real transformation.The CMS Health Tech Ecosystem Pledge was launched as a different model. The federal government used its convening power to articulate a clear vision and challenge industry to deliver minimum viable products within six to twelve months rather than years.Initially announced with roughly 60 companies, the pledge initiative has grown to more than 600 participants collaborating in working groups. The three initial patient-focused use cases include:Improving data interoperability“Killing the clipboard” through digital identity and QR-based sharingLeveraging conversational AI and personalized recommendations for chronic conditions such as diabetes and obesityAmy describes live demonstrations at a Connectathon showing OAuth-enabled data retrieval, QR ingestion into EHR workflows and AI-powered recommendations built on patient data. The goal is not perfection by the first milestone, but real-world minimum viable functionality that can iteratively improve.Alix notes that from the standards community perspective, this approach feels aligned with long-standing calls for industry-driven collaboration, though it remains early to measure widespread impact.Carrots, Sticks and Rural HealthThe discussion turns to incentives.Amy outlines the administration's carrots and sticks strategy:Stick: Enforcement of information blocking, with penalties up to $2 million per occurrenceCarrots: Financial incentives such as the $50 billion Rural Health Transformation Program and the CMS ACCESS Model, which pays for technology-enabled outcomesThe Rural Health Transformation Program directs money to states with expectations that ecosystem-aligned interoperability and app participation be incorporated into funding proposals. CMS retains oversight and clawback authority to ensure funds support rural providers.The ACCESS Model represents a significant shift. Technology-enabled care platforms can register as Medicare Part B providers and be paid for measurable outcomes in tracks such as cardiometabolic disease, musculoskeletal conditions and behavioral health. Providers remain in the loop and receive compensation for referral and care plan oversight.Alix underscores that rural providers face steep financial and workforce constraints. Standards participation, implementation and technology upgrades require resources that are often scarce. The success of these incentives will depend on whether they reduce burden rather than add to it.AI: Evolution, Risk and RealityAI becomes a central thread of the episode.Amy compares AI adoption to autonomous vehicle models. Some scenarios allow tightly controlled automation, such as medication refills, while others require a human in the loop for higher-risk decisions. She points to a Utah prescription refill pilot as an example of bounded automation, where malpractice coverage and clearly defined use cases mitigate risk.When Tony asks who owns risk in this evolving landscape, Amy emphasizes the need for light but clear regulatory pathways rather than fragmented state-by-state oversight.Patients, she notes, are already there. Millions are asking health-related questions weekly through AI tools. The more pressing issue is ensuring those tools are grounded in structured medical data rather than incomplete memory or unverified inputs.She shares a striking story. Her daughter was excluded from a clinical trial due to a misclassification of ulcerative colitis. By uploading her records into an AI model, they identified a more precise diagnosis, microscopic lymphocytic colitis, which did not disqualify her from the trial. For Amy, this demonstrates both the power and inevitability of AI use.Alix adds caution. AI is only as strong as the data beneath it. Dirty, inconsistent and poorly structured data limits performance. Standards and terminologies remain essential to fuel high-fidelity models and safeguard trust.FHIR, Deregulation and the Data FoundationThe conversation addresses an emerging tension. If regulatory burdens are being reduced, does that signal less need for structured standards like FHIR?Amy candidly admits she initially wondered whether AI might reduce the need for FHIR altogether. After discussions with labs and technologists, she concluded the opposite. Standardized data dramatically improves AI performance and reduces error.Deregulation is about removing unnecessary burden, not abandoning foundational data structures.Alix reinforces that FHIR enables discrete, normalized data capture that supports both legacy transactions and AI evolution. While future innovations may emerge, today FHIR remains the backbone for scalable interoperability.Prior Authorization and HIPAA ModernizationThe episode dives into prior authorization modernization across medical and pharmacy domains.Amy notes growing interest among pledge participants to expand into pharmacy prior authorization testing, diagnostic imaging, real-time benefit checks and bulk FHIR performance testing.Alix provides insight into ongoing work within the Designated Standards Maintenance Organizations to incorporate FHIR-based approaches into HIPAA-named standards, particularly for prior authorization. She highlights testing beyond Connectathons, including implementer communities and real-world pilot efforts.Both stress the importance of public comment periods and industry engagement, describing participation as a civic responsibility for health IT professionals.Trust as the Core EnablerThe final segment centers on trust.Amy explains that the ecosystem initiative aims to reinforce trust through:Stronger digital identity verification such as Clear, ID.me and Login.govCertification frameworks such as CARIN and DIME for patient-facing appsA new national provider directory to replace fragmented provider data sourcesTransparency dashboards showing data requests, volumes and purposeRather than replacing frameworks like TEFCA, she describes the pledge model as an accelerator layered above the regulatory floor.Transparency acts as sunlight, enabling visibility into who is accessing data and for what purpose.Final TakeawaysIn closing, Amy urges providers not to sit on the sidelines. Too often, she says, providers feel change is imposed on them. The pledge environment is designed as an open forum where they can directly shape what works or does not work in real workflows.Alix echoes the call. Standards require participation. Organizations must allocate budget and staff to engage, comment and collaborate. It truly takes a village.Tony concludes by framing the episode's core message. Regulation establishes baseline expectations, but voluntary movements can demonstrate what is possible before mandates reach the Federal Register.Across pledges, payment reform, AI evolution and trust frameworks, the episode underscores a consistent theme. Modernization in health IT depends not only on policy direction, but on shared accountability and active participation from every stakeholder in the ecosystem.Listeners are reminded that POCP is available to support organizations in understanding the implications of federal initiatives, enforcement priorities and their strategic implications. Reach out to us to set up an initial consultation. The episode closes, as always, with the reminder that Health IT is a dish best served hot.Prefer video? Catch episodes on the POCP YouTube channel

In this episode of Revenue Cycle Optimized, Jennifer Glockzin, Senior Director of Patient Access at Infinx, walks through the real-world prior authorization process from intake to determination and appeals. Her breakdown highlights how disciplined workflows, supported by AI agent automation and coordinated human in-the-loop, protect revenue and reduce preventable denials.

Some medical procedures and treatments require prior authorization from your health insurance company, meaning you'll need pre-approval before you can receive care. This episode, health care reporter Sarah Boden shares tips on making the prior authorization process as smooth as possible — so you can save yourself frustration and get medical support sooner.Follow us on Instagram: @nprlifekitSign up for our newsletter here.Have an episode idea or feedback you want to share? Email us at lifekit@npr.orgSupport the show and listen to it sponsor-free by signing up for Life Kit+ at plus.npr.org/lifekitLearn more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy

Some medical procedures and treatments require prior authorization from your health insurance company, meaning you'll need pre-approval before you can receive care. This episode, health care reporter Sarah Boden shares tips on making the prior authorization process as smooth as possible — so you can save yourself frustration and get medical support sooner.Follow us on Instagram: @nprlifekitSign up for our newsletter here.Have an episode idea or feedback you want to share? Email us at lifekit@npr.orgSupport the show and listen to it sponsor-free by signing up for Life Kit+ at plus.npr.org/lifekitLearn more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy

MEDICARE ADVANTAGE MINUTE:                                                                      CMS (THE FEDERAL GOVERNMENT) TIGHTENS MEDICARE ADVANTAGE PRIOR AUTHORIZATION RULES!                                                                                              Before that we discuss the qualities of certain members of England's Royal Family. January 1st saw a pilot program begin, creating prior authorization requirements for a few procedures performed under Original Medicare in six states. The program will be testing whether enhanced technologies like AI and machine learning can streamline prior authorizations and medical reviews. New, soon-to-be-client Sheryl, is taking a very cerebral approach to the concept of Medicare supplement rate stability. She discovered a site called: SERFF.com and is trying to identify those Medicare supplement plans with less rate stability, which most would prefer to avoid. Inadvertently, she uncovered a pattern that confirms my repeated assertion about Plan G vs. High Deductible Plan G: Plan G increases will be larger and more frequent while the rate increases under HDG will tend to be smaller and less frequent. Finally, I received six messages inviting me to be a guest on a brand new podcast. I was offered the opportunity to program the time for an initial chat but could not figure out how to use the scheduling app.  Stay tuned!        Contact me at: DBJ@MLMMailbag.com (Most severe critic: A+)                   Visit us on: BabyBoomer.ORG Inspired by: "MEDICARE FOR THE LAZY MAN 2026; SIMPLEST & EASIEST GUIDE EVER!" "MEDICARE DRUG PLANS: A SIMPLE D-I-Y GUIDE" "MEDICARE FOR THE LAZY MAN: ENROLLMENT GUIDE!" (coming soon) For sale on Amazon.com. After enjoying the books, please consider returning to leave a short customer review to  help future readers. Official website: https://www.MedicareForTheLazyMan.com.

Prior authorization and eligibility verification in therapy and rehab are shaped by payer urgency, visit limits, and fragmented automation. In this episode, we explore what it takes to manage these workflows at scale by combining payer-aware automation, human follow-through, and system integration to deliver complete, usable outcomes without shifting work back to internal teams.

In this episode, Chris Gay, CEO and Co-Founder of Evry Health, joins Jakob Emerson to discuss how technology, scale, and business model alignment can dramatically reduce prior authorization friction. He shares why Evry Health's approach delivers faster decisions, lower denial rates, and a better patient and provider experience, and what the industry needs to change next.

Episode 53 - Federal Rule to State Reality & National Impact: How MHDC Is Shaping Prior Authorization On this episode host Tony Schueth, CEO of Point-of-Care Partners (POCP), and co-host Ross Martin, MD, Senior Consultant with POCP are joined by guest, Denny Brennan, Executive Director of the Massachusetts Health Data Consortium (MHDC). Together, they examine how MHDC is translating national interoperability policy into practical, statewide action, specifically around the CMS 0057 rule. Find all of our network podcasts on your favorite podcast platforms and be sure to subscribe and like us. Learn more at www.healthcarenowradio.com/listen

The ultimate challenge of operating an OBL is staying profitable. In this episode of BackTable, we bring on healthcare administrator Laurie Bouzarelos and interventional radiologist Dr. Mary Costantino to talk through the intricacies of revenue cycle management as an IR managing an OBL. --- SYNPOSIS The conversation covers the full lifecycle of getting paid in an IR practice, from initial patient contact through final claim resolution. Key topics include credentialing, determining medical necessity, coordination of benefits, prior authorizations, and the importance of working with billing and practice management teams experienced in interventional radiology. The episode also examines how EHR and practice management platform selection impacts clinical workflows and reimbursement, and closes with a discussion on payment plans and how emerging technologies, including AI, may shape the future of revenue management in IR-led OBLs. --- TIMESTAMPS 00:00 - Introduction 01:08 - The Importance of Revenue Cycle Management09:29 - The No Surprises Act and Data Transparency12:03 - Professional Societies and Continuing Education17:50 - Credentialing and Taxonomy Codes40:28 - Impact of Insurance Credentialing on Patient Care42:08 - Revenue Cycle Management Walkthrough48:18 - Challenges with Medicare Advantage and Coordination of Benefits54:20 - Covered vs. Non-Covered Services59:03 - Medical Necessity and Insurance Policies01:01:04 - Prior Authorization and Payment Issues01:13:11 - Payment Plans and Compliance01:23:10 - Practice Management Software01:31:10 - AI in Healthcare and Compliance01:38:57 - Final Thoughts --- RESOURCES Medical Group Management Administration (MGMA)https://www.mgma.com/

In this episode, Jakob Emerson, Associate News Director, Becker's Healthcare, discusses consolidation across the Blue Cross Blue Shield system, rising friction between payers and providers over coding and denials, and how AI and upcoming CMS prior authorization rules are reshaping the payer landscape.

On Tuesday's show: Beginning in January, a new Medicare program in Texas and five other states will use AI to approve or deny certain services. We learn what that could mean for Houston patients and what it signals about the future of health care.Also this hour: We discuss the city of Houston's current approach to homelessness, and, in particular, giving out tickets to homeless people who can't possibly pay them. We discuss with Kelly Young, president and CEO of the Coalition for the Homeless of Houston/Harris County.Then, we consider how Houstonians can keep the peace at home while navigating holiday traditions that might put an unfair burden on someone in the household. We discuss with Robyn Martin from The Menninger Clinic.And we take you to an immersive multimedia holiday experience at ARTECHOUSE Houston.Watch

Broadcast from KSQD, Santa Cruz on 12-11-2025: Dr. Dawn presents colleague Dr. Paul Godin's essay on why US healthcare fails as a market system . She explains that healthcare violates every assumption of functional markets: patients can't compare options during emergencies, information asymmetry prevents informed decisions, demand is inelastic when one has an urgent medical issue, and trust is essential to medicine and in direct conflict with profit incentives. Since 1988's Knox-Keen Act allowed for-profit healthcare, private equity has acquired and stripped hospitals, while administrative costs consume enormous resources fighting over payments rather than providing care. She contrasts this with European models like Switzerland and Germany where everyone must participate, insurers must accept all patients, and profit on basic coverage is limited. She celebrates a vaccination success story: HPV vaccines have reduced cervical cancer by 50% over 30 years. The American Cancer Society now endorses self-collected vaginal samples for HPV screening, with an FDA-approved at-home kit from Teal Health allowing women to skip speculums and traditional Pap smears. Current guidelines recommend screening starting at age 25, with testing every five years after a negative result. Dr. Dawn issues a health alert about multiple hospitalizations in Santa Cruz County from foraged wild mushrooms identified incorrectly by phone apps. She describes cholinergic toxicity symptoms: sweating, excessive salivation, pinpoint pupils, and abdominal cramping—signs requiring immediate emergency care rather than waiting it out. She offers follow-up vaccine advice: "go in wet, then sweat." Hydrate before vaccination, then take a hot Epsom salt bath until sweat runs off your face. This helps eliminate adjuvants that cause post-vaccine fatigue and aches, which are often misinterpreted as catching illness from the vaccine itself. Dr. Dawn expresses alarm that Kennedy's reconstituted ACIP nearly voted to eliminate hepatitis B vaccination at birth. She notes infants exposed to infected mothers have 99% infection rates, with half becoming chronically infected and half of those developing terminal cirrhosis or cancer. Testing pregnant women misses infections acquired during pregnancy, and 12-16% of delivering women have no test records. Major insurers have committed to covering birth vaccination through 2026 despite the panel's actions. She offers holiday microbiome advice from researcher Karen Corbin: increase fiber intake through steel-cut oats, whole grain breads like Dave's Killer Bread, beans, apples, and alternative pastas made from lentils or garbanzo beans. Cooking potatoes ahead and reheating creates resistant starch that feeds beneficial gut bacteria, reduces inflammation, and even stimulates natural GLP-1 production. Dr. Dawn reviews research proving health insurance saves lives. When the ACA's Medicaid expansion became optional by state, researchers could compare outcomes, finding 8% lower mortality and 19,000 fewer deaths in expansion states over four years. An accidental IRS experiment—sending insurance enrollment letters to only 85% of penalty payers—showed significantly lower mortality among those who subsequently got insured. Studies of gunshot and auto accident victims found uninsured patients died more often despite receiving identical emergency treatment. She concludes with surprising cancer symptoms: chest pain specifically triggered by alcohol consumption may indicate Hodgkin's lymphoma, as vasodilation activates inflammatory chemicals in affected lymph nodes. Fractures from minimal trauma in people without osteoporosis warrant investigation, as 5% of cancers involve bone. Elevated calcium levels double cancer diagnosis risk in the following year and should prompt follow-up testing.

In this episode of Healthcare Americana, host Christopher Habig talks with neurosurgeon and healthcare policy leader Dr. Anthony DiGiorgio about the growing crisis around prior authorizations. They discuss how complicated approval processes delay urgent care, burden physicians, and put patients at risk. Dr. DiGiorgio explains why smarter, low-friction models such as gold-carding and stronger subspecialty board oversight could help rebuild trust between physicians and payers. The conversation also covers the 340B drug discount program, which Dr. DiGiorgio has testified about before Congress. He explains how a program originally designed to help low-income patients has become a major revenue source for large hospital systems and often increases costs for Medicaid, Medicare, and private insurance. Together, they offer a clear and accessible look at what is broken in these systems and what meaningful reform could achieve.More on Freedom Healthworks & FreedomDoc HealthSubscribe at https://healthcareamericana.com/More on Dr. Anthony DiGiorgioFollow Healthcare Americana: Instagram & LinkedIN

Changing the Oncology Prior Authorization Story with Exact Sciences This Office Hours will highlight Liz Durkin, Manager of Revenue Cycle as she tells the story of Exact Sciences' journey, from pain points to progress, and provide takeaways for other oncology organizations seeking to change the narrative on prior authorization. Find all of our network podcasts on your favorite podcast platforms and be sure to subscribe and like us. Learn more at www.healthcarenowradio.com/listen