Techy Surgeon Podcast

Christian Pean MD, MS
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Decoding AI, health tech & policy transforming healthcare—practical playbooks for clinicians, operators, & builders, from the OR to the boardroom. techysurgeon.substack.com

  1. 22. MÄRZ

    Claude Skills in the Clinic with Hadi Javeed, CTO and serial health tech founder

    Thank you Edward M. DelSole, MD, Danny Goldenberg, Audley Mackel III, Darren Michael, and many others for tuning into my live video with Hadi Javeed! Join me for my next live video in the app. The Skill Is the New Workflow Clinical AI won’t scale through better models. It will scale through better instructions. Interested in deploying clinical AI for your practice, value-based care organization, or health system? RevelAi Health partners with clinics and health systems to build AI workflows for CMS models (TEAM, ASM, ACCESS), care coordination, and clinical operations. We bring the software, the clinical expertise, and the AI-fluent staff to deliver outcomes, not just tools. Schedule a demo or reach us directly at hello@revelaihealth.com. A billboard on Market Street in San Francisco advertises “skills,” the hot new paradigm in AI development. Walk three blocks in any direction and you’ll find someone who can explain, in considerable detail, what a skill is, why it matters, and which framework implements it best. Fly to any hospital in the country and ask the same question. You’ll get a blank stare. This gap between what AI can do and what healthcare is doing with it has become the defining tension of clinical AI’s current era. The models are smart. Sixty-six percent of physicians now report using health AI tools, a 78% increase from 2023. Billions have been invested. Yet nearly four years after the ChatGPT moment, most large health systems still haven’t deployed a single patient-facing AI application beyond ambient documentation. The question is why. And the answer, increasingly, points not to the intelligence of the models but to the architecture around them: the instructions, the context, the workflows that translate raw capability into clinical utility. If you like deep dives on clinical AI and health policy, consider becoming a free or paid subscriber to Techy Surgeon! The Context Problem Nobody Wants to Admit This past weekend, my co-founder Hadi and I sat down for what we’ve been calling Founders Coffee, a live conversation on Substack about what we’re seeing in clinical AI, what’s working, what isn’t, and what comes next. Hadi brings a particular vantage point: before we started RevelAi Health together, he was one of the earliest applied AI engineers at Capital One, building voice AI for banking back in 2016, when the technology was, as he puts it, “not that cool and less practical.” The lessons from that era are uncomfortably relevant now. At Capital One, text-based chatbots found product-market fit. Voice did not. It got dates of birth wrong. It misread credit card numbers. And the core insight that emerged, one that the current wave of healthcare AI companies would do well to internalize, was deceptively simple: people hate chatbots. Not because the technology is bad, but because it fails to deliver unique value. Empathy for the sake of empathy, as Hadi noted, does not work. People engage with AI when it solves their problem. They disengage quickly, permanently, when it doesn’t. “People only would chat to a chatbot if it solves their needs,” Hadi said. “As long as the chatbot is not providing unique value, it does not work.” This observation lands differently in 2026 than it would have in 2016. Today, the models are dramatically more capable. But capability without context is just expensive latency. And in healthcare, the context lives behind a walled garden. The data gravity (the patient charts, the encounter histories, the medication lists, the imaging orders) sits in electronic health records. Epic. Cerner. Athena. And without that context flowing securely into AI systems, even the most sophisticated models are left prompting in the dark. As one survey found, hospitals on Epic had roughly 90% AI usage, while those on smaller EHR platforms averaged just 50%, a disparity that reveals how tightly AI adoption is coupled to infrastructure access. “AI is not the bottleneck,” Hadi argued. “It’s the context that’s the bottleneck right now. Models are pretty smart. But if you cannot get patient chart information securely into AI, you can do only enough.” What an AI “Skill” Means for Healthcare A skill, in this context, is a structured set of instructions that teaches AI how to perform a specific task when triggered by specific conditions. Think of it less as a prompt and more as a protocol manual for a very capable but context-dependent assistant. A prompt says: summarize this note. A skill says: whenever a patient mentions diabetes in an encounter, trigger a downstream workflow. Draft dietary counseling documentation for the staff. Generate a glucose monitoring plan. Prepare a patient-facing message at an appropriate reading level. Format all outputs according to this template. Ground clinical recommendations in these evidence-based guidelines. Hadi framed the clinical application nicely: “Healthcare workflows are very if-then-else logic. If BMI is 30, do this. If they have diabetes, go on this path. And traditionally with software systems, it was so hard to scale healthcare because who’s going to build this if-then-else logic? You’re going to rely on your dev team or maybe Epic consultants, and that takes forever.” Skills collapse that timeline. They translate clinical protocols (the ones that live in binders, in the heads of experienced nurses, in institutional memory that evaporates with staff turnover) into executable AI instructions. And critically, they can be built by clinicians, not engineers. You describe your workflow conversationally. The AI interviews you, iterates, produces the skill. You test it against real examples and refine. Looking to understand Claude’s skills better and see real-life examples? Check out this article below on meta-prompting (full article with in depth walkthrough) Consider the practical applications that emerged from our conversation: a pre-clinic screening skill that reviews a panel of patients before Monday morning, flags missing imaging orders, and surfaces relevant history in a style you specify. A prior authorization appeal skill that ingests a denial letter and produces a structured response matching the format that has historically succeeded with a specific payer. An independent medical examination skill that parses 6,000 pages of records into a timeline of treatment, imaging, and interventions, work that currently requires hours of manual review or a dedicated team. These aren’t hypothetical. We’re building and deploying versions of these at RevelAi Health right now, integrated with EHR data through FHIR resources, with the clinical team able to customize and test skills through a user interface rather than filing engineering tickets. The Compliance Reckoning There’s another thread from our conversation worth pulling. Earlier this month, allegations surfaced that Delve, a Y Combinator-backed compliance startup that had raised $32 million, allegedly generated 494 fabricated SOC 2 Type II reports for its clients. The reports were 99.8% identical boilerplate, with pre-written auditor conclusions filed before companies even submitted their evidence. The auditors Delve marketed as “US-based CPA firms” were traced to offshore operations using virtual addresses. The revelation emerged, almost poetically, because someone left a Google spreadsheet open to the internet. For health tech, this extends beyond a compliance scandal to become an ecosystem problem. Hundreds of companies, including health tech startups handling protected health information, may now hold invalid security certifications. The ripple effects will tighten an already rigorous procurement environment at a moment when health system CIOs were only beginning to open the door to smaller vendors. “You can’t outsource security responsibility,” Hadi said. “If someone is trusting you with their patient data, you have a huge responsibility to protect it. Security and compliance is not a cost center. It’s the most important foundational thing you have to do.” We felt the FOMO ourselves at RevelAi. We went through Vanta, checked every box, invested heavily in governance, and watched competitors claim they completed SOC 2 Type II in three weeks. The temptation to move faster was real. But in healthcare, the “move fast and break things” mantra will also break your company. We’ve watched it happen. Babylon, once valued at $4.2 billion, collapsed in 2023. Olive AI, valued at $4 billion, shut down the same year. The outward appearance of success, it turns out, is often inversely correlated with the rigor underneath. Curious about the tools that I use to put together Techy Surgeon and leverage AI to improve my personal productivity? Check out my article below — The Clinician Founder’s AI Stack. Where the Bridges Are Being Built Not everything is stalled. The interoperability landscape is shifting, unevenly but meaningfully. Athena has emerged as an unlikely leader. At HIMSS 2026, the company previewed an industry-first Model Context Protocol server, infrastructure that allows AI agents to securely access patient chart data in real time. They’re building athenaConnect, an intelligent interoperability layer connecting 170,000 providers serving 20% of the U.S. population. This matters enormously. Model Context Protocol (MCP) is what makes skills practical at scale. It’s the plumbing that lets an AI agent not just follow instructions but access the clinical context those instructions require. When Hadi built a FHIR integration with Cerner’s proprietary APIs, it took him one hour using skill-based development. Previously, that work took two weeks. That’s the offline version, engineers using skills to accelerate code. The online version, where skills execute in real time against live patient data, is coming but isn’t here yet in production. Anthropic, notably, has published a FHIR skill on their marketplace, the

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  2. 10. MÄRZ

    What a $110 Million ACO Actually Looks Like From the Inside: The VBC Operator's Playbook with Sarah Habeeb, MHA

    Thank you Hadi Javeed, Mike Logan, MD, Rachel, and many others for tuning into my live video with Sarah Habeeb! Join me for my next live video in the app. 🗓️ TEAM Connect Virtual Summit — April 30, 2026 A full-day virtual event for hospital leaders navigating TEAM implementation. Speakers from Mass General Brigham, CommonSpirit, AdventHealth, Duke-Margolis, and more. I’ll be presenting on technology-driven care coordination. Early bird registration closes March 20th. → Reserve your spot at apmconnect.com/virtual-summit Sarah Habeeb is System Director for Medicare Value-Based Care Products at Baylor Scott & White Health and co-founder of APM Connect. Connect with her on LinkedIn or at Dr. Christian Pean is an orthopedic trauma surgeon and faculty member at Duke University School of Medicine, core faculty at the Duke-Margolis Institute for Health Policy, and CEO and Co-Founder of RevelAi Health. He writes Techy Surgeon at the intersection of clinical AI, health policy, and care coordination. You can find him at the TEAM Connect Virtual Summit on April 30, 2026. Sarah Habeeb started pre-med at Texas A&M. Then she drove an ambulance, decided she didn’t want to touch patients, and pivoted into health administration. She was sitting in a grad school classroom around 2014 when someone started explaining the Medicare Shared Savings Program—a program that was, at the time, so new that almost no one understood it. She listened, thought it made intuitive sense, and made a career bet on it. That bet paid off. Today, she oversees a program that generates roughly $110 million in annual savings against a CMS benchmark, retaining about 75 cents of every dollar through MSSP’s Enhanced Track. That’s approximately $77 million flowing back out to physicians annually, for a program covering 120,000–125,000 Medicare beneficiaries across an entire major health system. She also co-founded APM Connect—a free community for hospitals navigating mandatory payment models—and will be speaking at the TEAM Connect Virtual Summit on April 30, 2026. I’ll be there too. If you’re a hospital leader trying to make sense of what TEAM actually requires in practice, this event is where you want to be. But before the summit, here’s what an hour with Sarah Habeeb taught me about what value-based care actually looks like from the inside—and what that means for every clinician and operator who is about to be pulled into it, whether they’re ready or not. The Mechanics Most Clinicians Never Learn Risk, in the value-based care sense, is a word that gets thrown around like clinicians should already know what it means. Most don’t. Here’s the actual structure: When you enter a total cost of care contract, a payer assigns you a benchmark—an expected spend per member, per year, risk-adjusted based on patient complexity. If you keep costs under that benchmark, you share in the savings. If you don’t, you may owe back a portion of the overage. The HCC risk adjustment model is how CMS calibrates those benchmarks—it’s essentially a formula that assigns a risk score to each patient based on documented diagnoses. A patient with diabetes, COPD, and heart failure carries a higher score than one with no documented conditions, so their expected cost is set higher. This is where documentation integrity enters the picture. Sarah is direct about it: “If you’re a cardiologist in the heart failure cohort, you need to be sure that you’re getting credit for the risk of your patients because that directly affects your benchmark, which then directly affects your performance in the contract, which affects your Part B adjustments.” Physicians often experience risk coding conversations as administrative irritation—another box to check, another form to sign. The translation layer is missing. If your patients are genuinely sicker than their documented diagnoses suggest, you’re being benchmarked against a population that looks healthier than yours. The comparison doesn’t hold. You look like you’re mismanaging costs when you’re actually managing a high-acuity panel with inadequate documentation. The fix isn’t gaming the system. It’s accuracy. And it starts with clinicians understanding why it matters. What “Operator” Actually Means on a Monday Morning Baylor Scott & White’s ACO is structured around what Sarah calls product owners—people responsible for specific contracts (MSSP, Medicare Advantage risk agreements, direct-to-employer deals) who identify their contract’s cost drivers and design remediation plans. Care management, quality teams, and marketing function as internal vendors to those product owners, not parallel departments chasing their own initiative lists. This sounds obvious until you’ve seen the alternative, which is how most health systems actually operate: multiple teams, multiple initiatives, no clear accountability, and no way to know what’s actually moving the needle. “Because if everybody’s working on a bunch of things and we’re not talking to each other,” Sarah explains, “you can’t figure out what actually made the difference.” The answer for Baylor was forced prioritization. Pick three initiatives. Measure them monthly. Make every stakeholder meeting about those three things. It’s not sophisticated—but discipline consistently outperforms sophistication in operations. The Inpatient Rehab Problem (Which Is Probably Your Problem Too) If you work in orthopedics, you already know what Sarah is about to say. If you don’t, here’s the version that will make you understand it. At Baylor Scott & White, inpatient rehab facility utilization is—in Sarah’s words—”completely unmanaged.” Against Milliman benchmarks, they’re over-utilizing inpatient rehab while simultaneously running near-zero skilled nursing facility use. They’re tracking 55% of hip fracture patients going to inpatient rehab, with a 30-something percent readmission rate that isn’t meaningfully better than SNF. “So was that the right decision? Were they even ready to discharge from the hospital?” she asks. And the honest answer is: often, no. This is the central tension in any ACO that’s embedded in a health system with joint ventures. The health system may have financial interests in keeping patients flowing through high-cost post-acute settings. The ACO’s job is to reduce unnecessary utilization of those same settings. Getting alignment between those two forces is genuinely hard. Sarah doesn’t pretend otherwise. “You have to find a way.” What Baylor has built: a six-to-eight-person team of post-acute care nurses who follow ACO patients through preferred SNFs, with direct EMR access (a requirement of network participation), weekly interdisciplinary calls with each facility, expected discharge dates set within seven days, and a target average stay under 28 days. They track return-to-acute rates and share scorecards with SNF partners quarterly. It’s brute-force infrastructure, but it’s working better than the alternative—which is patients in a black box beyond hospital discharge. From the orthopedic side, I’ve been doing telephone visits at two weeks for all my hip fracture patients, closing them with absorbable sutures so the visit doesn’t require a trip in. Half the time, I’m chasing down whether they’re still in the SNF, whether they’ve bounced back to a different ED, whether anyone has even talked to them since discharge. The clinical relationship doesn’t just end at the door of the facility. But the information infrastructure does. TEAM, ASM, and the Art of Not Panicking Here’s Sarah’s read on the two mandatory models that are consuming health system bandwidth right now: On TEAM: Most hospitals are in year one, upside only, and either don’t know they’re in it or aren’t taking it seriously. This is a mistake. The regional benchmarking structure means your performance is being measured against peers in your geography. “If your regional peers are paying attention and you’re not,” Sarah says, “that affects your benchmark.” By the time year two arrives with downside risk up to 20%, you’ll be starting from behind, not from neutral. Only four hospitals voluntarily opted into TEAM early from prior CJR participation—which tells you something about the expected economics. But ignoring it isn’t a viable option. On ASM: CMS’s Ambulatory Specialty Model is designed as physician-level accountability—NPI-specific participation, measuring cardiologists on heart failure costs and a range of specialists on low back pain. The design is conceptually provocative: specialists competing against each other within the same market for Part B adjustments. The implementation, however, came in lighter than expected. At Baylor, the initial projection was 200–400 physicians selected. The actual list: 51. Nationally, organizations are reporting five or six physicians per system. The cost of the required quality reporting infrastructure may exceed the penalty exposure for smaller lists. Baylor’s response: build a shadow bundle internally. Treat ASM as an MSSP workstream. Develop heart failure and low back pain strategies that produce dividends in 2027 regardless of what the formal adjustment looks like. It’s the right call. These conditions were selected because they represent genuine opportunities to improve care and reduce low-value utilization—guideline-directed medical therapy for heart failure, fewer unnecessary MRIs and high-risk opioid prescriptions for low back pain. The model may be imperfect, but the clinical direction is sound. Where AI Actually Fits—and Where It Doesn’t Sarah told me upfront: she doesn’t spend much of her day thinking about AI. Her ACO generates north of $100 million in savings the old-fashioned way, through data discipline, care management operations, and physician engagement. “I try not to [use AI],” she said.

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  3. The ACO Play Nobody's Talking About: ACCESS and Vibe Coding Clinicians with Dr. Mike Logan of Advocate Physician Partners

    18. FEB.

    The ACO Play Nobody's Talking About: ACCESS and Vibe Coding Clinicians with Dr. Mike Logan of Advocate Physician Partners

    Thank you Doctor Steven Murphy, Edward M. DelSole, MD, The Counterweight Pod, John Lee, Rez, and many others for tuning into my live video with Mike Logan, MD! Join me for my next live video in the app. Fourteen payers representing 165 million Americans just pledged to align with CMS’s ACCESS model by 2028. Venture capital funds are spinning up dedicated vehicles. Digital health companies are scrambling to retool their pitch decks. And the most interesting reaction I’ve heard so far came from an emergency medicine physician running government programs for one of the largest ACOs in the country. “This is potentially a windfall for ACOs.” Not the take you expected. I sat down with Dr. Mike Logan—medical director of government programs at Advocate Physician Partners, one of the nation’s largest clinically integrated networks covering over a million lives and redistributing $170 million in shared savings back to physicians and hospital partners this year—for a live conversation about accountable care, clinical AI, and what happens when CMS decides to pay for outcomes instead of activities. What emerged was a strategic framework that cuts against most of the ACCESS model commentary I’ve seen. The Payment Reality Let’s get the numbers out of the way, because they’ve dominated the conversation. CMS released the outcome-aligned payment amounts for the ACCESS model, and the reaction from the digital health world ranged from disappointment to alarm. For one year of managing a musculoskeletal patient: $180. Behavioral health: $180. Early cardio-kidney-metabolic disease: $360. Advanced CKM: $420. Waive the 20% coinsurance—which most participants will do as a beneficiary engagement incentive—and the MSK track delivers roughly $6 per patient per month in actual cash flow, with another $6 contingent on hitting a 50% outcome attainment threshold. That’s not going to sustain a care team of pharmacists and nurses managing a panel of 500 patients. And if you’re a digital health company that raised venture capital on the promise of this model as a standalone business, you have a problem. But here’s where the strategic calculus shifts entirely if you’re sitting where Mike Logan sits. The View from Inside the ACO Advocate Physician Partners runs one of the larger MSSP programs in the country. They have roughly 5,000 participating clinicians, a clinical integration scorecard built on standard HEDIS measures, value-based care advisers embedded with every practice, and a data infrastructure that lets them identify patients with uncontrolled diabetes, uncontrolled hypertension, and chronic back pain in a matter of hours. The economics Mike described are inverted from how most people are thinking about ACCESS. If you’re already accountable for total cost of care—Part A, Part B, everything—and you’re spending tens of millions annually on care management, outreach, RPM, and CCM programs, then ACCESS doesn’t need to be profitable on its own. It needs to be cheaper than what you’re already doing, while potentially bending the cost curve on your most expensive clinical populations. “If you’re spending tens of millions on legacy RPM and CCM per year, you can do the same thing now potentially for a tenth of the cost,” Mike told me. And then the savings compound: reduced MACE events, fewer hospitalizations, even delaying dialysis by six months represents significant avoided spend for an organization bearing total cost of care risk. This is the strategic insight that most ACCESS commentary misses. The model wasn’t designed for a digital health startup to build a business around $180 per patient per year. It was designed to give organizations already managing populations a low-friction way to extend their reach—and a new reason to invest in the technology-enabled workflows they should have been building anyway, through partners. The Free Salesforce One of the most striking things Mike said was also the simplest: “You basically have a legion of people that will be your salesforce for free.” He’s talking about the existing infrastructure of ACO quality teams, care managers, and value-based care advisers whose job is already to get patients on wraparound services. They’re already running patients through clinical filters in Snowflake. They’re already identifying who has uncontrolled A1c, who has chronic low back pain, who hasn’t had a Medicare wellness visit. For a digital health company partnering with an ACO, the most expensive problem in healthcare—patient acquisition—is already solved. The ACO has the attributed lives, the clinical data, the referring relationships, and the physician buy-in. The referring clinician earns roughly $100 per patient per year for coordinating with the ACCESS participant. The friction is intentionally low. Compare this to the alternative many digital health companies are considering: direct-to-beneficiary advertising and enrollment. Spray-and-pray outreach to Medicare beneficiaries, most of whom will be deeply skeptical of unsolicited health technology offers, all for $6 per month before outcomes reconciliation. The ACO pathway isn’t just cheaper. It’s the only one that makes operational sense at these payment levels. Where to Start: MSK and Behavioral Health Both Mike and I converged on the same conclusion, and it’s supported by the independent evidence. The Peterson Health Technology Institute’s 2024 evaluation found that virtual MSK solutions deliver clinically meaningful improvements in pain and function, with economic evidence supporting broader adoption. Behavioral health digital interventions have a similarly robust evidence base. The CKM and eCKM tracks, by contrast, require devices like continuous glucose monitors—which can run over $150 per month—and involve medication titration decisions that raise real questions about clinical workflows and liability at these payment levels. The MSK and behavioral health tracks also have the lightest clinical complexity. A patient-reported outcome measure for back pain or a PHQ-9 for depression is a fundamentally different data stream than real-time glucose monitoring with titration alerts. And if you pair a musculoskeletal patient who also has comorbid anxiety or depression—and the overlap in these populations is substantial—CMS applies only a 5% discount on the lower-cost track during overlapping months. That’s $360 for managing two conditions in a single patient, with an evidence base that actually supports the intervention. The harder tracks aren’t impossible. But they require a different level of clinical infrastructure, a closer partnership with the referring physician, and probably a willingness to subsidize pharmacist oversight from shared savings rather than from the OAP alone. The RPM Reckoning There’s a harder conversation happening underneath all of this, and Mike didn’t shy away from it. Legacy RPM and CCM companies are facing what may be an existential pivot. The payer pledge commits commercial insurers to adopt outcome-aligned payment approaches by January 2028. If that holds, the arbitrage of measuring a patient’s blood pressure sixteen times a month and billing fee-for-service codes gets replaced by a requirement to demonstrate that you actually improved someone’s blood pressure. “I don’t really see how it’s not going to significantly affect RPM companies,” Mike said. And he’s right. The entire payment philosophy is shifting from reimbursing activity to rewarding outcomes. Companies built around time-based billing for remote monitoring minutes are facing a structural change in how the work they do is valued. But this isn’t purely a story of decline. For RPM companies willing to adapt, the transition period offers an opportunity: partner with an ACO for ACCESS-eligible Medicare patients while continuing to provide traditional RPM services for the organization’s commercial population. It’s a bridge, not a cliff—but it requires moving now, not waiting to see what happens. The Agentic AI Question Mike and I both build things with Claude. He wrote an IRB proposal for a wearable-based sepsis readmission study using Claude that required only two edits from a seasoned researcher. He’s vibe-coded gamified GDMT compliance dashboards for heart failure patients. I’ve built interactive evidence spotlights and care coordination prototypes. The underlying insight we share: generative AI is going to deliver on the promise of value-based care by solving its scalability problem. If you’re an ACCESS participant managing 3,000 MSK patients, you need agentic workflows that can segment your panel, conduct text-based outreach at hours when patients actually respond, collect PROMs through conversational interfaces, and surface the 300 patients who need human attention this week. The math simply does not work if every patient interaction requires a licensed clinician. This is what CMS is betting on. Not that a company will hire an army of care coordinators at $6 per patient per month. But that AI-augmented workflows will compress the cost of engagement to a point where outcome-aligned payments become viable—and that the organizations who figure this out first will have a structural advantage that compounds over the ten-year life of the model. What Remains Unsettled There are genuine open questions. Can ACCESS participants actually hit the 50% outcome attainment threshold across a diverse Medicare population? Will the payer pledge translate into real commercial contracts, or is it aspirational signaling before the OAPs were released? How will CMS handle the inevitable friction when ACCESS care reports land in PCP inboxes alongside the forty other notifications competing for their attention? And perhaps most importantly: will the clinical workflow for the CKM and eCKM tracks prove tractable at these payment levels, or will the MSK and behavioral health trac

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  4. 10. FEB.

    Substack Live: A Conversation with Spine Surgeon Dr. Edward Del Sole on the Quiet Storm Reshaping Musculoskeletal Economics

    Thank you Shaleen Vira, MD, MBA, Audley Mackel III, Nick Lella, Darren Michael, and many others for tuning into my live video with Edward M. DelSole, MD! Join me for my next live video in the app. Techy Surgeon is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. A spine surgeon in rural Pennsylvania just found out his entire practice was selected for a mandatory CMS payment model he’d barely heard of. His Medicare Part B reimbursement—all of it, not just the episodes in question—is now subject to adjustments of up to 9% based on how his low back pain patients do over time. He didn’t opt in. There is no opt out. This is not a hypothetical. It’s happening now to an estimated 8,600 physicians across orthopedic surgery, neurosurgery, pain management, anesthesiology, and physical medicine and rehabilitation. And it’s just one of five policy forces converging simultaneously on spine care economics. I sat down with Dr. Edward Del Sole—spine surgeon, former Geisinger faculty, and author of the new Substack newsletter The Spinal Column—for a live conversation about what’s coming. Ed and I trained together at NYU, back when we were the guys pulling paper charts at Jamaica Hospital. The world has changed. What follows is a distillation of that conversation, supplemented with primary sources, because the policy landscape facing spine surgeons right now deserves more than casual awareness. The Convergence No One Is Talking About Ed’s debut article on The Spinal Column identified five policy forces reshaping spine surgery economics simultaneously. It’s worth naming them together, because their compound effect is greater than any single policy suggests: The Transforming Episode Accountability Model (TEAM)—a mandatory bundled payment program launched January 1, 2026, holding roughly 741 acute care hospitals in 188 markets accountable for surgical episodes including spinal fusion, from admission through 30 days post-discharge. Site neutrality expansion, which continues to compress the payment differential between hospital outpatient departments and freestanding offices. The inpatient-only list elimination, migrating more procedures to outpatient settings. Physician fee schedule erosion—a 33% decline in real-dollar Medicare physician payment since 2001, according to the AMA. And the Ambulatory Specialty Model (ASM)—the one that caught Ed’s practice by surprise. Any single one of these warrants strategic attention. Together, they represent a structural transformation of how spine care is financed, measured, and delivered under Medicare. The Ambulatory Specialty Model: Why the Employment Shield Is Dissolving ASM is the policy that should keep spine surgeons awake at night—and that most haven’t yet internalized. Here’s why. Unlike TEAM, which operates at the hospital level, ASM is evaluated at the individual TIN/NPI level. Every physician. Individually scored. No hiding behind institutional averages. The model launches January 1, 2027, with payment adjustments beginning in 2029, ranging from –9% to +9% of all Medicare Part B payments—scaling to –12% to +12% by 2033. CMS will retain 15% of the total risk pool as built-in program savings, meaning this isn’t budget-neutral like MIPS. The government is designed to win. Ed put it bluntly during our conversation: “If you are a practicing clinician and you’ve been selected for ASM, you would be a fool to push it off even for a minute.” He’s right. The requirements are concrete and operational. For low back pain episodes, clinicians must collect Oswestry Disability Index scores and demonstrate functional improvement over time. They must screen for health-related social needs. They must establish collaborative care agreements with primary care physicians. And they’re scored on episode costs—spending they may have limited ability to control once a patient leaves their office. What struck Ed most—and what should strike every employed surgeon reading this—is what he called the dissolving “employment shield.” Historically, surgeons employed by health systems could rely on institutional infrastructure to manage quality reporting, MIPS compliance, and payment model participation. ASM breaks that pattern. It evaluates you, the individual clinician, regardless of your employment arrangement. As Ed noted: “Even as an employee, you are going to be responsible for the result here.” The Scorecard Gap: What You Don’t Know Is Costing You One of the most revealing moments in our conversation came when we discussed a question from Darren Michael of Forte Analytics: Do physicians actually look at their MIPS scores? Ed’s answer was striking in its candor. At Geisinger, across five years of academic practice, MIPS was a word he “never really heard.” The institution handled it in the background. When he transitioned to private practice, he discovered he needed to personally enroll. And even now, enrolled in MIPS, he described a system that delivers no usable scorecard to his inbox, no integration with his daily workflow, no actionable intelligence about how his patients are actually doing relative to peers. This is the gap that ASM will expose. Under ASM, clinicians will be scored relative to other specialists in their geographic region using decile-based benchmarking. If you don’t know your current performance on episode costs, patient-reported outcomes, and functional status improvement, you’re operating blind in a game where the stakes are real and the clock is already running. The infrastructure to deliver that intelligence—to surface a clinician’s performance on the measures that actually matter and then connect it to guideline-concordant recommendations for improvement—doesn’t exist natively in most EHR platforms. It’s one of the areas where purpose-built tools could make the difference between a 9% penalty and a 9% bonus, but only if clinicians engage with the data before 2029. The Social Determinants Paradox Here’s an inconsistency worth scrutinizing. ASM mandates screening for health-related social needs—an acknowledgment that non-medical factors drive outcomes and costs. Yet the model includes no meaningful risk adjustment for social determinants in its cost benchmarks. TEAM, by contrast, incorporates a community deprivation index decile into its target price risk adjustment. ASM does not. CMS is effectively telling clinicians: identify the social factors affecting your patients, but don’t expect us to account for them when we score you on cost. Ed and I spent time on this tension. The research is clear—patients with adverse social determinants are less likely to complete patient-reported outcome measures, more likely to have ED utilization, and more likely to experience complications that drive episode costs. Penalizing clinicians who serve these populations without adjusting benchmarks accordingly is a policy choice with equity implications that deserve continued advocacy. The practical response, in the interim, is to both screen and act. Organizations like Understood Care—which partners with practices to deploy medical advocates who help patients navigate insurance, transportation, and care access using the community health integration CPT code (G0136)—represent one model for extending the care team without adding burden to the clinician. The Fee Schedule Erosion You Can’t Ignore Behind all these model-specific dynamics sits a structural reality that Ed described in terms any practice manager would recognize: “You cannot really earn your money and your lifestyle by just seeing patients and even just doing surgery anymore.” The numbers support the claim. The AMA reports that Medicare physician payment has declined 33% in inflation-adjusted terms since 2001, while practice costs have risen approximately 39% over a similar period. The 2025 physician fee schedule applied an additional 2.83% cut. The 2026 schedule avoided further cuts but did not close the gap. Medicare physician reimbursement remains the only major healthcare payment category not tied to an annual inflation index. Meanwhile, physician compensation represents roughly 6–9% of total healthcare expenditure. The United States spent $5.3 trillion on healthcare in recent years, concentrated in hospital care and clinical services—categories where spending has continued to grow. Cutting physician pay while administrative and facility costs remain largely untouched doesn’t address the primary cost drivers. As Ed put it: “Trust in God, and all others must come with data.” For private practices, this fee schedule erosion compounds the other four forces. Margins compress from the revenue side while compliance requirements expand from the regulatory side. The strategic calculus for many independent surgeons is increasingly stark: invest in infrastructure to win under value-based models, or accept slow financial attrition. The Clinical AI Question: Where Technology Actually Helps Our conversation pivoted from policy to technology—and to the question every clinician is quietly asking: where does AI actually make a difference in my practice? Ed identified three domains. Surgical planning, where AI-driven preoperative modeling and robotic assistance are already transforming complex spine reconstruction. Patient communication, where asynchronous, AI-augmented care coordination can extend the care team without adding staff. And clinical triage—the “AI-powered front door” that routes patients to the right clinician or care pathway before wasting a visit, a copay, and a referral. That last point deserves emphasis. Ed described a scenario any orthopedic surgeon would recognize: a patient calls their health system complaining of knee pain, gets routed to a joint reconstruction surgeon expecting a 70-year-old with arthritis, and instead encounters a 22-year-

    59 Min.
  5. The Practical AI Company: How Doximity Is Building Clinical Intelligence That Actually Works

    6. FEB.

    The Practical AI Company: How Doximity Is Building Clinical Intelligence That Actually Works

    I first became aware of Doximity GPT the way many clinicians do—through a peer's recommendation. My brother who was an internal medicine resident at the time, (now a hospitalist), was using their AI scribe over a year ago when it was still in beta. “Have you seen this thing? It’s free,” he texted me. That early signal—clinicians organically discovering and adopting a tool—turns out to be core to Doximity’s entire philosophy. When my three-year-old recently may have swallowed a plastic fork tine at a restaurant (he didn’t, thankfully), I found myself cycling through the emerging ecosystem of clinical AI tools: Open Evidence, Doximity GPT, even poison control. Each gave slightly different guidance, each had its own approach to surfacing evidence. It crystallized a question I wanted to explore: in this rapidly converging landscape of clinical AI, what actually differentiates one tool from another? Visual Prompt 1: Split-screen comparison showing a worried father with phone at restaurant table on left (warm editorial illustration style), transitioning to floating interface elements of multiple clinical AI apps (Open Evidence, Doximity GPT, Up-to-Date) on right, with subtle connecting lines between them suggesting the question of convergence—modern editorial style, muted teals and warm grays The User-First Philosophy Dr. Amit Phull, Doximity’s Chief Clinical Experience Officer and an emergency medicine physician, joined the company in 2014 through what he calls “equal parts luck and serendipity.” With a background in computer engineering before medical school, he was part of Doximity’s early physician advisory panel, beta testing their software. At a 2014 medical advisory board meeting, when leadership mentioned they were looking for technically-minded clinicians, Phull essentially raised his hand and said, “What about me?” That origin story matters because it encapsulates Doximity’s approach to product development. “Our process is what differentiates us,” Phull explained. “We have a very broad network of clinicians that comprise the membership of Doximity, and we have the capability to engage those connections and directly ask them how we might improve this product to be most useful in their clinical practice.” The emphasis on “improve” is deliberate. “Our approach is not that medicine was solved last Tuesday,” Phull said, pushing back against what he sees as hubris in healthcare AI. “Anyone who presents any technology as having been the solution to everything that ails healthcare—there’s a healthy amount of hubris there.” From Administrative Assistant to Clinical Reference Doximity GPT’s evolution reflects this user-driven philosophy. In early 2023, the company surveyed hundreds of clinicians about where AI could have the most impact. The answer was overwhelming: administrative burden. Well-designed AI tools, clinicians estimated, could save 12-13 hours per week—”a ridiculous amount of time,” as Phull put it. You could do two additional operations or see 20-30 more patients. So Doximity GPT launched focused on being an administrative assistant—helping with documentation, prior authorizations, and appeals. The “unsexy part of AI,” Phull called it, acknowledging data showing that for every hour of clinical work, the average clinician does two hours of “other stuff.” But then something interesting happened. Despite being fashioned as an administrative tool, a significant percentage of queries were actually clinical reference questions. Users were asking for clinical decision support. Visual Prompt 2: Flow diagram showing the evolution of Doximity GPT—left side showing “Administrative Burden” (prior auths, documentation, appeals) with bar graph showing 12-13 hours saved, flowing through user feedback loop in center, emerging as “Clinical Reference” (clinical questions, decision support, peer review) on right—clean modern editorial style with flowing connection lines At their March 2025 medical advisory board, Doximity floated the idea of leaning into clinical reference. Advisors made their requirements clear: HIPAA compliance, instant lookup capability, peer review, and transparent citations. By summer’s end, Doximity had acquired Pathway, a company co-founded by clinicians that had built a massive semantic graph of medical knowledge—guidelines, peer-reviewed drug monographs, and interconnected citations that could interface with large language models to improve reliability. “In about nine weeks, we did nine months or nine years worth of work,” Phull joked. They integrated Pathway’s entire tech stack and re-released Doximity GPT with clinical reference capabilities. The Convergence Question This brings us to the elephant in the room: convergence. UpToDate, Open Evidence, Glass Health, Expert AI—they’re all racing toward similar functionality, all surfacing citations, all claiming to reduce hallucinations. What differentiates them? Phull’s answer was nuanced. He positioned Doximity as occupying the middle ground in a spectrum. On one end sits UpToDate—humans creating consensus, then layering AI on top. On the other end are tools that lean more heavily into pure AI capabilities. “We’re kind of right in the middle,” Phull explained. “We have technology that enables us to move at a speed that the UpToDate process probably could not. And we also have this human layer—a very broad and extensive network of folks who have domain expertise.” The key difference is the “order of operations”—technology for speed, human expertise for verification, but sequenced differently than traditional approaches. This philosophy manifests in PeerCheck, an initiative Doximity announced recently, co-chaired by Dr. Eric Topol and Dr. Benjamin (readers can learn more at the Doximity blog). The program leverages hundreds of thousands of Doximity members who are primary authors of cited literature, bringing them “behind the curtain” to verify AI representations of their own work. “When there are areas that a pure technology might give variable answers—you ask the same question 100 times and get some variation—we can solve for some of those problems by maintaining human expertise right at the center of clinical decision making,” Phull said. The Patient-Facing Question Perhaps the most interesting tension in clinical AI is whether these tools should be patient-facing. As Phull noted, “Even if we didn’t want to put these tools in the hands of patients, they’re gonna be in the hands of patients.” He trained in the “WebMD or Dr. Google generation,” where patients arrived armed with information and pre-formed decisions. But Doximity’s answer is “not yet”—and maybe never. “The way we’re structured as a company, we’re not inherently patient-focused,” Phull explained. Their focus remains on empowering clinicians alongside their patients. They’ve made docs.doximity.com publicly accessible (with caps after a few queries), primarily to make it easier for clinician members to access. “Our summary position is not that we’re trying to make Doximity GPT restricted from patients. It’s just not our personal point of focus as a company—at least not yet,” Phull said. “We hope that the more empowered we make our clinician user base to leverage these tools to advance patient care, the patients are gonna be right along with them for that ride.” Visual Prompt 3: Conceptual illustration showing clinician and patient together looking at screen/interface (suggesting collaborative use rather than separate patient tool)—warm editorial style showing partnership, with subtle Doximity interface elements floating nearby, modern healthcare setting Beyond the Hype: Practical AI What stuck with me most from our conversation was Phull’s repeated emphasis on “practical AI”—technology that’s actually usable in the real world, not just impressive in demos. This means navigating health system approvals, ensuring HIPAA compliance, building EHR integrations, and maintaining trust. “The technology is just the technology,” Phull said. “It’s not gonna show up and suddenly be operating on your patients. The way this technology actually does anything is clinicians participating in its development and its deployment. That’s where we think we win. And frankly, it’s not even about winning—it’s about making sure healthcare doesn’t miss this boat.” When I asked what we should be excited about on Doximity’s roadmap, Phull reframed the question. “Viewing artificial intelligence as a product unto itself permits folks to maybe wrap their mind around it in one way, potentially get caught up in hype around it, and think about it as this wholly separate element. The actual unlock of Doximity GPT is infusing its capabilities into the entire ecosystem we’ve built for clinicians over the last fifteen years.” That ecosystem includes one of the most broadly used telemedicine platforms in the United States, a comprehensive news and content machine, and fundamentally, a network that connects clinicians across the healthcare system. AI woven through that infrastructure, rather than sitting as a standalone product, represents Doximity’s vision. The Real Differentiator As clinical AI tools converge on similar features—citations, peer review, specialty-specific training—what actually matters may not be the technology itself, but the process of development and the infrastructure for deployment. Doximity’s advantage isn’t just in what they’ve built, but in how they’re building it: listening to hundreds of thousands of clinician users, iterating based on real-world usage patterns, and integrating AI into workflows clinicians already trust. The race isn’t really to build the smartest AI. It’s to build the AI that clinicians will actually use—and that patients will actually benefit fr

    41 Min.

  6. 16. JAN.

    Knowtex’s CEO Believes Ambient AI Is Healthcare’s Next Operating System—Here’s Why

    Knowtex is a 2022 women-founded company led by Stanford AI scientists that is headquartered in San Francisco, building ambient clinical intelligence to transform how clinicians capture and use medical information. Designed to be EHR-agnostic, specialty-specific, and deeply integrated into workflows, Knowtex enables providers to generate complete, accurate notes, codes, and orders in real time. By combining clinical-grade AI with enterprise-grade security and speed, Knowtex helps health systems and providers reclaim time, reduce burnout, and deliver comprehensive patient care.Knowtex is backed by Y Combinator, Amazon Web Services (AWS), the UCSF Rosenman Institute, and MedTech Innovators among others. To learn more, visit www.knowtex.ai Recent Press Release on their rollout in Kansas below as well! Knowtex Successfully Launches Ambient Clinical AI at VA Kansas City Site, Advancing Multi-Phase Rollout Across VA Primary Care This is a founder story that announces itself through a single anecdote. Caroline Zhang, CEO of Knowtex, tells hers about a 75-year-old oncologist who had spent his entire career resisting technology. “He had never used a scribe,” Zhang told me during a recent conversation at JPM Healthcare Conference. “He would print out twenty pages of paper every day for chart review and pre-charting. He liked to write up the notes too. He saw notes as his legacy.” His verdict on ambient AI? “I’ll have to die before this technology can help me.” Six months later, that same physician was using Knowtex for every patient visit—going from fifteen patients daily to over twenty—and spending his reclaimed hours reading clinical research papers rather than documenting encounters. His new position: “Do not take it away from me.” The conversion of a single skeptic might seem like thin evidence. But it captures something essential about where clinical AI has landed in early 2026: the technology has crossed from “promising but unproven” to “infrastructure I cannot practice without.” And Zhang believes we’ve only glimpsed the beginning of what ambient technology can become. Enjoy this article and podcast? Consider becoming a free or paid subscriber to Techy Surgeon! The Operating System Thesis Knowtex occupies an increasingly crowded market. Ambient scribes—tools that listen to clinical encounters and generate documentation—have become healthcare’s fastest-adopted AI category. Abridge recently raised $300 million at a $5.3 billion valuation. Nuance DAX, now owned by Microsoft, has deployed across thousands of sites. Epic announced native ambient capabilities at its 2025 User Group Meeting, sending a shiver through every startup in the space. Against this backdrop, Zhang makes a distinction that shapes how she thinks about the market: scribing is a feature; ambient is a platform. “When it comes down to it, I think workflows stem from the doctor-patient visit,” she explained. “Even intake and everything else…we want to work in the center of everything.” If you enjoy reading founder stories and staying on the cutting edge of where clinical AI and health policy are going, consider becoming a paid or free subscriber to Techy Surgeon. The argument is that whoever captures the clinical conversation captures the most valuable data in healthcare. Not claims data, which is downstream and filtered for billing. Not chart data, which is shaped by documentation requirements and time constraints. The actual conversation—what the patient said, how the physician responded, the nuances that never make it into a structured note. “We have over a million clinical, real clinical conversations in our dataset,” Zhang noted. “That’s a novel dataset, and that informs the rest of the product and workflow design.” The vision extends beyond documentation. Zhang describes a “voice AI operating system”—a command center that can surface fifteen years of patient history in a face sheet, alert clinicians to clinical trial eligibility mid-visit, complete coding and orders within thirty seconds of encounter end, and push everything back into the medical record for downstream billing and quality teams. “It really becomes the central focal point for upstream and downstream workflows,” she said. “That can eliminate the reliance on EHRs that historically have been the data repository, the billing system, the reminder, the alert—all those types of things. Ambient can abstract all that away.” The VA Breakthrough The scale of Zhang’s ambition became tangible in October 2025, when Knowtex was selected alongside Abridge as one of two ambient AI vendors for deployment across the Department of Veterans Affairs—170 medical centers and 1,193 outpatient clinics serving 9.1 million veterans. The contract, valued at $15 million for Knowtex’s portion, represents more than revenue. It represents federal validation for a company that emerged from Y Combinator’s Summer 2022 batch with modest seed funding. And it required solving a problem that has defeated larger, better-funded competitors: integrating with CPRS, the VA’s notoriously antiquated electronic health record. “The VA and the government side—they’ve allocated CTO resources, so their team has been a true partner in that sense,” Zhang said. “That’s also how we approach integration as a whole. If we can work with the provider and we can work with the health system and they are willing to think beyond EHR limitations, then we can build out API connectivity, FHIR API connectivity, HL7—all the things.” The subtext is important. CPRS, the legacy VistA system, is not known for its interoperability. That Knowtex achieved integration where others have struggled speaks either to unusual technical capability or unusual government partnership—likely both. Zhang frames it as a product philosophy rather than a technical achievement. “I would recommend to other folks—still makes sense to be in EHR marketplaces. We’ve worked with Epic, Cerner, Athena, OncoEMR, Flatiron. We definitely have earned our stripes in EHR integration. But we believe there’s more that you can do with innovating directly with the provider.” The Metrics That Matter When I asked Zhang about the KPIs she tracks internally and shares with health system buyers, the list was specific: Technology performance: 97.3% medical accuracy for audio processing, voice activity detection, and custom speech recognition. Over 99% accuracy for medical entity extraction—the translation of spoken information into notes, coding, and orders. Clinician outcomes: Two hours saved per day for adopters. A 29% decrease in “pajama time”—the evening and weekend hours physicians spend completing documentation—documented through internal Epic Signal data. Financial impact: Over $92,000 per month in additional revenue capture at implemented sites, primarily through surfacing ICD-10 codes and evaluation and management suggestions that busy clinicians would otherwise miss. Adoption: Above 80-90% utilization across specialties, which Zhang considers the leading indicator. “Initially, when you roll out AI, it’s a lot to tell the hospital stakeholders that you’re going to get XYZ results for your system—that’s just not something that we can guarantee for an organization. But when you see good utilization, then we hear all the happiness and the time saved and the burnout reduction.” The University of Rochester Medical Center case study, published in February 2025, offers independent verification of similar claims. URMC physicians reported reducing documentation time from ten hours weekly to two hours weekly. The revenue capture calculation—16,000 appointments monthly, 10% undercoding rate, approximately $58 revenue leakage per appointment—arrives at figures consistent with Zhang’s claims. The First Customer The conversion of the 75-year-old oncologist took six months. Zhang’s team was developing the product in his clinic, iterating on feedback from a user who represented the hardest possible case. “Someone who delivered such quality of care but in his lifetime told us that the EHR had only increased his workload,” Zhang recalled. “He didn’t feel like he was operating at his competency level. He was spending two to three hours, four hours every night, just working on documentation.” The breakthrough wasn’t a feature or a demo. It was sustained exposure to technology that actually worked—that didn’t require him to change his workflow but rather eliminated the parts of his workflow he had always resented. “I think that’s the biggest win we can have with anybody,” Zhang said. “Now our technology is something that is a daily reliance in your life and that is adding value and that is making the joy of medicine possible for you again.” The observation echoes across ambient scribe implementations: physicians often don’t realize how dependent they’ve become until the tool is unavailable. The ultimate test of product-market fit may be whether users panic when it goes down. The Patient-Facing Question Our conversation turned to a topic generating considerable anxiety in clinical circles: patient-facing AI. OpenAI launched ChatGPT Health in early January 2026, offering medical record integration through b.well and enterprise rollouts to HCA Healthcare, Cedars-Sinai, and Stanford Medicine. Anthropic followed days later with Claude for Healthcare, emphasizing HIPAA-ready infrastructure and partnerships with Banner Health, Sanofi, and Novo Nordisk. Both products target patients directly, positioning AI as a first-line resource for health questions, a development that evokes memories of “Dr. Google” but with vastly more sophisticated capabilities. Zhang’s response was measured but firm. “I’m going through some of a journey here too,” she acknowledged, describing a personal shoulder injury that led her to run her own MRI

    44 Min.

  7. 5. JAN.

    Do Ambient Scribes Really Pay Off?

    We’re taking a short break from health policy and the 12 days of ACCESS to rehash a clinical AI story. In 2025, ambient scribes proliferated and they got squeezed as well. I anticipate as native EHRs roll out more of their ambient scribes, the market is going to contract and contort in interesting ways. Ambient scribes have become the default “first use case” for clinical AI.Vendors promise hours back to doctors, reclaimed evenings, and a future where the note writes itself while we focus entirely on the patient. A recent STAT News piece pulled together the best evidence we have so far on whether that vision is actually coming true. I turned it into a short Techy Surgeon video explainer you can watch above (love making these videos, it’s just very gratifying), but I also wanted to lay out the numbers here. The short version: ambient scribes don’t yet deliver the clean financial ROI many people imagine…but they DO seem to help clinicians feel less crushed by documentation. And that matters. Enjoy articles on clinical AI, health policy, and venture capital? Subscribe to Techy Surgeon to receive new posts and support this work. What the studies show on “time saved” Across the early randomized and observational studies, the story is remarkably consistent: * In a randomized trial at UCLA, clinicians using AI-powered scribes saved on the order of 20–30 seconds per visit compared with usual care. * In a study from Wisconsin using Abridge, ambient documentation reduced total documentation time by about 22 minutes per day. Those are not trivial numbers, but they are also not the “two hours back every clinic day and an extra visit to squeeze in” narrative that dominates marketing decks. That’s a big, sobering gap on expectations if you’re a CFO evaluating ROI purely on time savings. So why are clinicians (and investors) still excited? Because something else is showing up consistently in the data: Even when the time savings are modest, burnout scores improve. In these early studies, clinicians using ambient scribes report: * Less emotional exhaustion * Lower mental load from documentation * A generally better experience of their clinic day The hands on the clock don’t move much, but the experience of work does. That’s hard to price in a traditional ROI model…and impossible to ignore if you’re actually practicing medicine. The missing layers for true financial ROI If ambient scribes only replace human scribes or take a small bite out of after-hours charting, the math is shaky. For these tools to generate clear financial ROI, they’ll need to plug into a broader automation workflow: * Upstream: smarter intake, templated plans, and structured data capture. * Downstream: automatic coding, quality reporting, and care coordination tasks triggered from the note. * Across the continuum: reuse of the same structured data for value-based programs (ACCESS, TEAM, PRO-PMs, etc.). Right now, most ambient scribe deployments still stop at “nice note, slightly faster.” The next wave of use cases must look more like “note → data → automated work that someone used to do manually.” Emotional ROI is still ROI As a surgeon and founder, I’m wary of tools that are all story and no signal. But I love my ambient scribe. And I’m also honest about the fact that feeling less ground down by documentation has its own incredible value: * It can help keep people in practice. * It can make full clinical days sustainable. * It can create the mental space needed to actually engage with patients, learners, and quality improvement. If a tool barely moves the clock but helps clinicians feel less burned out, I wouldn’t call that a failure. I’d call it baseline value, and then challenge the ecosystem to build the automation around it that makes the financials work too. 🎥 Video explainer:I walk through these studies and the ROI question in more detail in the Techy Surgeon explainer video embedded above. If you want to see how I make these video explainers in under an hour using Gemini 3 pro, Claude, and CapCut, consider becoming a paid subscriber to get access to my Techy Surgeon tutorials! If you’re experimenting with ambient scribes in your clinic or health system, I’d love to hear what you’re seeing…both on the clock and in how it feels to practice. Techy Surgeon is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit techysurgeon.substack.com/subscribe

    1 Min.

  8. 26.12.2025

    How ACCESS Restructures Medicare Payment: Outcome Aligned Payments

    Part 2 of a 12-part operator series on the CMS ACCESS Model Today, we’re covering how ACCESS fundamentally restructures Medicare payment for chronic disease management. This is truly a different financial architecture than fee for service. Here’s how the mechanics work. The 50% Withhold Structure The payment model uses a split disbursement approach with backend reconciliation. During the 12-month care period, CMS distributes up to 50% of the total annual Outcome-Aligned Payment (OAP) through quarterly installments. You submit monthly claims using track-specific G-codes to your Medicare Administrative Contractor (MAC), which processes them as “zero-paid.” The Innovation Payment Contractor (IPC) then issues actual payments quarterly based on validated claims. The remaining 50% sits in escrow until the care period concludes. This isn’t arbitrary—it creates the financial mechanism for outcome accountability while providing operational cash flow during active care delivery. Clinical Outcome Adjustment: The Performance Threshold At reconciliation, CMS calculates your Outcome Attainment Rate (OAR)—the percentage of completed 12-month care periods where patients met all required outcome measure targets for that track. This gets compared against the Outcome Attainment Threshold (OAT). In Year 1, the OAT is 50%. If your OAR equals or exceeds 50%, you receive full payment with no adjustment. If your OAR falls below 50%, you receive proportional payment calculated as (OAR ÷ OAT) × full OAP amount. Example: If 40% of your completed care periods met all outcome targets: * OAR = 40% * Calculation: 40 ÷ 50 = 0.80 * You earn 80% of full OAP * Clinical Outcome Adjustment = 20% reduction The adjustment caps at 50% reduction. If only 20% of patients met outcomes (which would yield 40% payment under the formula), you still receive 50% of the gross OAP. However, consistently performing below minimum thresholds subjects you to termination under the Participation Agreement. The OAT will increase in subsequent participation years beyond 50%, creating a ramp that balances initial accessibility with long-term accountability. CMS will publish the year-by-year OAT schedule in 2026 before the first application deadline. Substitute Spend Adjustment: Preventing Duplicative Care The second adjustment mechanism addresses care coordination and duplicate spending. CMS calculates your Substitute Spend Rate (SSR)—the percentage of aligned patients who did not receive specified substitute services from other Medicare providers for the same qualifying condition during their ACCESS care period. Each track includes a defined Substitute Spend List identifying services that represent new care initiation for the same diagnosis. For eCKM/CKM, this includes ambulatory blood pressure monitoring setup (CPT 93784, 93786, 93788, 93790), remote physiologic monitoring device setup (99453, 99473), diabetes self-management training (G0108), intensive behavioral therapy for cardiovascular disease (G0446) or obesity (G0447), medical nutrition therapy initial visits (97802), and MDPP enrollment (G9880, G9881, G9886, G9887). For MSK: physical therapy evaluations (97161-97163), occupational therapy evaluations (97165-97167), and remote therapeutic monitoring setup (98975). For BH: digital health medical treatment device supply (G0552-G0553), initial individual psychotherapy (90832-90834, 90836-90838), and RTM setup (98975). The Substitute Spend Threshold (SST) in Year 1 is 90%. If your SSR equals or exceeds 90%, no adjustment. Below 90%, proportional payment as (SSR ÷ SST) × full OAP. Example: If 85% of your patients avoided substitute services: * SSR = 85% * Calculation: 85 ÷ 90 ≈ 0.944 * You earn 94.4% of full OAP * Substitute Spend Adjustment = 5.6% reduction This adjustment caps at 25% reduction. The Single Adjustment Rule Critical detail: CMS applies only the larger of the two adjustments during semi-annual reconciliation. If you face a 20% Clinical Outcome Adjustment and a 5% Substitute Spend Adjustment, you lose 20% total—not 25%. This prevents compounding penalties while maintaining accountability across both dimensions. The reconciliation occurs twice per year, assessing all patients whose 12-month care periods ended during the trailing 6-month window. CMS nets the reduction against your withheld payments. Any remaining balance owed to you gets released through the next quarterly payment. If additional recovery is needed beyond withheld amounts, CMS may offset against future payments or initiate standard Medicare overpayment recovery procedures. Enjoy timely and insightful deep dives on health policy and clinical AI? Subscribe to Techy Surgeon for more! Payment Rates and Rural Adjustments CMS hasn’t published specific dollar amounts yet—those come in 2026 before the April application deadline. What we know: payment rates vary by track, with higher rates for Initial Periods (first 12 months or when baseline measures aren’t at target) and lower rates for Follow-On Periods (maintenance care for established patients or those starting at target). For eCKM and CKM tracks specifically, the payment includes expected device cost for a cellular network-connected blood pressure cuff used for condition management and outcome reporting. Rural patients in these tracks receive a fixed add-on payment to account for higher distribution costs. Other tracks don’t include device costs and therefore have no rural adjustment. Rates may update annually based on Medicare Physician Fee Schedule (PFS) updates, regulatory changes, or an efficiency adjustment tied to the Medicare Economic Index (MEI) productivity adjustment percentage. Multi-Track Discount When a beneficiary enrolls in multiple tracks with the same participant, CMS applies a payment discount to the total OAP amount. This reflects administrative and operational efficiencies from delivering integrated care—shared intake, unified patient records, coordinated communication with PCPs. The specific discount percentage will be published with payment rates in 2026. Cost-Sharing Policy Participants must establish a uniform cost-sharing policy per track: either collect standard Medicare Part B cost-sharing or forego collection under the CMS-sponsored model patient incentive safe harbor (42 CFR § 1001.952(ii)(2)). This policy must apply consistently to all aligned beneficiaries within each track and can only be revised with CMS approval on a go-forward basis. If you elect to collect cost-sharing, you must clearly disclose expected beneficiary payment amounts before enrollment. Most participants are expected to waive cost-sharing as a beneficiary engagement strategy, but the choice remains at the organizational level. What the Payment Enables This structure creates financial predictability—recurring revenue based on panel size—while tying full payment to measurable outcomes. The quarterly disbursement provides operational cash flow. The split threshold approach (50% for outcomes, 90% for substitute spend) balances accessibility for new participants against long-term accountability for results and coordinated care. Unlike shared savings models where attribution complexity makes condition-specific savings hard to isolate, ACCESS directly measures clinical outcomes and duplicative spending. Unlike fee-for-service where reimbursement requires specific billing codes, ACCESS gives flexibility in care delivery modalities as long as outcomes are achieved. The model explicitly accommodates technology-enabled care—virtual visits, remote monitoring, asynchronous engagement, FDA-cleared digital therapeutics—under appropriate clinical oversight. Payment doesn’t depend on time-based billing or in-person visit requirements. It depends on moving the clinical measures. Next module, we’ll cover the six-stage beneficiary journey from discovery through reconciliation. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit techysurgeon.substack.com/subscribe

    3 Min.
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Decoding AI, health tech & policy transforming healthcare—practical playbooks for clinicians, operators, & builders, from the OR to the boardroom. techysurgeon.substack.com

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