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Vital.io: Guides patients during an ER or hospital stay. Advanced AI predicts wait times,…
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How to do a LinkedIn AMA
How to do a LinkedIn AMA
With an increase in posts & content from business leaders, LinkedIn has become a place to learn from the best. Only…
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Aaron Patzer shared thisBest way to recruit: create such a compelling product that when someone uses it they join the company. So happy to have Ryan on the team these last two and a half years. He now leads all Client Success, Sales and Commercisl operations. Missionaries over mercenaries.Aaron Patzer shared thisPatient experience is often talked about as a metric. For Ryan Randall, it started as a memory: sitting alone in a hospital parking lot in October 2020, waiting for word on his 15-month-old son during a seizure, with no updates and no way to know what was happening inside. A year later, at an Emory ER for his own care, Ryan encountered Vital. Seeing real-time updates and plain-language explanations delivered straight to his phone, he immediately thought back to that night with his son, and how differently it would have felt with that same information instead of silence. Now Vital's VP of Commercial, Ryan shares the personal story behind his work, and why he believes no patient or family member should have to wait in silence. Learn more about Ryan's story: https://lnkd.in/gHzeVwn8
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Aaron Patzer shared thisToday, 5,000 patients in the US will walk out of an ER with a cancer or aneurysm that radiologist found, but that wasn't communicated. Every single day. Health systems know this, but feel powerless to do anything (too much effort). There's a communication gap in modern healthcare.Aaron Patzer shared thisHappy to share my latest guest article for Healthcare IT Today related to a product, VitalGuard, we have been working on for the past two years. Let's fix the hot potato issue of communicating missed findings in the acute care setting and make sure patients and understand what is happening to their health. https://lnkd.in/gffhK2_4 Thank you John Lynn for the opportunityOne in a Hundred: The Incidental Finding Problem Emergency Medicine Can’t IgnoreOne in a Hundred: The Incidental Finding Problem Emergency Medicine Can’t Ignore
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Aaron Patzer shared thisAlmost none of my CEO friends have visited me in New Zealand (lived here 10+ years). Fortunately WSJ was kind enough to help my social life with a feature piece on our farm: https://lnkd.in/eYzRNnNK If we're friends, colleagues, or someone I've known for ages, come, I'll host. You'll feel healthier for it.After Selling His Company, He Could Have Lived Anywhere. He Chose New Zealand.After Selling His Company, He Could Have Lived Anywhere. He Chose New Zealand.
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Aaron Patzer shared thisCourtney's story is tragic, but all too common - cancer or pre-cancer found years in advance, but never disclosed. A few years back, Courtney left hospital after complications from Crohn's. She was happy a bowel bleed was under control and she could go home. But lingering on page 4 of CT scan was an indication of a possible cancerous lump. No one disclosed it. No one followed up. And sadly, it was a cancer. We built Vital Guard because 60-70% of "incidental findings" (ones you weren't looking for) are never disclosed or followed up on - mostly because hospitals simply didn't have the manpower to do anything. Vital automates everything, from AI that detects neoplasms and aneurysms, to searching through doctors notes and follow-up appointments for signs of disclosure and follow-up, to acknowledge and education for the patient, to automating clinician outreach for the highest risk cases.Aaron Patzer shared thisOur CEO, Aaron Patzer, built Vital Guard because of someone he lost. Her name was Courtney. She was 29. A finding on her scan was documented, but no one told her about it. By the time it was discovered, it was too late. This is the story of why Vital Guard exists. Watch it here: https://lnkd.in/gg393Tz2
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Aaron Patzer shared thisWe've already saved lives with this product. 60-80% of neoplasms & aneurysms found "incidentally" during an ER visit are never disclosed to the patient. For example, during our pilot, we found a Bosniak 4 lesion the patient was never aware of. Our AI found it in radiology, then scanned doctors notes & discharge to see if it had been disclosed. Since it was serious, it was marked for human outreach (vs. simple text message with education & acknowledgement). Patient started chemo literal weeks later. Other companies (Eon is likely the best) identify incidentals. But no one is checking whether they've been disclosed to patients, and automating the follow-up. In our pilot, a single radiology PA can manage an entire region of hospitals. 1 FTE instead of an after-care team you don't have funding for. This is how AI should be used.Aaron Patzer shared this🚀 Introducing Vital Guard™ Vital Guard is an AI-driven solution that protects patients and physicians by ensuring incidental findings don't fall through the cracks. ✔ Automatically prioritizes uncommunicated findings ✔ Supports closed-loop patient outreach ✔ Reduces clinical risk while saving staff time Because discovering a finding isn't enough — communication matters. 👉 Learn more: https://lnkd.in/gCMVzTqy #IncidentalFindings #PatientSafety
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Aaron Patzer shared thisOn an health system AI Committee and secretly haven't a clue how AI actually works under-the-covers? We developed tools clinicians, operations and technical people can use. Happy to have worked with individuals from Stanford, MedStar, HCA, CHLA, Hackensack, IU, Matter, and more here. Big tech (won't name names) actually joined us for a hot-minute...but wanted to own all the tools, and all the IP. We want this to be unbiased, non-profit, a standard to be used by anyone with no cost/licensing or ulterior motive.Aaron Patzer shared thisToday marks the launch of the AI Care Standard™ for Patient Communication. AI communicates directly with patients through portals, chatbots, care navigation tools, and automated outreach. Yet healthcare has lacked a clear, consistent way to evaluate whether AI systems meet the same standards expected of clinician–patient communication — until now. The AI Care Standard™ establishes these guardrails. Developed by a select group of health system leaders, patient safety experts, clinicians, and AI practitioners, the Standard defines what safe, responsible patient-facing AI must deliver in real-world care — with clear expectations for accuracy, clarity, transparency, and accountable governance. The Standard is anchored by 10 Core Pillars that define what responsible patient-facing AI must deliver in practice, and an accompanying Evaluation Framework tool that allows organizations to assess real systems against those expectations. Together, the Core Pillars and Evaluation Framework move the conversation from abstract AI principles to practical accountability in real-world care delivery. And it provides a shared reference point the industry can rely on as patient-facing AI becomes integral to everyday care. Built to protect patients. Designed to support clinicians. Created to reduce risk and uncertainty as innovation continues to advance. Learn more about the AI Care Standard™ → https://lnkd.in/g4mJ5DEs #AICareStandard #PatientSafety #PatientFacingAI #HealthcareAI Raj Ratwani PhD Bridget Duffy, MD Terry Adirim, MD, MPH, MBA Steven Collens Beth D. Dev Dash MD Christine Dow Karen Drenkard, PhD, NEA-BC, RN Kathy English Alistair Erskine MD MBA jeffrey Gold MD Carole Hemmelgarn Tina HsinTing Liu,CPA,FACHE Kelly Nye Aaron Patzer Kelsey Powell Jeremy Rogers Marla Sammer MD, MHA, FAAP Janae Sharp FHIMSS Michael A. Rogozinski RN Justin Schrager MD Nicholas Sterling, M.D., Ph.D. Julie Wheelan Adrienne Woods MPH
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Aaron Patzer shared thisToday we launch AICareStandard.com - a set of scoring tools & principles to ensure patient-facing health AI is safe to use. This is an industry collaboration with individuals at MedStar, HCA, Stanford, Highmark, IU, Hackensack Meridian, Children's Los Angeles, Texas Children's, patient safety advocates like Patients for Patient Safety, directors or former directors of AONL, the VA and Cleveland Clinic. This is to be used by health systems and tech vendors alike. Because right now, big tech is in an arms race, almost regardless of patient safety. And at the same time, health systems are notoriously conservative, with chatbots that almost reflexively say "Go to the ER or call 911" for anything. This is a practical middle ground, from the actual experts both building and evaluating healthcare AI in the real world. Use it for internal tools. Insist your vendors use it. Pressure big tech to use it - they initially joined the coalition, then bailed when they couldn't own all of the IP. This is a free, non-profit collective, beholden to no one. Proud of work like this. It will save lives? That's not hyperbole. There are dozens of deaths linked to chatbots: https://lnkd.in/enxpgkbj You wouldn't let an unlicensed clinician practice medicine. And while this isn't a "license" for AI, it's the start of a free-market practice (think Hitrust or SOC2) for everyone in healthcare.
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Aaron Patzer shared thisComing Feb 11th, in collaboration with individuals from MedStar, Stanford, IU, HCA, Texas Children's, Highmark, AARP, patient advocacy groups and more, we'll be announcing guidelines and tools to ensure AI safely interacts with patients. There are too many chatbot deaths/suicides from big tech, and at the same time a too conservative approach by health systems. This sits in the practical, yet patient safe middle.Aaron Patzer shared thisWe would never allow an untrained clinician to provide medical guidance to a patient. And yet AI is already doing exactly that — quietly, at scale, every day. These messages shape understanding, decisions, and behavior. They carry real clinical consequences. Healthcare has clear expectations for humans who speak to patients. It’s time those same expectations applied when AI does. On Feb. 11, that line gets drawn. #PatientSafety #PatientCommunication
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Aaron Patzer shared thisWhen a journalist had an emergency visit…and likes your software so much they spontaneously write an article… Here’s to guiding millions of patients + family through the ER, translating doctor speak notes, and explaining lab results & post discharge steps like a human.Aaron Patzer shared thisThank you to Teta Alim and Healthtech Magazines for capturing the innovation and real-world value our Emergency Department application is delivering at MedStar Health. Serving over 7 million patients across the US, we're committed to transforming how healthcare systems support patients during their most vulnerable moments. Proud to partner with forward-thinking organizations like MedStar! Read the full article here: https://lnkd.in/es4tX-85Improving Patient Communication and Transparency in the EDImproving Patient Communication and Transparency in the ED
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Aaron Patzer liked thisAaron Patzer liked thisImplement AI health monitoring in your app today
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Aaron Patzer liked thisAaron Patzer liked thisIt took me a year to say the sentence out loud: "I can't afford to fail again. I have three kids and a mortgage." The failure it referred to was real. My second startup, BizeeBee, took my entire life savings — the money from the Mint acquisition — plus three years of my life. We raised about $500K in angel funding. Then, right as we launched our second product, scammers started using it for money laundering. I came clean with my investors and my team. I couldn't make payroll. I couldn't raise again. I paid back the people who'd been defrauded by teaching product development courses through Femgineer. I pulled myself out of that hole. But I had time, no kids, and no real commitments. So when I started planning a restart last year, that sentence ran on a loop. I finally told a college friend the whole story on a weekend away in March. She listened, and then pointed out that I'd left the last seven years out of my own accounting — everything I'd built at Femgineer, everything I'd learned in seven years at Apple. The fear wasn't irrational. I really did lose everything once. But it was working from an outdated file. It knew what happened. It didn't know who I'd become since. Three months into the restart, has the business taken off? Not completely — I've got coaching clients and I'm expanding what I offer. And that's enough, because this is also meant to be a period of rest and reinvention. What sentence are you not saying out loud? Full letter in this week's newsletter 👇Moving Forward in This Market When the Fear Won't Go AwayMoving Forward in This Market When the Fear Won't Go Away
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Aaron Patzer liked thisAaron Patzer liked thisChatGPT democratizes access to Medical Knowledge, but it does NOT democratize access to Health Care. Here’s what I mean: Yes, ChatGPT is an incredible AI partner to help patients answer questions, navigate the healthcare system, and heck, even help solve mystery medical problems that doctors fail to fix. All of that is helpful and good! But I can’t help but roll my eyes when Technologists claim ChatGPT is putting doctors out of business. In fact, the better ChatGPT gets, the MORE we will demand of doctors and healthcare systems. Patients will be able to speak with a ChatGPT that is perhaps as smart as their doctor. Patients will know more than ever before because access to Medical Knowledge is no longer gate-kept. From there, patients will ask for more tests. Request more diagnoses to consider. More treatments to explore. But ChatGPT isn’t able to do fully doctor things - e.g. perform surgery, prescribe new medications, make specialty referrals, etc. Unlike a human doctor, ChatGPT can’t simply and immediately act on everything the patient now knows. This is what I mean when access to Medical Knowledge will be democratized, but not access to Health Care itself. Knowledge is power, but only if you can act on it. Sure, patients will have an easier time scheduling appointments - their ChatGPT agent will just do it for them. But the number of available appointments won’t suddenly increase. The number of surgeons able to operate won’t suddenly increase. The number of operating rooms available won’t suddenly increase. The actual delivery of care is highly constrained by a combo of human and physical resources. The only way this changes is if ChatGPT eats away at more of what traditional healthcare labour does - if it automates taking medical histories, symptom questionnaires, prescription refills and more. But I don’t see that happening. I’m NOT saying AI won’t do this - I think some AI will - I just don’t think it’ll be ChatGPT. Any AI that engages in replacement of human clinical labour will likely go through some complex regulatory pathway that won’t make sense for ChatGPT to do. It’s just too niche, too time consuming, too risky, too costly. The AI that replaces what human clinical labour does will be a clinic/hospital that decides to be AI-native from the ground up. We’re already seeing this with companies like Doctronic. Which means the question isn’t “will ChatGPT replace doctors?” Because it won’t. The question is: will incumbent doctors and health systems reinvent themselves as AI-native before new AI-native providers earn the trust and distribution incumbents already have?"
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Aaron Patzer liked thisAaron Patzer liked thisPatient experience is often talked about as a metric. For Ryan Randall, it started as a memory: sitting alone in a hospital parking lot in October 2020, waiting for word on his 15-month-old son during a seizure, with no updates and no way to know what was happening inside. A year later, at an Emory ER for his own care, Ryan encountered Vital. Seeing real-time updates and plain-language explanations delivered straight to his phone, he immediately thought back to that night with his son, and how differently it would have felt with that same information instead of silence. Now Vital's VP of Commercial, Ryan shares the personal story behind his work, and why he believes no patient or family member should have to wait in silence. Learn more about Ryan's story: https://lnkd.in/gHzeVwn8
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Aaron Patzer liked thisAaron Patzer liked thisEvery camera can now be a medical device - including the one in your smartphone. As of today, Shen SDK is CE-certified as a Class IIa medical device under MDR - covering Blood Pressure, Heart Rate, Heart Rate Variability, and Breathing Rate. Class IIa is not a badge you can self-declare. It's clinical trials. A full technical file. An ISO 13485-certified quality management system. And a notified body - TÜV Rheinland - certifying every part of it. When Przemek Jaworski and I started Shen AI, the vision was simple to say and brutally hard to execute: turn any smartphone into a health monitor. No wearable to buy, no cuff to own, no clinic to reach - so it can reach everyone, anywhere at anytime, democratizing access to healthcare. We started in wellness, but the goal was always medical. Today the door opens to regulated healthcare - where measurements available daily actually change outcomes. Shen AI team - I know what this cost. You didn't just build technology, you proved it against the hardest standard there is. I couldn't be prouder. Thank you. This is the threshold. Now we scale. If you're building in telehealth, RPM, or pharma and want medically certified vitals from a camera with highest accuracy and reliability standards - let's talk! More informations here: https://lnkd.in/eSdS9nfv
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Aaron Patzer liked thisAaron Patzer liked thisI tried to price what it would actually take for a regional health system to build and run its own open-source EHR. The modeled answer was about $115 million over five years. What surprised me was not the total. It was the shape of it. The permanent engineering and support team is substantial, but it accounts for only about ~21% of the bill. Infrastructure is tiny by comparison. I went into this expecting an expensive software operation given the headlines on EHR build costs ("ACME hospital budgets 1.4 gajillion dollars for new EHR, sources say"). The largest cost is the one-time conversion itself: migrating data, rebuilding interfaces, running old and new systems in parallel, training thousands of users, and absorbing the productivity loss that comes with changing how an entire hospital works. The model describes something else: a hospital conversion project that happens to involve software. That distinction matters because it also limits how much a platform consortium (of the sorts open software tends to cultivate) can help. The platform can be shared. Most of the conversion work cannot. Part 3 of The Open EHR Experiment is out now. Link to dynamic cost explorer posted below, too! #HealthcareIT #EHR #OpenSource #DigitalHealthThe Open EHR Experiment, Part 3: What Would Have To Be TrueThe Open EHR Experiment, Part 3: What Would Have To Be True
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Aaron Patzer liked thisOne of the biggest questions in healthcare right now isn't whether patients will use AI. It's whether health systems will give them AI they can actually trust. Patients are already asking ChatGPT and other AI tools questions about symptoms, medications, diagnoses, and treatment options. That behavior isn't coming. It's already here. The real challenge for healthcare organizations is figuring out how to meet patients where they are without sacrificing safety, transparency or clinical responsibility. That's why I enjoyed my conversation with Aaron Patzer, CEO and Co-Founder of Vital.io. We talked about what responsible patient-facing AI actually looks like. Not the hype, but the practical work of building tools that patients can trust and health systems can confidently stand behind. This is what we talked about: • Why "safe AI" requires more than a good large language model • The responsibility health systems have when introducing AI directly to patients • How transparency and clinical oversight build trust • Whether industry standards can move faster than regulation • Practical questions every organization should ask before deploying patient-facing AI If your organization is exploring AI for patient engagement, digital experience, or consumer health, I think you'll find this conversation worthwhile. And I'd also love to hear your perspective: What do you think is the biggest barrier to deploying patient-facing AI responsibly today?Aaron Patzer liked thisOur CEO Aaron Patzer joined Chris Boyer for a conversation recorded at the Healthcare Marketing & Physician Strategies Summit in Salt Lake City, discussing the safe and responsible use of AI in patient communications. It's an insight packed discussion with practical takeaways for healthcare marketing and strategy leaders navigating AI adoption. 🎧 Watch the full interview here: https://lnkd.in/gpzrrvUT #Patientexperience #Patientcommunication #Vital #HMPS26Live from HMPS26 | Aaron Patzer: Safe AI Starts with Better StandardsLive from HMPS26 | Aaron Patzer: Safe AI Starts with Better Standards
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Education
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Princeton University
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Was pursuing a PhD, passed my quals, was teaching...but then met Sandy Fraser (former CTO of AT&T / Bell Labs) and Ed Zschau (former Congressman, entrepreneur, HBS professor) and dropped out to begin the entrepreneurs journey...
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Publications
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Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks
Methods Inf Med
Abstract
OBJECTIVE:
To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements.
METHODS:
Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several…Abstract
OBJECTIVE:
To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements.
METHODS:
Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient's reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model.
RESULTS:
Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.731- 0.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN.Other authors -
Patents
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System and method for building a script for a web page using an existing script from a similar web page
Issued US 9,996,441
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Multiple alignment genome sequence matching processor
Issued US 6,983,274
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Feedback cycle detection across non-scan memory elements
US 6,986,114
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Replicant simulation
US 8,161,448
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System and method for building and repairing a script for retrieval of information from a web site
US 9,779,007
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System and method for categorizing credit card transaction data
US 7,840,456
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System and method for providing price information
US 9,286,639
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Temporal replicant simulation
US 7,979,820
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Doug Hayes
Three incredible days at #JPM2026. Same conversation with 10+ health system execs: "We're building our AI strategy. Evaluating vendors. Need to pick the right partners." This sounds familiar. 2009-2012: The EHR decision. After the HITECH Act, health systems spent 18-24 months evaluating Epic vs. Cerner. Consultants. Committees. RFPs. Multi-year implementations. The logic made sense: EHRs were monolithic platforms with huge switching costs. You had to get it right. 2025: The AI decision. I'm watching health systems do the same thing. "AI steering committees." "Strategic AI partner" RFPs. Trying to pick winners. But there's a critical difference: EHRs were a single, massive platform decision. AI in healthcare is dozens of different problems that need dozens of different solutions. Here's what health systems are missing: They're looking for "the AI vendor" when they should be building "an AI portfolio." There's no single AI platform that solves: → Documentation burden (ambient notes) → Radiology throughput (workflow optimization) → Clinical trials matching → Revenue cycle optimization (across specialties) → Clinical decision support These are different problems. Different technologies. Different vendors. Different timelines. Some need FDA clearance and 24-month validations. Some can pilot in 90 days. Some integrate with Epic. Some replace parts of Epic. You can't solve all of these with one "strategic AI partnership”....no matter that Anthropic is saying right now. <ducks> The AI landscape is evolving too fast for monogamous vendor relationships. 6 months ago: GPT-4 for clinical documentation. Today: Ambient listening, agentic workflows, multimodal imaging analysis. 6 months from now: Something else entirely. That's why you need exposure across the whole landscape—not a bet on one vendor. The mistake I see health systems making: Approach 1: "Strategic Partner" Strategy Pick one AI vendor → Enterprise deal → 3-year commitment → Hope they solve everything Result: You're locked in. Technology moves on. You're stuck in vendor negotiations while competitors move faster. Approach 2: "Portfolio Strategy" Deploy capital across multiple companies → Take equity → Co-develop solutions → Maintain optionality Result: You capture upside. You shape products. You're first to new capabilities. You have relationships across the ecosystem. After 20 years building innovation platforms at JPMorgan, Mousse Partners, Blueprint Health, Junto Health, Atlantic Health: In fast-moving markets, portfolio strategies beat single-vendor bets. Healthcare AI is moving fast. The question isn't "Which AI vendor should we pick?" The question is: "How do we build an AI portfolio that gives us the most optionality" If you're thinking about this, let's talk. 📩 douglas.m.hayes@gmail.com ☎️ 215-620-2032 #healthcareAI #digitalhealth #venturecapital #innovation
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Alex Koshykov
This is a big “decision → action” moment in healthcare AI. OpenEvidence (evidence-based clinical answers at point of care) just partnered with Tandem (automates the messy access layer: prescription workflow + prior auth + appeals + affordability + routing). The pitch is simple: if the clinician makes the right call, the system should execute it without a week of paperwork. What’s interesting here is the direction: OpenEvidence started as “medical search + decision support.” Then comes coding. Now—through Tandem—it’s reaching into prescriptions and prior auth, i.e., the part of healthcare that routinely breaks the care plan after the visit. If they can actually connect: evidence-backed recommendation → prescription generated → prior auth submitted (and appealed if needed) → routed to the right pharmacy/site of care → affordability support to reduce abandonment… …that’s not just “AI that answers.” That’s AI that gets care across the finish line. Bigger trend: the race to become an all-in-one clinical AI platform is getting real—and the winners won’t be the ones with the smartest chatbot. They’ll be the ones who reduce the time between “this is the right treatment” and “the patient actually gets it.” https://lnkd.in/dzeeDfYj 🔁 Know someone building or buying workflow AI in healthcare? A quick repost might save them time.
22 Comments -
Stephen Macharia
🌍 Proof of Concept: Standardizing Oncology Care with FHIR. I'm excited to present Oncology Implementation Guide;a proof of concept developed to demonstrate how HL7® FHIR® standards can streamline oncology care. This guide provides a structured framework for capturing, managing, and exchanging oncology data—spanning prevention, diagnosis, treatment, and follow-up—enabling interoperability across healthcare systems. Explore the guide here: https://lnkd.in/ggMugMUj #Kenya #OncologyCare #FHIR #DigitalHealth #HealthTech #Interoperability #CancerCare #ProofOfConcept
5 Comments -
Jonathan Friedman
Too many startups assume: 𝙩𝙝𝙚𝙧𝙚’𝙨 𝙖 𝘾𝙋𝙏 𝙘𝙤𝙙𝙚 → 𝙩𝙝𝙚𝙧𝙚𝙛𝙤𝙧𝙚 𝙩𝙝𝙚𝙧𝙚’𝙨 𝙖 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨. Reality is rarely that linear. Viability tends to unfold in fits and starts, shaped by payer coverage, provider workflows, and the real operational burden behind reliable billing. ⚙️📉 Inspired by Matt Kamen's excellent post on CPT adoption curves & the recent ACCESS discussion, we’re sharing our internal framework: 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐭𝐨 𝐀𝐬𝐤 𝐁𝐞𝐟𝐨𝐫𝐞 𝐁𝐞𝐭𝐭𝐢𝐧𝐠 𝐨𝐧 𝐚 𝐍𝐞𝐰 𝐁𝐢𝐥𝐥𝐢𝐧𝐠 𝐂𝐨𝐝𝐞 It goes deeper than payment policy — covering clinical and documentation requirements, enrollment and service mechanics, operational readiness, and the maturity of the CPT pathway. 🧠📋 If you’re evaluating a CPT-driven business model — or working with someone who is — comment “𝘾𝙋𝙏” below + DM me & I’ll share the document. 📄➡️ #startups #HealthcarePolicy #HealthTech #CMMI #ACCESSModel #Medicare #DigitalHealth
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