Podcasts about EHR

Your ER doctor has about 25 minutes to figure out your medical history and decide what to do next. Hamed Husaini, an emergency physician and physician executive, explains why so much of that data never reaches the bedside and what AI can do about it. This episode is based on his article "AI in health care data management: Curing the EHR overload," published on KevinMD. You will hear why records from skilled nursing facilities, primary care, and home health rarely get read in time, why duplicate medications and missed end-of-life directives slip through, and how a one-page AI synopsis pushed into the native EHR before you walk into the room changes what the next 25 minutes look like. Hamed argues the bottleneck is not data volume; it is the pull model that asks busy clinicians to fetch records they never have time to read. If the system already feels like it should know your records and still doesn't, this episode names why and what changes when the data starts flowing the other direction. True team-based care starts with you. At ChenMed, we believe the best way to care for patients is to change the way we practice medicine. When you join our team, you are empowered to lead. We've moved beyond the traditional volume-heavy model to focus on true value-based care. Our model gives you the time and resources to manage complex cases and make a lasting impact on your community. Whether you are applying for a primary care physician, nurse practitioner, or medical director position, you will feel supported by a physician-led culture that understands your challenges. Your dedication doesn't go unnoticed here. You'll be rewarded with a career that offers both professional fulfillment and a better quality of life. Visit ChenMed.com/physicians-KevinMD to learn more. VISIT SPONSOR → https://ChenMed.com/physicians-KevinMD Partner with me on the KevinMD platform. With over three million monthly readers and half a million social media followers, I give you direct access to the doctors and patients who matter most. Whether you need a sponsored article, email campaign, video interview, or a spot right here on the podcast, I offer the trusted space your brand deserves to be heard. Let's work together to tell your story. PARTNER WITH KEVINMD → https://kevinmd.com/influencer SUBSCRIBE TO THE PODCAST → https://www.kevinmd.com/podcast RECOMMENDED BY KEVINMD → https://www.kevinmd.com/recommended

Why do so many chiropractors struggle to sell care plans they genuinely believe in? And why does every “no” from a patient feel so personal? In this episode, Dr. Lauryn sits down with Dr. Daniel Bai of Close for Chiro for a candid, hilarious, and unfiltered conversation about ethical sales, patient psychology, and why the best closers are often the ones who stop talking so much.Together, Lauryn and Dr. Dan break down what actually happens on day one and day two, why patients need to feel deeply heard before they'll trust your solution, and how confusion kills conversions. They also dive into pricing confidence, why doctors over-explain their recommendations, how to stop taking patient decisions personally, and why ethical sales might be one of the most important skills for reducing practice owner burnout.Key Takeaways:Ethical sales starts with understanding the patient's real want, need, or desire. When doctors lead with curiosity instead of explanation, patients feel heard and are more likely to trust the recommended solution.Day one and day two are not separate events; they are one connected sales process. The more curious and patient-centered you are on day one, the more authority you earn when presenting answers on day two.Confused minds don't buy, and time kills deals. Simplifying care plans, finances, and recommendations can increase conversions more than adding more explanation ever will.Practice owners burn out when every patient yes or no becomes personal. A strong sales system helps doctors stay confident, ethical, and consistent without constantly defending their value.Guest Bio:Dr. Daniel Bai is the CEO of Close for Chiro, a provocative and enterprising consulting company serving the chiropractic industry through sales training, communication strategy, and business development. An author, speaker, and thought leader on modern sales and marketing, Dr. Dan is known for challenging outdated ideas about selling in healthcare while helping doctors build practices rooted in confidence, clarity, and ethical patient communication. His lessons are designed for chiropractors, but his straight-from-the-hip teaching style and no-nonsense approach to sales apply far beyond the profession.Follow Daniel on InstagramBook a consulting call with DanielResources:Find all things Dr. Lauryn B including ways to work with herFollow Dr. Lauryn: Instagram | Facebook | LinkedInFollow She Slays on YouTubeMentioned in this episode:Holistic Marketing HubWant to attract ideal patients to your clinic? No time to utilize your clinic's social media pages? Holistic Marketing Hub teaches you (or one of your team members) exactly how to use your clinic's Instagram account to find and attract those patients in your community. Use code "SheSlays" to get $300 off!Holistic Marketing HubINSiGHT CLAThis episode is brought to you by the INSiGHT scanning system from CLA, the tool that helps chiropractors show patients objective neurological data so the value of care becomes clear, fueling conversion, retention, and growth. She Slays listeners get preferred pricing, affordable financing, and a free Getting Into Scanning guide.CLA (Current)Clinic MindClinic Mind is the all-in-one EHR and practice management platform built for chiropractors — billing, documentation, scheduling, and patient follow-up in one place, whether you run a cash practice, take insurance, or are scaling to multiple locations. She Slays the Day listeners get an exclusive offer.Clinic Mind

What happens when artificial intelligence starts giving healthcare providers their time back?In this episode of TechTalk, Brad Cost and Dr. Jay Greenstein sit down with Blake Head, Vice President of Product & Strategy at PracticeTek, where he oversees product development and innovation for ChiroTouch (a leading chiropractic EHR and practice management software). In this episode, these three explore how AI is reshaping the future of chiropractic and healthcare technology.Blake shares his journey from working directly inside provider offices to leading product strategy for one of the largest chiropractic EHR platforms in the country. The conversation dives into the challenges providers face with documentation, workflow inefficiencies, patient engagement, insurance reimbursement, and how emerging AI tools are helping solve them.You'll learn:How AI is reducing after-hours documentation for providersWhy better technology can lead to stronger patient relationshipsThe role of AI in improving compliance and claims managementHow healthcare practices can leverage automation without losing the human touchWhy the future of healthcare may be "our agents versus their agents"Whether you're a chiropractor, healthcare leader, practice owner, or technology enthusiast, this episode offers a fascinating look at how innovation is transforming the provider experience—and what comes next.Tune in to hear how AI, patient experience, and smarter workflows are creating new opportunities for healthcare practices to grow while delivering better care.To connect with Blake, visit ChiroTouch.com, check out his LinkedIn at Blake Head, or shoot him an email at Blake.Head@PracticeTek.com

Explore how artificial intelligence is transforming medical education with insights from Drs. Eric Burnett (Columbia) and Alexander Glaser (Pennsylvania Hospital, UPenn), recorded live at AIMW26. This episode breaks down practical ways educators are using AI- from feedback tools and clinical reasoning support to EHR integration- while tackling real concerns like deskilling, bias, and academic integrity. Walk away with actionable frameworks and strategies to help learners use AI effectively, ethically, and in a way that actually improves their skills.Claim CME for this episode at curbsiders.vcuhealth.org!Website | Instagram | Twitter | Subscribe | Patreon | CME!| Youtube thecurbsidersteach@gmail.comCredits Producer, Show Notes, CME: Molly Heublein MD Script: Mike Cheng MD Infographic/ Cover Art: ChatGPT and Molly Heublein MD Hosts: Mike Cheng MD, Molly Heublein MD    Editor:  Era Kryzhanovskaya MD Guests: Eric Burnett MD, Alexander Glaser MD Technical support: Podpaste Theme Music: MorsyMusic  Show Segments Intro, disclaimer, guest bio Guest one-liner/ Best piece of advice Benefits of AI Risks of AI Institutional Policies and Guidelines around using AI in MedEd Frameworks for Assessing AI Use Practical Considerations and Future Directions How to get started with AI as an Educator Balancing Climate/societal Concerns Outro

Welcome to Health-e Law, Sheppard's podcast exploring the fascinating health tech topics and trends of the day. In the second part of this two-part episode, Cora Han, Chief Health Data Officer for University of California Health, joins partner and host Michael Orlando to discuss the current state of AI adoption across healthcare systems, including deployment, governance challenges, regulatory developments and the future of AI-enabled care delivery. What we discuss in this episode: The growing role and current state of AI adoption across healthcare delivery systems Ambient scribes, inbox management, coding assistance and other practical AI use cases Using AI to improve quality reporting and operational efficiency Opportunities for AI to address clinician shortages, burnout and healthcare access issues Expanding access to specialized care through AI-enabled care models The challenges of evaluating and governing a rapidly growing AI vendor ecosystem Adapting governance frameworks to keep pace with AI innovation Federal and state regulatory developments impacting healthcare AI adoption The importance of transparency in AI tools, including model development, performance and use Why implementation and workflow integration remain critical barriers to successful AI adoption Measuring ROI and real-world impact of AI tools in healthcare settings About Cora Han Cora Han is Chief Health Data Officer for University of California Health and Executive Director of the Center for Data-driven Insights and Innovation. She also serves as Co-Chair of the Health System and Provider Advisory Board for the Coalition for Health AI (CHAI).  Drawing on her extensive experience in AI strategy, regulatory advocacy, and data privacy, Cora leads efforts to establish consistent guardrails for the use of health data with AI vendors and third-party collaborators. Her work spans the full spectrum of health data challenges, from de-identification of clinical data to navigating HIPAA compliance and AI vendor relationships, making her a leading voice on responsible AI adoption in academic health systems. Before joining UC Health, Cora spent over ten years at the Federal Trade Commission, most recently as Senior Attorney in the Division of Privacy and Identity Protection, where she focused on data privacy and consumer protection, including a term as Counsel to the Director of the Bureau of Consumer Protection. Prior to her tenure at the FTC, she practiced at a leading international law firm, where she counseled clients on copyright and trademark matters. Cora also served as an Adjunct Professor of Consumer Protection Law at George Mason University School of Law for five years. Cora holds a BA in Government from Harvard University and a JD from the University of Chicago Law School. About Michael Orlando Michael Orlando is a partner in Sheppard's San Diego (Del Mar) office. He is team leader of the firm's Technology Transactions team, a member of the Life Sciences, Healthcare and Artificial Intelligence teams, and co-leader of the firm's Digital Health & Innovation team. Michael has more than 20 years of experience advising health technology companies, insurers, healthcare systems and providers, academic medical centers and research institutions, medical device manufacturers, pharmaceutical and wellness companies on intellectual property and business transactions in key strategic areas, including EHR systems procurement and integration, telehealth, mobile health applications, clinical decision support technologies, artificial intelligence, data use, wearable devices, remote patient monitoring, medical devices and equipment, research and collaborations, patent licenses, software licenses, joint ventures, mergers and acquisitions, revenue cycle management, and other outsourcing transactions.  Michael founded a software-as-a-service company before entering private practice and completed an in-house secondment at a publicly traded biotechnology company, an experience that informs his practical and business-focused approach to client engagements. Thank you for listening! Don't forget to SUBSCRIBE to the show to receive new episodes delivered straight to your podcast player every month. If you enjoyed this episode, please help us get the word out about this podcast. Rate and Review this show on Apple Podcasts, Amazon Music, or Spotify. It helps other listeners find this show. This podcast is for informational and educational purposes only. It is not to be construed as legal advice specific to your circumstances. If you need help with any legal matter, be sure to consult with an attorney regarding your specific needs.

Four Pillars of Advertising for Pediatric Practices: Newborn ROI, TikTok, Google Maps, and Internal OutreachHosts discuss pediatric practice marketing with repeat guest Dr. Cliff James, focusing on ROI-driven patient acquisition and replacing attrition by targeting newborns while balancing capacity and scheduling efficiency. James argues marketing spend should be measured by cost per acquired patient (nationally ~$80–$100; his ~$32), not percent of revenue, and stresses tracking “how did you hear about us” to avoid misleading metrics like clicks. He outlines advertising pillars: (1) social media content, especially TikTok, to educate pregnant/new parents and generate both patients and platform revenue; (2) hyperlocal visibility via Google Maps/Google Business and consistent listings across many directories, with mobile-optimized, content-rich websites that AI search tools can scrape; (3) targeted paid ads such as geofencing OB offices and filtering by demographics; and (4) internal “advertising” using EHR outreach to drive well visits and chronic care follow-ups. He emphasizes outsourcing execution while physicians stay involved and recommends treating the website like a revenue-producing employee.00:00 Podcast Intro and Guest01:38 Why Market to Newborns03:23 Attrition and Growth Math05:43 Capacity and Scheduling Limits13:47 Walk Ins and Workflow Hacks16:33 Marketing Spend and CPA18:48 Modern Referral Channels20:51 Outsource vs Be the Star23:22 Social Media Pillar TikTok27:59 Tracking Leads and Targeting29:32 High Income Ad Targeting30:10 Geofencing OB Offices31:16 60 Second Video Strategy32:32 Choosing Social Channels33:14 Avoiding Link Penalties35:29 Google Maps Over SEO36:29 AI Search Website Pages39:50 Reviews And Internal Outreach48:19 Quiz Funnels For Leads52:06 Webinars Worth It55:36 Delegate Marketing Work01:00:16 Website As An Employee01:02:23 Closing And DisclaimersSupport the show

Electronic medical records (EMRs) have moved from a back-office upgrade to a frontline determinant of care quality, clinician burnout, and hospital economics. With U.S. hospitals often spending tens to hundreds of millions—sometimes exceeding $100 million—on EMR implementations, the stakes have never been higher for getting both the technology and the human adoption right. As healthcare continues shifting toward interoperability, outpatient care, and data-driven decision-making, the conversation around EMRs is no longer technical—it's strategic.So what does it really take to build a business in the EMR space—and more importantly, how do you know when it's time to walk away from it?Welcome to I Don't Care, hosted by Dr. Kevin Stevenson. In the latest episode, Dr. Stevenson sits down with Mark Embry, partner and co-founder of MedSys Group, to unpack decades of experience in healthcare technology consulting, the evolution of EMR implementation, and the personal side of exiting a company after 30 years.Top insights from the talk…How EMR consulting evolved from niche staffing to mission-critical healthcare transformation work: What started as staffing has become strategic work shaping how health systems operate.Why user adoption—not just technology—is the biggest determinant of EMR success: Without workflow change and clinician buy-in, even the best systems fall short.What founders should consider when transitioning out of a business they've built from scratch: A strong exit balances financial outcomes with team, culture, and timing.Mark Embry is the co-founder and EVP of Client Relationships at MedSys Group, where he has spent nearly three decades leading EMR advisory, implementation, and healthcare IT consulting services for providers across the U.S. He played a key role in building the company from its origins as Genesys Group into a nationally recognized firm supporting major initiatives, including federal EHR modernization projects with the DOD and VA. With over 20 years in IT consulting, Embry specializes in strategic partnerships, healthcare technology transformation, and scaling consulting organizations to deliver high-impact client outcomes.

Are you still the bottleneck in your clinic even though you have a team? In this solo episode, Dr. Lauryn is back with part two of her conversation on owner dependency, and this time she is getting practical. If your team still needs constant hand-holding, your systems feel messy, and delegation feels harder than just doing it yourself, this episode is your next step.Dr. Lauryn walks through the SEAD framework: Simplify, Eliminate, Automate, and Delegate. She explains how to use team time audits, blank org charts, AI tools, VAs, software automation, and smarter delegation to create real bandwidth inside your practice. This is not about dumping more work onto your staff. It is about cleaning up chaos, freeing your best people for higher-level ownership, and helping you finally move from manager mode into CEO mode.Key Takeaways:Before you delegate more, you need to understand what your team is already carrying. A full team time audit helps reveal repeated tasks, interruptions, unnecessary approvals, manual work, and hidden capacity leaks.The SEAD framework gives clinic owners a practical way to clean up operations before adding more responsibility to the team. Simplify what is too complicated, eliminate what no longer matters, automate what software or AI can handle, and delegate work to the right person or resource.Delegation should not start by pushing your chaos onto an already overwhelmed employee. The first move is often delegating tasks away from your team to a VA, software, AI tool, outside service, or lower-level support role.Moving from manager to CEO requires patience, leadership, and a willingness to invest time now for freedom later. Your team needs clarity, authority, safety to make mistakes, and structured support as they take ownership of higher-value work.Resources:Find all things Dr. Lauryn B including ways to work with herFollow Dr. Lauryn: Instagram | Facebook | LinkedInFollow She Slays on YouTubeMentioned in this episode:INSiGHT CLAThis episode is brought to you by the INSiGHT scanning system from CLA, the tool that helps chiropractors show patients objective neurological data so the value of care becomes clear, fueling conversion, retention, and growth. She Slays listeners get preferred pricing, affordable financing, and a free Getting Into Scanning guide.CLA (Current)Holistic Marketing HubWant to attract ideal patients to your clinic? No time to utilize your clinic's social media pages? Holistic Marketing Hub teaches you (or one of your team members) exactly how to use your clinic's Instagram account to find and attract those patients in your community. Use code "SheSlays" to get $300 off!Holistic Marketing HubClinic MindClinic Mind is the all-in-one EHR and practice management platform built for chiropractors — billing, documentation, scheduling, and patient follow-up in one place, whether you run a cash practice, take insurance, or are scaling to multiple locations. She Slays the Day listeners get an exclusive offer.Clinic Mind

Dr. Rob Bart is the Chief Medical Information Officer at UPMC, where he is leading one of the largest EHR consolidations in the country- bringing the entire health system onto a single Epic instance. A pediatric intensivist by training, Rob has been a pioneer in the CMIO role for more than two decades, with prior leadership at Cerner and Los Angeles County Department of Health Services. In this episode of DGTL Voices, Rob tells Ed about growing up in Hawaii (his high school classmate happened to become President of the United States), the conversation that pulled him from research into medicine, why clinicians need to keep practicing to keep their credibility, and his case against the endless creation of new C-suite titles every time technology evolves. Plus: the trust framework he uses with his team, why recovering from a wrong decision matters more than being right the first time, and how bike rides through a cemetery near his home keep him grounded.

This podcast explores how an interdisciplinary team of pharmacists, nurses, and providers redesigned frequently ignored and interruptive allergy review alerts within the electronic health record (EHR). By leveraging data from the EHR and incorporating frontline feedback, the team implemented changes to reduce noise and maintain patient safety.  The information presented during the podcast reflects solely the opinions of the presenter. The information and materials are not, and are not intended as, a comprehensive source of drug information on this topic. The contents of the podcast have not been reviewed by ASHP, and should neither be interpreted as the official policies of ASHP, nor an endorsement of any product(s), nor should they be considered as a substitute for the professional judgment of the pharmacist or physician.

Aledade has been supporting primary care organizations to succeed in value-based care arrangements since 2014. By providing the resources, technology, and data necessary for success, Aledade enables clinicians to focus on what matters most: the patient. As Aledade's Co-founder and CEO, Farzad Mostashari, MD, often says, the company's goal is to make it more profitable to prevent a stroke than to treat one.We recently sat down with Jonas Goldstein, Senior Vice President of Transformation at Aledade, and Jeremy Presley, MD, who runs a primary care practice in Kansas, and also serves as an Aledade Regional Medical Director in that area, to discuss Aledade Assist. In our discussion, they both dive into how Aledade Assist is surfacing relevant health data and insights at the point of care. Plus, they share their unique approach to integrating this data and information within the EHR workflow.Learn more about Aledade: https://aledade.com/Healthcare IT Community: https://www.healthcareittoday.com/

It's in the News! The top diabetes stories and headlines happening now. Top stories this week include: Afrezza inhaled Insulin is Approved for Kids, CGM + Ketone Monitor gets European approval, Food Coloring & Diabetes Study, Device Recalls include Omnipod and Dexcom, Beta Bionics shares more about their patch pump, ADA conference info and more! This podcast is not intended as medical advice. If you have those kinds of questions, please contact your health care provider. Announcing Community Commericals! Learn how to get your message on the show here. Learn more about studies and research at Thrivable here Please visit our Sponsors & Partners - they help make the show possible! Omnipod - Simplify Life All about Dexcom  All about VIVI Cap to protect your insulin from extreme temperatures The best way to keep up with Stacey and the show is by signing up for our weekly newsletter: Sign up for our newsletter here Here's where to find us: Facebook (Group) Facebook (Page) Instagram Check out Stacey's books! Learn more about everything at our home page www.diabetes-connections.com  Episode transcripts: Welcome! I'm your host Stacey Simms and this is an In The News episode.. where we bring you the top diabetes stories and headlines happening now. A reminder that you can find the sources and links and a transcript and more info for every story mentioned here in the show notes. ADA starts this week – safe travels to those of you heading to New Orleans. We'll be covering remotely so please follow on social – make sure to Like the FB page or join the group. We've got a wrap up episode planned for this podcast as well as some indepth interviews with the newsmakers from the conference. I will see some of you next week in Chicago. We have a couple of seats left for our Club 1921 dinner on June 10th in Northbrook – this is a FREE dinner for HCPs and patient leaders – all about screening for T1D. More info on the website under the events tab. Okay.. our top story this week: XX Afrezza inhaled insulin is now approved for kids and teens. The FDA okayed MannKind's afrezza for children 6 and older with type 1 and type 2 diabetes. MannKind says its proprietary Technosphere drug delivery platform enables the rapid absorption of insulin into systemic circulation. This follows FDA approval earlier this year for an update that revises recommendations for the starting mealtime dosage when patients switch from subcutaneous mealtime insulin regimens. MannKind also completed enrollment in February for a study evaluating the initiation of Afrezza therapy shortly after type 1 diabetes diagnosis in pediatric patients.   The company said it made Afrezza available for eligible patients for $35 or less per month. Desmond Schatz, professor of pediatrics at the University of Florida College of Medicine, said: "Mealtime insulin can be especially challenging for children because eating and snacking patterns, activity levels, and daily settings like school and sports often vary. With its rapid onset and dosing at the start of a meal, Afrezza may help clinicians better match insulin therapy to how children and families live day to day, while offering a needle-free mealtime option." Lots more to come on this – we're working on a bonus episode with one of the pediatric endos who worked on the clinical trials that led to this approval – hopefully have that out later this week. https://www.massdevice.com/mannkind-fda-approval-inhaled-insulin-children/ XX FDA has agreed to consider a new drug for the treatment of adults with type 1 and chronic kidney disease. Finerenone (fy-near-uh-known) is currently approved in the US for adults with CKD associated with type 2 diabetes and for adults with heart failure with left ventricular ejection fraction of 40% or greater. Chronic kidney disease (CKD) is present in over one-third of adults with diabetes, and because it's such a serious condition, interventions are needed to reduce its incidence and help people live a long and prosperous life. https://www.docwirenews.com/post/fda-grants-priority-review-to-finerenone-snda-for-type-1-diabetes-associated-ckd XX Abbot gets European approval for the world's first dual glucose‑ketone sensing technology for people with diabetes. They're calling this Libre Duo and Libre Duo 10 Day, and it's designed to continuously measure glucose and ketone levels every minute. Abbott plans to begin launching Libre Duo systems in select European countries later this year. Libre Duo delivers up to 15 days of wear and will be offered to adults ages 18 and older. Libre Duo 10 Day offers up to 10 days of wear and is intended for people ages 2 and older. Abbott is also working with leading pump companies to allow automated insulin delivery (AID) systems to connect with the sensors. https://abbott.mediaroom.com/2026-05-27-Abbott-secures-CE-Mark-for-worlds-first-dual-glucose-ketone-sensing-technology-for-people-with-diabetes   XX Huge recall for Omnipod. Insulin says a manufacturing issue through ongoing product monitoring that could result in insulin under-delivery  with specific lots of its Omnipod 5, Dash and Eros pods. Insulet said the scope of this action reaches approximately 7 million pods. This issue is separate from the March recall that affected certain Omnipod 5 lots. According to the Acton, Massachusetts-based company, some of its affected pods may have a small tear in the tubing (cannula) just above the skin. This tear lands between the pod and the point where the cannula enters the body. If this occurs, insulin may leak outside of the device instead of being fully delivered into the body as intended. This may lead to under-delivery of the therapeutic.   Individuals using an affected pod may notice wetness on the skin or pod adhesive or detect the smell of insulin. However, some cases may prove difficult to detect and go unnoticed. Of the approximately 7 million pods included in the action, approximately 60% have been consumed or are expired. The pods affected by the correction represent approximately 8.5% of the 2025 global Omnipod pod prodcution. Insulet says it has sufficient supply to replace affected pods. It expects no disruption to product availability. The company said it has notified the FDA and all other relevant regulatory authorities of its action.   The full list of affected pod lots can be found here. https://www.massdevice.com/insulet-another-omnipod-5-recall-dash-eros/ XX Dexcom is warning that certain scrapped glucose sensors have been stolen and resold. Dexcom said it has not received any reports of severe adverse events associated with the stolen product. One lot of scrapped devices carries a risk of infection for sensors that are not properly sterilized, and another lot had an elevated internal testing failure rate, meaning users would have an increased risk of having no sensor readings available. Dexcom said the affected sensors were stolen during the destruction process and then sold by third parties. The company routinely scraps sensors that do not meet its standards. The sensors are sent to a third-party vendor for destruction and recycling.   Dexcom said it traced sales of the stolen devices to Pharmsource, which is not an authorized Dexcom distributor but supplies some independent pharmacies and U.S. durable medical equipment distributors. Because of this, pharmacies that purchase products from Pharmsource should review their inventory, Dexcom said.   People with sensors from the affected lots should not use those sensors and can call customer support to request replacements. Dexcom has set up a website to help users check if their devices are affected. https://www.medtechdive.com/news/dexcom-warns-of-scrapped-glucose-sensors-being-resold/821139/ XX XX   Beta Bionics plans to debut its first insulin patch pump by the end of the second quarter of 2027, subject to Food and Drug Administration clearance. The device, called Mint, would be compatible with Beta Bionics' interoperable automated glycemic controller, a software that allows for the pump to automatically adjust insulin delivery based on readings from a glucose sensor. Beta Bionics first unveiled the prototype for Mint last year at the American Diabetes Association's Scientific Sessions. The device is expected to have a similar size and wear time, at three days, to Insulet's patch pumps on the market. It would have a 200-unit insulin reservoir.   Mint differs by containing a mix of reusable and disposable components. Beta Bionics plans to make the device exclusively available in the pharmacy channel, building on its existing agreements for its current iLet insulin pump. Beta Bionics is one of several diabetes tech companies developing patch pumps to compete with market leader Insulet. Tandem Diabetes Care and Medtronic spinoff MiniMed have also announced planned patch pumps. Tandem said it plans to file a 510(k) submission this quarter for a tubeless version of its small, durable pump, and Medtronic plans to submit its patch pump to the FDA this fall.   https://www.medtechdive.com/news/beta-bionics-to-launch-its-first-insulin-patch-pump-to-compete-with-insulet/821091/ XX CVS puts Zepbound back on it's coverage list – with it's Caremark PBM. They also added Foundayo, Lilly's obesity pill. CVS had dropped Lilly's Zepound last summer but kept competitor Wegovy. It'll be back at Caremark October first. All three of the nation's largest pharmacy ⁠benefit managers ​now cover Lilly's full obesity medicine portfolio. https://www.reuters.com/legal/litigation/cvs-brings-back-coverage-lillys-obesity-drug-zepbound-2026-05-28/   More to come, including a new benefit from metformin for women, something new from Tidepool, big news for T1D in Austalia and more.. XX A new study suggests that higher long-term exposure to food colouring additives — including both synthetic and natural colourings commonly found in processed foods and beverages — may be associated with an increased risk of developing type 2 diabetes. Researchers analyzed data from more than 108,000 adults in the French NutriNet-Santé cohort between 2009 and 2023, following participants for a median of just over eight years. During that time, 1,131 participants developed type 2 diabetes. The study found that people with the highest intake of total food colouring additives had a 38% higher risk of developing type 2 diabetes compared with non- or low-consumers.   Several specific additives were linked to increased risk, including caramel colouring additives such as total caramel (E150 family), plain caramel (E150a), sulphite ammonia caramel (E150d), and beta-carotene (E160a). Additional associations were observed for curcumin (E100), anthocyanins (E163), paprika extract (E160c), lutein (E161b), and cochineal-derived colourings (E120). "Our findings revealed positive associations between widely consumed food colouring additives and type 2 diabetes incidence," the authors wrote, adding that further research is needed to better understand the mechanisms behind the findings and whether food colouring regulations should be reevaluated. https://www.medscape.com/viewarticle/use-common-food-colours-tied-high-type-2-diabetes-risk-2026a1000hes XX Big news for Australia – their Therapeutic Goods Administration (TGA) approves Tzield. Tzield is now approved in Australia to delay the onset of stage 3 (or clinical) T1D in people aged eight years and older with stage 2 T1D – the early, pre-symptomatic stage of the condition, where changes in blood glucose levels have begun but insulin therapy is not yet required. Breakthrough T1D Australia Chief Executive Officer, Sydney Yovic, said the approval represented a transformational moment for Australians affected by T1D. https://newshub.medianet.com.au/2026/05/landmark-approval-of-tzield-in-australia-ushers-in-a-new-era-of-delay-for-type-1-diabetes/155036/ XX https://www.theatlantic.com/health/2026/05/diabetes-pregnancy/687324/ XX A common diabetes drug may hold great potential to help with aging, even if scientists aren't exactly sure why. According to a study, the drug metformin doesn't just help patients to effectively manage their type 2 diabetes. it may also give older women a better chance of living to 90. Scientists in the US and Germany used data from a long-term US study of postmenopausal women.   Records for a total of 438 people were selected – half of whom took metformin to treat diabetes, and half of whom took a different diabetes drug, sulfonylurea.   While there are some caveats and asterisks to the study, those in the metformin group were calculated to have a 30 percent lower risk of dying before the age of 90 than those in the sulfonylurea group. The study used age 90 as the marker for 'exceptional' longevity. However, scientists aren't yet sure that the drug extends lifespan, especially in humans – which is part of the reason for this study. RCTs could follow further down the line to dig deeper into these results, the researchers suggest. In the meantime, as the global population continues to skew older, studies continue to find ways to keep us healthier for longer and reduce damage to the body as we age. https://www.sciencealert.com/a-common-diabetes-drug-is-linked-with-exceptional-longevity-in-women XX The American Diabetes Association® (ADA) will host the 2026 Scientific Sessions from June 5-8 in New Orleans. The ADA's Scientific Sessions is the world's largest diabetes meeting, convening an expected audience of over 12,000 leading physicians, scientists, researchers, and healthcare professionals from around the globe. The premier diabetes meeting, which is also offered virtually, will feature the latest scientific findings in diabetes and obesity, where leading experts and peers will share findings in research for prevention, care, and cures at the Ernest N. Morial Convention Center. Key themes will include: Advancing obesity and metabolic health: Prevention, early detection, and disease modification: Improving cardiometabolic outcomes: Transforming care through innovation and access: New research will highlight how technology, artificial intelligence, and implementation strategies are reshaping diabetes care—reducing treatment burden, expanding access, and enabling more person-centered care. Advancing beta cell replacement and cure strategies: Fostering innovation: On Saturday, June 6, from 4:30-6:00 p.m., the Innovation Challenge, which debuted in 2023, invites emerging companies to pitch novel ideas to improve the lives of people living with diabetes. A panel of judges, with input from a live audience, determines which contestants will earn a private audience with potential funders. XX Tidepool, the nonprofit leader advancing innovation in diabetes technology, announced that Tidepool+ Direct Connect is now available through the Epic Showroom. Built on SMART on FHIR, Direct Connect brings interactive diabetes device data directly into Epic workflows, helping clinicians use patient data during routine care. "Tidepool has always focused on making diabetes data more accessible and actionable," said Brandon Arbiter, CEO. "We're excited to empower clinicians using Epic with insightful, intuitive patient data that fits directly into their encounter workflow so they can use it to improve care in the moment it matters."   Tidepool+ Direct Connect supports scalable deployment across Epic-enabled health systems. This architecture enables faster, more intuitive rollouts, enhancing Tidepool's existing EHR integration capabilities.   Direct Connect is part of Tidepool's ongoing work to improve how clinicians can use timely and relevant diabetes device data during patient visits to help drive better health outcomes.   The feature is now available in the Connection Hub of the Epic Showroom.   https://www.businesswire.com/news/home/20260527780274/en/Tidepool-Launches-in-Epic-Showroom-to-Bring-Diabetes-Device-Data-into-the-Point-of-Care XX

Part 2 of our multi-location revenue series. If you haven't listened to Part 1 (EP182) yet, start there — the systems in this episode build directly on what we covered last week. EP182: Click hereToday we cover the two structural problems that let the Part 1 gaps stay open: front-end data inconsistency across sites, and the one role that either holds a multi-site practice together or lets it fall apart. System 3 — The EHR and Billing Disconnect: Different front desks develop different habits. One site verifies eligibility morning-of. The other verifies the day before. One collects copay at check-in. The other sends a statement after. A practice doing $120,000/month at Location B with a 20% authorization miss rate sends $24,000/month into billing with incomplete data. Some claims get caught in scrubbing. Some get denied. Some sit in a gray zone no one can explain at month-end review. Front-End Gap Reference: Authorization not captured → Denial or recoupment post-payment Insurance not updated at visit → Claim sent to wrong payer Copay not collected at check-in → Patient AR that rarely converts Eligibility verified day-of only → Coverage lapses missed pre-visit System 4 — The Office Manager Problem at Scale: Location A has a strong office manager who has been there since the beginning. Location B has whoever was available when the site opened. The metrics look similar on paper. The difference shows up in the denial rate, days in AR, authorization miss rate, and the number of times the billing manager has to fix something that should have been caught at the front desk. A $90,000/month site with an underperforming office manager loses an estimated $8,000 to $15,000/month in avoidable billing delays. That is $180,000/year from one seat filled with the wrong person. Three actions this week: Audit front-end protocol consistency — pull authorization miss rate and eligibility verification rate by site Run a site-level office manager assessment — KPIs only, not by feel Schedule weekly site-level KPI reviews — separate meetings, not consolidated Episode breakdown: 00:00 Series callback: the gap the report will not show you 02:00 The thread left open in Part 1 04:30 System 3: The EHR and Billing Disconnect Across Sites 08:00 The $24,000/month authorization miss scenario 11:30 Who owns the front-end protocol fix 14:00 System 4: The Office Manager Problem at Scale 18:30 The $180,000/year gap from one wrong seat 22:00 Who owns the accountability structure 24:30 Three actions this week 28:00 Free resource + next episode tease Resources Mentioned Payment Posting Audit Checklist (free): eligibility.natrevmd.com/payment-posting-checklist Practice Revenue Leak Scorecard (free): eligibility.natrevmd.com/nrm-revenue-scorecard-v3 Book a free 30-minute audit call: calendly.com/heather-natrevmd RECOVER Diagnostic Quiz: natrevmd.com/quiz EP182 — Part 1 of this series: Link here

The Big Unlock · Podcast with Dr. Nele Jessel, Chief Medical Officer, athenahealth In this episode, Dr. Nele Jessel, Chief Medical Officer at athenahealth, explores the rapid shift in physician sentiment toward AI and why healthcare may finally be reaching a true inflection point in digital transformation. Dr. Jessel explains how decades of EHR-induced administrative burnout initially made clinicians wary of new technology. However, the arrival of ambient note generation changed the game almost overnight, removing immense cognitive load and restoring the intimate, face-to-face physician-patient relationship. A core theme of the discussion is the critical pivot toward clinician-guided development rather than vendor-driven solutions. Dr. Jessel details how athenahealth uses rapid “pre-alpha” prototyping to tackle the modern challenge of interoperability data-overload, deploying large language models to synthesize complex clinical records into actionable insights at the point of care. While emphasizing that medicine remains an art that requires a human in the loop for diagnostics, she outlines a future where autonomous, agentic AI conquers administrative burdens like prior authorizations. Ultimately, healthcare is reaching a true inflection point, transforming the EHR from a passive data repository into an invisible, intelligent assistant. Take a listen. This guest appearance was facilitated through conversations initiated at ViVE.

For the 115th episode of the CIO podcast hosted by Healthcare IT Today, we are joined by John Gaede, CIO at San Juan Regional Medical Center, to talk about healthcare communication solutions! We kick this episode off by discussing the big challenges Gaede faces as a rural health CIO. Next, Gaede shares why he chose to go with the PerfectServe solution over the EHR functionality for his organization. Then, we dive deep into the scope of Gaede’s project with PerfectServe as he shares his main goals for it as a clinical project vs as an IT project. Gaede has mentioned that this is the most important project he’s undertaken and is the major focus of his transformation efforts, so we dive into why this is so important. Next, we talk about the other projects Gaede is working on/recently completed that he’s excited about. We then switch over to the technologies/solutions/vendors/etc. Gaede has not implemented, but is keeping an eye on. Lastly, we conclude this episode with Gaede sharing advice to anyone aspiring to be a CIO like him. Here’s a look at the questions and topics we discuss in this episode: What are some of the big challenges you face as a rural health CIO? Talk about why you recently chose to go with the PerfectServe solution at your organization rather than using the EHR functionality. Describe the scope of the project with PerfectServe and your main goals for it as a clinical project vs an IT project. You mentioned that this is the “most important project you’re undertaking” and is a major focus of your transformation efforts. Why is this so important? What other projects are you working on or recently completed that you’re excited about? What technology, solution, vendor, etc., have you not implemented, but you’re watching? What advice would you give someone who aspires to be a CIO like you? Now, without further ado, we’re excited to share with you the next episode of the CIO Podcast by Healthcare IT Today. We release a new CIO Podcast every ~2 weeks. You can also subscribe to the Healthcare IT Today podcast on any of the following platforms: NOTE: We’ll be updating the links below as the various podcasting platforms approve the new podcast.  Check back soon to be able to subscribe on your favorite podcast application. Apple Podcasts Google Podcasts Stitcher Podcast Radio TuneIn Spotify iHeartRadio Amazon Music Thanks for listening to the CIO Podcast on Healthcare IT Today and if you enjoy the content we’re sharing, please rate the podcast on your favorite podcasting platform. Along with the popular podcasting platforms above, you can Subscribe to Healthcare IT Today on YouTube.  Plus, all of the audio and video versions will be made available to stream on HealthcareITToday.com. We’d love to hear what you think of the podcast and if there are other healthcare CIO you’d like to see us have on the program. Feel free to share your thoughts and perspectives in the comments of this post with @techguy on Twitter, or privately on our Contact Us page. We appreciate you listening! Listen to the Latest Episodes

Imaging centers face serious legal exposure from contrast reactions—not from the reactions themselves, but from gaps in supervision and documentation. Discover why virtual models may offer stronger protection than on-site oversight and what your EHR templates are missing. Learn more at https://www.contrast-connect.com/blog-post/contrast-reaction-liability-exposure-supervision-model-risk-documentation-practices ContrastConnect City: Las Vegas Address: Las vegas Website: https://www.contrast-connect.com/

Dr. Stephanie Lahr, Chief Medical Officer at uPerform, highlights the critical need for new ways to conduct health IT education. Traditional one-time training sessions for large groups are no longer sufficient for the constantly evolving healthcare technologies. Self-paced and personalized training is the way to meet users at their individual skill levels, freeing up training teams to provide targeted support and build organizational competencies, including how to effectively use AI.  Stephanie explains, "This idea, which we are seeing evolve rapidly, is that as we integrate technology into healthcare delivery, people need to understand how to use it, which requires ongoing training, support, and communication."   "It's really a way to think about doing just-in-time, as-needed, self-driven education based on the user for whatever needs might arise with the technology they're using in care delivery. Often, that starts with systems like the EHR and ERP, but it goes far beyond that. And we're seeing that more and more as different kinds of technologies find their way into healthcare delivery to support the care providers in what they're trying to do."  "Traditionally, we had this idea that we rolled out technology, and you did some training, which usually involved a classroom, a quick video tutorial, or maybe an online course or something along those lines. And it was sort of a one-and-done: you learned it, you went, you moved on, and you used it. And I think what we're now seeing is that the systems themselves are changing constantly."   #uPerform #AI #JustInTimeTraining #WorkflowEducation #HealthIT #EHR #ClinicalInformatics #DigitalHealth #ClinicianExperience #HealthcareInnovation #MedicalEducation #AIinHealthcare uperform.com Listen to the podcast here

Dr. Stephanie Lahr, Chief Medical Officer at uPerform, highlights the critical need for new ways to conduct health IT education. Traditional one-time training sessions for large groups are no longer sufficient for the constantly evolving healthcare technologies. Self-paced and personalized training is the way to meet users at their individual skill levels, freeing up training teams to provide targeted support and build organizational competencies, including how to effectively use AI.  Stephanie explains, "This idea, which we are seeing evolve rapidly, is that as we integrate technology into healthcare delivery, people need to understand how to use it, which requires ongoing training, support, and communication."   "It's really a way to think about doing just-in-time, as-needed, self-driven education based on the user for whatever needs might arise with the technology they're using in care delivery. Often, that starts with systems like the EHR and ERP, but it goes far beyond that. And we're seeing that more and more as different kinds of technologies find their way into healthcare delivery to support the care providers in what they're trying to do."  "Traditionally, we had this idea that we rolled out technology, and you did some training, which usually involved a classroom, a quick video tutorial, or maybe an online course or something along those lines. And it was sort of a one-and-done: you learned it, you went, you moved on, and you used it. And I think what we're now seeing is that the systems themselves are changing constantly."   #uPerform #AI #JustInTimeTraining #WorkflowEducation #HealthIT #EHR #ClinicalInformatics #DigitalHealth #ClinicianExperience #HealthcareInnovation #MedicalEducation #AIinHealthcare uperform.com Download the transcript here

The Department of Veterans Affairs is getting bipartisan praise for its resumed rollout of a new Electronic Health Record; a multibillion-dollar project that has run into myriad problems since it began under the first Trump administration. The VA rolled out the system to new sites last month after a three-year pause on deployment. Groups representing VA employees, however, say the latest facilities to adopt the new EHR are dealing with problems. Federal News Network's Jory Heckman has more.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Welcome to Health-e Law, Sheppard's podcast exploring the fascinating health tech topics and trends of the day. In part one of this two-part episode, Cora Han, Chief Health Data Officer for University of California Health, joins partner and host Michael Orlando to discuss the adoption of Artificial Intelligence in healthcare, including the management of data privacy, vendor relationships and AI governance. What we discuss in this episode: De-identification of protected health information in practice The HIPAA standards for de-identification and when to apply them The unique challenges unstructured clinical notes present for re-identification risk A layered approach to de-identification, including automated tools and human review Third-party certification of de-identification processes Key contract provisions for protecting PHI when working with AI vendors Hidden data training clauses in vendor agreements The evolving AI vendor marketplace and contract strategy UC Health's federated governance model for AI and data governance Shadow AI use and the importance of safe experimentation The importance of legal and compliance teams engaging early in the AI adoption process About Cora Han Cora Han is Chief Health Data Officer for University of California Health and Executive Director of the Center for Data-driven Insights and Innovation. She also serves as Co-Chair of the Health System and Provider Advisory Board for the Coalition for Health AI (CHAI).  Drawing on her extensive experience in AI strategy, regulatory advocacy, and data privacy, Cora leads efforts to establish consistent guardrails for the use of health data with AI vendors and third-party collaborators. Her work spans the full spectrum of health data challenges, from de-identification of clinical data to navigating HIPAA compliance and AI vendor relationships, making her a leading voice on responsible AI adoption in academic health systems. Before joining UC Health, Cora spent over ten years at the Federal Trade Commission, most recently as Senior Attorney in the Division of Privacy and Identity Protection, where she focused on data privacy and consumer protection, including a term as Counsel to the Director of the Bureau of Consumer Protection. Prior to her tenure at the FTC, she practiced at a leading international law firm, where she counseled clients on copyright and trademark matters. Cora also served as an Adjunct Professor of Consumer Protection Law at George Mason University School of Law for five years. Cora holds a BA in Government from Harvard University and a JD from the University of Chicago Law School. About Michael Orlando Michael Orlando is a partner in Sheppard's San Diego (Del Mar) office. He is team leader of the firm's Technology Transactions team, a member of the Life Sciences, Healthcare and Artificial Intelligence teams, and co-leader of the firm's Digital Health & Innovation team. Michael has more than 20 years of experience advising health technology companies, insurers, healthcare systems and providers, academic medical centers and research institutions, medical device manufacturers, pharmaceutical and wellness companies on intellectual property and business transactions in key strategic areas, including EHR systems procurement and integration, telehealth, mobile health applications, clinical decision support technologies, artificial intelligence, data use, wearable devices, remote patient monitoring, medical devices and equipment, research and collaborations, patent licenses, software licenses, joint ventures, mergers and acquisitions, revenue cycle management, and other outsourcing transactions.  Michael founded a software-as-a-service company before entering private practice and completed an in-house secondment at a publicly traded biotechnology company, an experience that informs his practical and business-focused approach to client engagements.   Thank you for listening! Don't forget to SUBSCRIBE to the show to receive new episodes delivered straight to your podcast player every month. If you enjoyed this episode, please help us get the word out about this podcast. Rate and Review this show on Apple Podcasts, Amazon Music, or Spotify. It helps other listeners find this show. This podcast is for informational and educational purposes only. It is not to be construed as legal advice specific to your circumstances. If you need help with any legal matter, be sure to consult with an attorney regarding your specific needs.

EHR downtime is inevitable. Cyberattacks, network outages, and even planned system upgrades can disrupt access to patient records, placing care delivery, patient safety, and operations at risk.In this video, Jackie Rice, Vice President and Chief Information Officer at Frederick Health, shares how her organization proactively prepared for EHR downtime by moving away from paper-based workflows to a near real-time digital environment that mirrors the EHR user experience. Ryan Dickerson, President of IPeople Healthcare (now part of RLDatix), explains how a purpose-built data resiliency solution supports safe, uninterrupted care during both planned and unplanned outages.Built for MEDITECH EHR environments, IPeople's Offline Suite captures near real-time clinical data in a secure on‑premises system, with optional cloud replication. When the primary EHR or local network is unavailable, clinicians maintain uninterrupted access to critical patient information through familiar workflows, without reverting to paper-based processes.Watch to learn how healthcare organizations are safeguarding patient safety, reducing operational risk, and ensuring readiness for EHR downtime.Learn more about iPeople Healthcare: https://www.ipeople.com/Learn more about Frederick Health: https://www.frederickhealth.org/Healthcare IT Community: https://www.healthcareittoday.com/

What happens to your private practice if you suddenly become incapacitated… or pass away?It's a difficult question, and one most therapists never want to think about.But in this deeply practical and important conversation, Mel sits down with Dr. Robin Miller to discuss one of the most overlooked responsibilities in private practice: creating a professional will.Dr. Miller shares the personal story that led her to found TheraClosure after unexpectedly losing a close colleague and stepping in to manage the aftermath of her private practice, client care, records, and continuity planning.This conversation is thoughtful, grounded, and incredibly relevant for every clinician in private practice.In this episode, we discuss:What a professional will actually isWhy every therapist in private practice should have oneWhat happens to client care and medical records if a clinician dies unexpectedlyThe emotional and ethical impact on clients after sudden therapist lossHow professional wills differ for solo practitioners vs. group practicesCommon mistakes therapists make when planning for continuity of careWhy “I'll deal with this later” can create major problems for loved ones and clientsHow retirement, incapacitation, and practice sales impact continuity planningWhat therapists should think about regarding passwords, EHR access, payroll, and two-factor authenticationWhy professional wills are about protecting clients — not just protecting businessesA powerful idea from this conversation:“We can't prevent loss. But we can prevent traumatic loss.”Very few conversations ask: What happens if something happens to you?This episode is an invitation to think proactively, ethically, and compassionately about continuity of care — for your clients, your loved ones, and your profession.--RESOURCES Building and managing the practice you truly want can feel overwhelming. That's why Alma is here—to help you create not just any practice, but your private practice.With Alma, you'll get the tools and resources you need to navigate insurance with ease, connect with referrals that are the right fit for your style, and streamline those time-consuming administrative tasks. That means less time buried in the details and more time focused on delivering exceptional care to your clients.You support your clients. Alma supports you.Learn more at sellingthecouch.com/alma and get 2 months FREE–an exclusive offer for STC listeners.--Ready to launch (or grow) your online course?Haven is our membership for therapists who want to turn their expertise into sustainable online income through courses, content, and simple systems that actually work.You'll get access to trainings, live accelerators, and a community that supports you every step of the way.Get on the waitlist: sellingthecouch.com/haven

This week on Pulse: Hot Topics, Louise and George cover a fortnight that captured the whole spectrum of digital health in 2026 — political turmoil at the top, consumer tech-led disruption from below, and an expert call for responsible AI delivery in the middle.UK Health Secretary Resigns as Palantir Contract Unravels — Wes Streeting resigns; James Murray becomes the 9th UK Health Secretary in 8 years; the £330M NHS Federated Data Platform faces a break clause as workforce, MPs and unions revolt. Reports emerge of Palantir staff being granted "unlimited access" to identifiable patient data, while the NHS Analysts Together collective launches an open letter calling for the contract to end.The Wearable Category Just Split Three Ways — Google retires Fitbit, launches Google Health with a Gemini-powered AI Coach and the $99 Fitbit Air, cross-platform with Apple HealthKit. One day later, WHOOP launches live clinician video consultations and EHR integration via HealthEx, backed by Mayo Clinic and Abbott. Meanwhile Oura quietly acquires Galen AI to build a longitudinal health operating system. Three completely different theories of where value sits in wearable health.Responsible AI UK: The Delivery Playbook — A BMJ Digital Health editorial from RAi UK sets out four priorities for execution: infrastructure and open standards, problem-focused innovation, holistic evaluation, and workforce capability. Essentially the operating manual the new UK Health Secretary should be reading tonight.Resources:Stryker Vocera's Initial Delays Diagnosis Quiz LinkResponsible AI UK, BMJ Digital Health& AI LinkDigital Health Workforce Census (opens 1 May, ANZ) LinkVisit Pulse+IT.news to subscribe to breaking digital news, weekly newsletters and a rich treasure trove of archival material. People in the know, get their news from Pulse+IT – Your leading voice in digital health news.Follow us on LinkedIn Louise | George | Pulse+ITFollow us on BlueSky Louise | George | Pulse+ITSend us your questions pulsepod@pulseit.newsProduction by Octopod Productions | Ivan Juric

Episode Summary Particle Health CEO Jason Prestinario joins MedCity Pivot to assess the state of U.S. healthcare interoperability with clear-eyed candor. He grades the technical infrastructure a B — data can move — but gives access governance a C, because the rules around who uses data, and how, remain murky and poorly enforced. Jason draws a direct line between true interoperability and the viability of value-based care: without frictionless data access, accountability for patient outcomes is impossible. The conversation also covers Particle's antitrust lawsuit against Epic, now past its first major legal hurdle, and the broader wave of litigation challenging Epic's market dominance. Jason urges nuance: there's a meaningful difference between patients authorizing their own data use and bad actors harvesting records without consent — and conflating the two risks setting back the entire data-sharing ecosystem. Key Takeaways The data infrastructure gets a B — but access governance is still a C. The technical pipes for moving health records exist, but who can use them, when, and for what purpose remains the critical unsolved problem. Interoperability is a 'nice to have' in fee-for-service care — but it's a hard requirement for value-based care. When a provider is accountable for outcomes that happen outside their four walls, they need data from outside those walls. Information blocking penalties need teeth. Until healthcare organizations believe violations will result in real consequences, the rules won't change behavior — just like speed limits only work when drivers believe tickets are real. There's a critical distinction between patients authorizing their own data use and third parties accessing data without consent. The current Epic lawsuit debate conflates two very different scenarios that deserve separate legal and regulatory treatment. True patient data ownership is still largely a myth. Despite portals and progress, patients still face significant barriers — forgotten logins, provider-controlled systems — to accessing their own medical records programmatically. Links and Resources Connect with Arundhati Parmar aparmar@medcitynews.com Arundhati Parmar (@aparmarbb) on X MedCity News Keywords healthcare interoperability, Particle Health, Jason Prestinario, Epic lawsuit, antitrust healthcare, value-based care, CMS interoperability, TEFCA, Carequality, health data access, information blocking, 21st Century Cures Act, patient data ownership, HIPAA compliance, health information exchange, payer interoperability, digital health data, EHR data sharing, CommonWell, ONC rules Episode Highlights [00:04:22 - 00:05:16] Jason grades the interoperability 'pipes' a B-plus but gives data access governance a C at best. [00:10:56 - 00:12:37] Interoperability shifts from 'nice to have' in fee-for-service to a hard requirement in value-based care. [00:17:05 - 00:19:27] Jason explains why Particle sued Epic and what the case means for the broader healthcare data ecosystem. [00:25:11 - 00:27:11] A key distinction: patient-authorized data use versus unauthorized third-party data harvesting. [00:28:34 - 00:32:44] Why patients still can't easily access their own records — and what it would take to change that. [00:29:02 - 00:29:41] Information blocking penalties only work when organizations believe the consequences are real.

Dr. Sherif Taraman on Fixing the Primary Care Shortage: Economics, Culture, Policy, and TechnologyIn a Tuesday-morning discussion, repeat guest Dr. Sherif Taraman (dual board-certified child neurology and clinical informatics; CEO of Cognoa behind FDA-authorized Canvas DX for autism) joins Herb and George to examine the U.S. primary care shortage. They argue the core driver is broken health economics: low reimbursement, outdated CPT practice-expense assumptions, high overhead, time-strapped 10–15 minute visits, and EHR-driven administrative burden that pushes volume over prevention and fuels burnout, direct-care models, and consolidation or profit-driven ownership. They discuss loss of generalist skills, outdated training and regulation, medical debt discouraging primary care, and cultural preference for quick fixes over preventive care. Potential solutions include clinician-driven tech (e.g., ambient scribes), collaborative care scaffolding with reimbursable codes, more flexible retraining and licensing reciprocity, better education in health economics/population health, and restoring patient-physician relationships to rebuild trust (e.g., vaccines).00:00 Welcome Back Sherif01:43 Why Primary Care Matters03:44 Reimbursement Drives Shortage05:29 Generalist Skills Fading07:58 Outdated Rules and Costs11:11 Practice Models Shifting13:14 Workforce and Policy Crunch14:47 Tech Incentives and Burnout17:54 Collaborative Care Scaffolding19:03 Culture Debt and Training Reform26:17 Reinventing Physician Careers27:44 Credentialing Roadblocks28:46 Regulation Versus Access29:45 Modernizing Training Models30:52 Paying Primary Care Right32:55 Telehealth Licensing Mess33:47 Learning Without Certificates34:52 Screening Belongs Upstream36:34 Workforce Fixes And Scope39:48 Guidelines And Critical Thinking42:44 Medicine As Art And Trust48:49 EHRs Billing And Burnout49:44 Closing Thoughts And ActionSupport the show

In this video, Paul L Wilder, Executive Director of the CommonWell Health Alliance, discusses the spread of health data exchange as it involves not just providers but new actors such as payers, public health, and patients themselves.CommonWell, a nonprofit QHIN that started in 2013 and has an enormous reach today, contains IT vendors ranging from startups to big EHR vendors, and providers now as well. For a long time, Wilder says, EHRs supported only unidirectional data exchange: they would allow it to be extracted but not inserted. Now it's more bidirectional.Learn more about CommonWell Health Alliance: https://www.commonwellalliance.org/Healthcare IT Community: https://www.healthcareittoday.com/

This month on the My DPC Story podcast we are talking about the tools that serve us, and Dr. Michael Hobbs is a voice you do not want to miss.Dr. Hobbs is a pediatrician and founder of Lakes Pediatrics, the first pediatric Direct Primary Care practice in the Minneapolis area, serving families across Edina, Wayzata, and the western suburbs. He brings over twenty years in Twin Cities pediatrics, more than a decade as an adjunct professor at the University of Minnesota Medical School, Top Doctor recognition from Mpls.St.Paul Magazine and Minnesota Monthly, subspecialty training in infectious diseases and Group A Strep, and a Reach Institute mini-fellowship in pediatric mental health care.What makes this episode essential listening is Dr. Hobbs himself. A self-described knowledgeable hacker who grew up alongside the technology, from a Commodore 64 to writing early web pages, he has watched the entire arc of medical documentation: index cards, paper charts, dictation, the EHR, templates, and now AI scribes. He knows what gets better and what gets worse when tech enters the exam room.In this conversation, Dr. Hobbs covers:The one question to ask before adopting any tool, EHR, phone system, or AIWhy building your own tools is more doable than you thinkWhy now is not the time to lock into a long term software contractThe difference between AI that serves you and AI that turns you into a liability machinePatient transparency, shadow AI, BAAs, and using tools safelyWhy LLMs are terrible at math, learned the hard wayThe best first AI investment for a new DPC doctor on a small budgetAI as a clinical decision support thought partner, not a guideline machineAnd because both Dr. Concepcion and Dr. Hobbs are recovering anthropology buffs, they keep returning to the truth underneath the technology: people like people. The tools only matter if they give us more room to be human with the families we serve.Whether you are deep into building AI workflows or you hear the word AI and want to run, this episode meets you where you are.New to DPC or ready to go deeper? Visit the Start Here page at mydpcstory.com. Have a question for the show? Leave a voice message on the Contact page. Loved this episode? Leave a five star review on Apple Podcasts and follow @mydpcstory on socials.Connect with Dr. Hobbs at lakespediatrics.com.Learn more about VIVID VAULT HEALTH SOLUTIONS TODAY! Find a My DPC Story Event near you! State Summits in CA, IL, a My DPC Story LIVE event and the DPC Women's Summit are all coming! Learn more at mydpcstory.com/upcoming-events! The DPC Directory: If you're a DPC doctor, you'll find resources to grow your practice! If you serve the DPC world, grab a FREE listing today and get discovered by doctors who need your services.

If charting is taking over your evenings, weekends, and mental space, this episode is for you. Dr. Jessie Mahoney, board-certified pediatrician, physician coach, and former physician wellness leader at Kaiser Permanente, shares a mindful, sustainable approach to clinical documentation that doesn't require working faster, working later, or another productivity hack. Most charting advice for doctors focuses on efficiency tools, templates, dot phrases, AI scribes, and time-blocking. Those things help. But they don't address the real reason so many physicians chart late into the night: the nervous system state we are in when we sit down to chart. In this episode, you'll learn: Why charting feels harder than it should — even when you know what to write The mindset shift that makes notes flow instead of stalling How perfectionism, people-pleasing, and over-explaining quietly inflate every chart A presence-based practice you can use between patients to reset Why "charting with ease" is possible without sacrificing clinical quality This conversation is for women physicians, primary care doctors, pediatricians, family medicine, internal medicine, hospitalists, and any clinician who is tired of pajama-time charting and wants a sustainable, human approach to EHR documentation. Nothing shared in the Healing Medicine Podcast is medical advice. The Healing Medicine Podcast was formerly known as the Mindful Healers Podcast. If this resonates, go deeper with 1:1 Coaching: www.jessiemahoneymd.com/ Connect in Nature & CME Wellness Retreats: www.jessiemahoneymd.com/retreats Free Live-Stream Mindful Yoga: www.jessiemahoneymd.com/yoga Blog: www.jessiemahoneymd.com/jessies-blog

The Indian Health Service (IHS) is modernizing its electronic health record (EHR) systems while strengthening cybersecurity to protect sensitive patient data. IHS CISO Benjamin Koshy joined GovCIO Media & Research at CyberScape: The Federal Cybersecurity Summit to discuss how his agency is building a culture of cyber awareness among clinicians and staff. Koshy also outlined the challenges of securing telehealth services in remote environments and how AI is helping cybersecurity teams more quickly identify and respond to threat actors.  

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

This episode is sponsored by GoTo: If your practice is losing patients to missed calls and front-desk burnout, you're not alone. Most independent practices miss around 30% of incoming calls, that's 30% of your patient pipeline walking to the clinic across the street.  GoTo Connect consolidates your phones, patient messaging, video visits, and scheduling into one platform that helps you stay HIPAA-compliant. AI receptionist. EHR integration. No IT team required. Built specifically for physician-owned practices.  See how it works: goto.com/healthcare   ————————- In this episode of Bootstrap MD, Dr. Mike Woo-Ming sits down with Adam Peeler, Director of Product Management at GoTo Technologies, to unpack the communication challenges quietly draining revenue and efficiency from independent medical practices. From missed calls and overloaded front desks to AI receptionists and EHR-integrated workflows, Adam explains how small clinics can modernize patient communication without building an expensive IT department. Together, they explore how AI-powered communication tools are already reshaping healthcare operations for solo and small-group practices. Adam breaks down the real-world impact of missed calls, fragmented communication systems, and outdated workflows, while sharing practical ways physicians can automate scheduling, reduce staff burnout, improve patient engagement, and increase operational efficiency. The conversation also covers HIPAA compliance, the rise of AI receptionists, predictive staffing, and why communication systems should be viewed as a revenue-generating asset rather than just another utility bill. Whether you're starting a practice or trying to scale one, this episode offers a practical roadmap for leveraging AI and modern communication infrastructure to create a better patient experience and a more profitable clinic. Top 3 Key Takeaways: Missed Calls Are Missed Revenue: Many independent practices unknowingly lose thousands of dollars every month simply because calls go unanswered or patients abandon the scheduling process. Adam explains that some clinics miss up to 30% of inbound calls, creating significant financial leakage that most physicians never measure. AI in Healthcare Is Already Here: AI is no longer a future concept reserved for large hospital systems. Small practices are already using AI-powered communication tools to answer calls, summarize patient interactions, automate scheduling workflows, and reduce front-desk overload without hiring large IT teams. Technology Should Support, Not Replace Human Care: While AI can handle repetitive administrative tasks and FAQs, complex scheduling decisions, emotional conversations, and nuanced patient interactions still require human judgment. The most effective practices will combine AI efficiency with compassionate patient communication.   About the Show:   Bootstrap MD is the ultimate podcast for physician entrepreneurs looking to escape traditional healthcare and control their financial futures. Hosted by Dr. Mike Woo-Ming, a successful physician, entrepreneur, and investor, the show delivers actionable insights on starting businesses, creating passive income, and navigating healthcare entrepreneurship. Featuring interviews with industry leaders, physicians, and experts in telemedicine and digital health, it's your guide to building a profitable, fulfilling career.  Tune in weekly at  http://bootstrapmd.com     About the Guest: Adam Peeler is the Director of Product Management at GoTo Technologies, where he leads product strategy for cloud-based communication tools designed for healthcare practices and service-driven businesses. His work focuses on AI-powered communication systems, phone platforms, messaging, scheduling workflows, and EHR-integrated patient engagement solutions that help independent practices modernize without requiring enterprise-level IT resources. Website: goto.com/healthcare   About the Host: Dr. Mike Woo-Ming has over 20 years of experience as a physician entrepreneur. He's built and sold multiple seven-figure companies and now leads Executive Medical, a group of clinics specializing in age management and aesthetics. Through BootstrapMD, he mentors physicians in business, content creation, and autonomy. Let's Connect: www.https://www.bootstrapmd.com  Want to start a podcast? Check out the Doctor Podcast Network!

The Big Unlock · Bharat Sutariya, MD, Senior Vice President and Chief Health Officer, Oracle Health In this episode, Dr. Bharat Sutariya, Senior Vice President and Chief Health Officer at Oracle Health, discusses the radical transformation of healthcare through AI-native digital platforms. As an emergency physician with over 25 years of experience, including leadership roles at Cerner and Deloitte, Dr. Sutariya provides a unique perspective on moving past the “burden” of legacy EHR systems. The core of the conversation centers on Oracle's bold bet: moving away from the industry-standard “bolt-on” AI approach. Instead, Oracle is rebuilding the healthcare stack from the ground up, embedding AI into the foundational layer. Dr. Sutariya argues that the future of healthcare technology isn’t just about capturing data but about systems of orchestration. This means AI that doesn’t just transcribe a note but listens to the clinical intent to automatically queue orders, handle referrals, and initiate prior authorizations. Dr. Sutariya predicts that within a year, the conversation will shift from documentation efficiency to a truly connected, intelligent ecosystem that gives time back to both providers and patients. Take a listen. This guest appearance was facilitated through conversations initiated at HIMSS.

AI may be the hottest topic in healthcare, but most organizations still aren't ready to use it at scale.  In this episode, host Sandy Vance chats with the CEO of Hart, Dominique Gross. Together, they break down the real barriers holding healthcare back: from fragmented data and legacy EHR systems to inconsistent standards and limited access. Dominique shares how building a semantic data layer is helping organizations unlock their data, scale innovation safely, and move from pilot projects to real enterprise impact. In this episode, they talk about: Many healthcare organizations are eager to adopt AI, but they struggle to scale beyond pilot programs due to foundational data challenges. The most immediate barrier to innovation is simply gaining access to all relevant data across fragmented and legacy systems. Data quality is just as critical as access, as organizations must normalize and clean their data before it can be used effectively. Legacy electronic health records often contain valuable historical data, even if organizations are hesitant to use it due to inconsistencies. Patients ultimately benefit when their full medical history is accessible, rather than only recent encounters. HART has developed a semantic data layer that acts as a “translation system” across different EHRs, enabling consistent data use. This approach allows organizations to aggregate, migrate, and stream data more efficiently across dozens of systems. One health system successfully scaled from connecting a small number of affiliates to centralizing more than 60 data sources over time. The same organization was able to complete a major EHR migration in under 12 weeks by extracting, normalizing, and preparing data for a new system. Proprietary data models within EHR systems create significant barriers to interoperability and data portability. Despite increasing regulation and improved standards, accessing complete and meaningful data remains a challenge across vendors. Market consolidation is likely to continue, as organizations seek fewer vendors that can handle multiple data needs. Clinical research remains an underutilized opportunity, with many organizations still relying on manual processes to identify eligible patients. Improving data accessibility could dramatically accelerate patient recruitment and engagement in clinical trials. Simple improvements in data completeness and standardization can have immediate impacts on reimbursement, efficiency, and patient care. A Little About Dominique: As CEO of Hart since 2023, Dominique Gross provides healthcare data leadership, guiding the company's mission to eliminate healthcare data fragmentation and empower organizations to achieve true interoperability. With more than two decades of experience driving health IT innovation and go-to-market strategies, Dominique has shaped Hart's focus on making the impossible possible every day, ensuring that every innovation at Hart advances both operational efficiency and equitable patient care.  Prior to joining Hart, Dominique led strategy and growth initiatives at multiple firms, working as one of the industry's prominent healthcare technology executives to launch connected care solutions for veterans and major health systems. Her leadership philosophy centers on collaboration and integrity, guiding Hart to serve as both a technology provider and a trusted partner for healthcare's most complex data challenges. Dominique is a frequent speaker on the future of healthcare data ecosystems and a strong advocate for patient empowerment through healthcare data accessibility. 

What actually creates long-term impact in chiropractic: better systems or deeper conviction? In this conversation, Dr. Lona and Dr. Jack Bourla unpack why Sherman College continues to attract students who carry a strong sense of purpose into practice, and why culture inside a chiropractic school matters as much as curriculum. They discuss preserving chiropractic principles while expanding into areas like upper cervical, sports, family, and animal chiropractic, along with the responsibility chiropractors have to create demand through certainty, enthusiasm, and service. The conversation ultimately points back to one reality: when chiropractors fully understand what they do and why they do it, practices grow differently, communities respond differently, and the profession moves forward with more momentum. Key Highlights 00:57 – Hear why Dr. Jack Bourla says culture matters more than curriculum and how the feeling students experience on campus shapes the kind of chiropractor they become in practice. 03:15 – Understand the “reverence for chiropractic” Dr. Lona noticed at Sherman and why younger doctors with clarity and certainty communicate differently with patients. 04:57 – Learn how chiropractic schools create referrals through vision, not marketing when students deeply understand their role and communicate hope with conviction. 06:57 – Discover why Dr. Bourla watches the student parking lot every morning and how leadership inside a school can shape future practice leaders and community impact. 09:13 – See how Sherman is building focused specialties inside chiropractic through upper cervical, sports, family, and animal chiropractic while maintaining principled foundations. 11:24 – What happens when chiropractic care is framed as family care instead of pediatrics and how that shift changes generational retention inside practices. 15:32 – Learn why Dr. Bourla believes enthusiasm is the profession's greatest growth strategy and how contagious certainty influences patient trust and practice growth. 18:24 – Hear Dr. Lona explain why the public may be more ready for principled chiropractic than the profession itself and why chiropractors must stop playing defense. 19:13 – Understand the difference between creating ripples versus tidal waves in practice growth and why demand for chiropractic must outpace supply for the profession to expand. 21:38 – Discover why Sherman prioritizes campus visits so heavily and how environment, culture, and alignment influence the future trajectory of a student's career. 23:48 - Dr. Lona sits down with Dr. Brian Capra from Success Partner ClinicMind to explore how chiropractic practices are evolving beyond traditional EHR systems into fully integrated growth platforms. They discuss how AI, automation, patient communication, and practice management are transforming attraction, retention, and scalability for modern clinics. Dr. Brian also shares how ClinicMind is helping practices simplify operations, improve patient experiences, and prepare for the future of chiropractic growth through one connected platform. Resources Mentioned For more information on Sherman College, please visit: https://www.sherman.edu/ For more information about ClinicMind please visit: https://www.clinicmind.com/ To schedule a Strategy Session with Dr Lona: https://go.oncehub.com/DrLonaBuildPodcast To schedule a Strategy Session with Dr Bobby: https://go.oncehub.com/DrBobbyBuildPodcast Learn more about the Remarkable CEO Podcast: https://theremarkablepractice.com/podcast

Navin Gupta is the CEO of Viventium, a verticalized HCM platform purpose-built for the post-acute care market — serving home health, skilled nursing, and hospice providers. He's spent over a decade at the intersection of senior care and technology, with deep experience across EHR, revenue cycle management, and engagement platforms for senior living. Adam Lewis is the founder of Apploi and now GM of Talent and Workforce Management at Viventium following the February acquisition. He's been building HR tech since 2007 and grew Apploi into a leading recruiting, credentialing, onboarding, and scheduling platform for healthcare. Together, the combined company now serves 13,000+ provider organizations and is on a mission to fix workforce instability in the most demographically urgent corner of healthcare.We discuss:Why post-acute care is the most mission-critical — and most underserved — tech opportunity in healthcareThe four-part workforce crisis every operator is fighting: supply, utilization, retention, and complianceWhat the Apploi + Viventium acquisition unlocks that a five-year partnership couldn'tWhy hiring friction is a direct hit to revenue — and why staffing now sits with CEOs and COOs, not just HRThe case for purpose-built vertical platforms over retrofitted horizontal HCMThe Perks4Care acquisition, and why you cannot hire your way out of a retention problemWhere AI creates real leverage in caregiver hiring — and how to deploy it without losing the human touchThree audit questions every post-acute provider should ask their current vendor today—Brought to you by:Sage Growth Partners — Value-focused strategy and marketing for growth-driven healthcare organizations.—Where to find Jared:• X: https://x.com/jaredstaylor• LinkedIn: https://www.linkedin.com/in/jaredstaylor/

https://youtu.be/oPA1dSUab9Y James Green, CEO of Cognome and former Pixar executive under Steve Jobs, is driven by a deep curiosity and a pull toward ideas that can create massive impact. From early internet ventures to mobile innovation and now AI in healthcare, James has consistently aligned himself with transformative trends. In this episode, he shares hard-earned lessons from scaling multiple companies and introduces a simple but powerful framework that explains why many startups struggle to grow beyond their early stages. We explore James' 3-Stage StartUp Growth Framework: Whiteboard Phase, PowerPoint Phase, PDF Phase—a model that captures how organizations must evolve as they scale. He explains why early-stage chaos is necessary, how structure begins to take shape in the middle phase, and why standardization becomes critical at scale. James also dives into the toughest leadership challenges—especially making difficult people decisions—and shares why aligning with strong market tailwinds and creating “pull” from customers is essential for sustainable growth. — Grow Your Business in 3 Phases with James Green  Good day, dear listeners. Steve Preda here with the Management Blueprint, and my guest today is James Green, the CEO of Cognome, a health tech company that is solving the problem of how to manage different AI models that are being deployed in healthcare today. Earlier, he worked as a vice president at Disney. He worked directly under Steve Jobs at Pixar, and he has had at least six other CEO roles in ed tech, media, and healthcare. Welcome to the show, James.  Thank you very much. Delighted to be here.  Yeah, super excited. And Steve Jobs—you don't often have people that have known Steve Jobs now even Tim Cook has resigned. Yeah. Yeah.  And it’s 13 years, I guess. Steve Jobs is being gone. So what was it like working with the man? Was he a difficult boss?  First of all, most of the things you hear about him are accurate. So it’s not one of these things where you hear a lot about Steve Jobs and actually the man was totally different. So most of what you’ve heard is true. And I’ll give you one short anecdote sort of before we go on, which is something that I always found incredibly impressive about him. When you work for him, if you disagreed and said, “Hey, you want it to be white, I want it to be black,” without hesitation he would say something like, “Here are seven reasons why you're wrong.” First of all, before we go into those seven reasons, what’s impressive about that is he had a number and he stuck with it.  And it happened in seconds and he didn’t know before. So if you think about that, it’s hard to keep all of that in your head. So the guy was just super, super clever. And then he would list them 1, 2, 3, 4, 5, 6, 7, and you’d be out. Like it’s done. It’s like, “Oh, damn.” So yeah, he was unbelievable human, and it was an honor and a privilege to have worked with him.  Yeah, well, that's awesome—to talk to you, having worked with him and having some direct experience. Definitely not an easy boss when he has seven guns to shoot you down. Yeah.  But there's a lot to learn. I mean, you learn the most from these kinds of bosses.  Yeah.  So let's get into the question—which is normally the first one, but this is the exception: What is your personal “why,” and how are you manifesting it in Cognome, James, and in your previous jobs?  Yeah, I've thought about this a lot. I've tried to come up with what my “why” really is. And what I’ve come up with is I can’t help myself. And I’m going to go through examples of it and what I mean by that. I pay a lot of attention to the world. I pay a lot of attention to what’s going on. I get very seduced by new ideas and new things and things that I think will have big impact. And once I start thinking about it and thinking about what that impact is, I cannot help but start getting involved in it. That sounds very abstract, so I want to try to make that super concrete. So when I was working at Pixar, for example—the internet was being born. This is the late '90s.  I couldn't help myself. I started an ad-serving company called Sabela Media. That company got sold to 24/7, then to DoubleClick, which later got acquired by Google. So the internet was there. I had to do it. I had to have something in it. Then after that, I was thinking about what to do next—and mobile phones, if you remember, were still flip phones, mostly used for texting. The second company that I did was putting content onto those phones. It just seemed obvious to me—I couldn't help myself. I saw the opportunity, and it clearly worked. That company was called GiantBear. It was sold to BlueCora. After that, there was this crazy innovation going on in television of all things with effects. Now, again, we take these things for granted. We’ve got AI creating things all day long, back in the day, we didn’t. So I ran a company called PVI, which is famous for inventing the first-down line you see in football games. So that was kind of the very first virtual object you saw in live things. Again, it may seem like, oh, that’s an everyday event, but back in the day it was totally not. And I think it opened up football to many more people—you no longer needed the chain crew to understand what was going on. And then if we fast-forward—there are a few things in the middle, but I don't want to bore everyone—to where I am today at Cognome. I even wore my little Cognome shirt so I could advertise it throughout the podcast.  Yeah, that's smart. I have to do that.  AI is clearly the big thing today. But for me, intellectually, it's not enough to just say, “I'll do an AI model,” like everyone else. For me, healthcare is one of the areas that AI will have the biggest impact with. Healthcare for a lot of reasons has been a laggard technologically for specific things about how they store data, so it hasn’t been adopted things like multi-tenant SaaS, because the data has to stay local and things like this. So AI will revolutionize it. And AI will make decisions about whether people live or die, right? So it's really consequential. And for me, the question is—how are you going to manage that? That's a super interesting intellectual opportunity. And so Cognome ExplainerAI. So my “why” is: what's going on, what's interesting, and what's changing the world? And the beautiful thing about that is you get a “rising tide lifts all boats” situation. You're not fighting against a trend—you're moving with it. The whole world is rising, and you can be part of that. That’s sort of my “why”.  Yeah, so basically—in other words—it's about coming up with revolutionary ideas and implementing them?  Yeah. I mean, I want to make an impact in the world. I want to make a difference. I'm not a very religious person—in fact, not at all. So I believe our time here is limited. I want to make a difference. I want to be part of what's going on. So yeah, that's my “why.”  Yeah—tapping into trends. Well, that’s great. I mean, don't know if it's a “why,” but making the most of the opportunity to be here and maximizing impact—that's a huge one. Love it.  Yeah.  STEVE PREDA: So let me segue to the next one. This podcast is all about frameworks. So the objective here is what’s a shortcut that you can teach the listeners that they can implement in their business? So what is your “shortcut” to success? Maybe “shortcut” is the wrong word. What is the framework you use to interpret the world, understand it better, and make decisions?  Yeah, this is another thing I struggled with a little bit. So I listened to your questions, and I tried to make my answers really personal. I'm trying to be authentic—this is what I actually do all the time, as opposed to this is what I’m doing at the moment, or this is what I did for a second. The truth is, frameworks come and go. There are a lot of frameworks out there. I've probably used 15 different sales frameworks. I mostly operate in the B2B world, so there are lots of frameworks you can use—for example, in sales. But I tried to think of something more consistent—a framework I've used across every company I've worked with, all the time. And the one I always come back to is about growth. So what I want to talk about is: how do you manage a company that's going through growth? Because it's not obvious—and I do have a framework for it. And unlike some of the other frameworks—like something McKinsey, Bain, or someone’s invented this framework and you are adopting it. This is really pretty personal to me, and I’ve adopted various little things about it. There are these two ideas that live in parallel. One is in the sales process, where I think companies go through this idea of, I call it a Whiteboard sales process, a PowerPoint sales process. And forgive me for being a little dated, but a PDF style process, something you can’t change. And at the same time, they go through these stages where you are a small company, a medium-sized company, and a larger company. Think of it roughly as fewer than 12 people, then 10 to 75, and then 75 to 100 and beyond. And I’ve managed all of these sizes. And what’s interesting about these is that if you don’t have a framework to manage yourself through these stages, you’re going to fail. You as a leader will be replaced. I personally have replaced leaders who cannot go through those kinds of things. One of the things I've done in my career is act as a sort of hired gun for VCs. They make an investment, and then they bring me in to replace the founder if they haven't been able to navigate that growth stage. And so the framework works like this. When you're starting a company—what I call the “whiteboard” phase—what you're selling is a little different every time. And the consequence of that inside the company is everyone is doing everything. It’s a little chaotic and it’s okay. Like, less than 10 people, it’s okay. It’s okay that the finance person is doing a little selling and the engineer is doing a little marketing. It’s okay, because you only have 10 people maybe. When you go into a client, you are sort of inventing yourself as you go. There's always that first client where you're saying, “I think we should do this. This is how I'm going to help you make money, save money, or do something better.”  You’re figuring things out. Yeah.  And maybe there's some pivots in there. Maybe there isn't. Not everyone gets to be Google and get it right the first time, but you’ll see. In the end, you start getting things right. And then you go through what I call the PowerPoint phase. So what this is—you now have more than 10 people. It kind of isn't okay that the sales guy is doing finance, or the engineer is doing marketing. You actually have people in their swim lanes. I call it the PowerPoint because you've built PowerPoints, so you’ve got slides that you can use and it’s replicable. Guess what? You tend to tweak them for each client. You are still—you know what—the way you're selling to… I don't want to make a stupid example up—Home Depot is still a little different than selling to Lowe's. You know that—even though it should be exactly the same—it's still a little different. You're tweaking it each time. You're moving slide three to slide seven. Sometimes you don't show slide 10. You're still tweaking it.  Yeah. I relate to that.  And your organization is structured, but not completely rigid. Everyone still knows each other in the company. It's up to maybe 50—I think it maxes out at about 75 people. But every single person in the company knows each other. They’re all collaborating. You don’t need a lot of structure inside the company because there’s sort of culture in there to hold everyone together, right? And then you get to the third stage, which I call the PDF stage—where you've figured it out. You sell the same thing. Maybe you have three PDFs because you're selling in three verticals. But you go into a client—this is the thing—and it never changes. Slide one is always slide one. Slide two is always slide two. Slide three is always slide three. And you have maybe a hundred people in your company. And by the way, now you have levels. So not everybody knows everybody. And as a CEO, I have my lieutenants. My lieutenants have people working for them. And I sort of feel like everyone can manage—I don't know—five, six, seven, eight people. More than that is difficult unless the roles are not very sophisticated. So you need this management layer, which separates the CEO from the rest of the organization. So you need a lot more structure. And as you go through these three phases—and they're really different—a tragic thing happens. It happens all the time. The person who was so helpful in the whiteboard phase, who was your go-to person, they don’t make it in the third phase because they’re a generalist. They liked the chaos. They liked being able to have their foot, and they’ll complain to you. They'll say, “Why aren't you listening to me?” It's an engineer saying, “Why isn't sales listening to me?” Dude, you're an engineer—stick to your knitting. Like, no. And this culture goes through every single company I’ve ever run. Most of them have gone through these three phases—small, medium, and large. And one of the things I try to do with employees in these phases—and this is part of the framework—is to give them a huge amount of latitude to see if they can succeed in the phase. So, to give them the freedom—if you're being blunt—to give them enough rope to hang themselves. And if you're being kind, to give them the freedom to be who they are, to be the best they can be, and to support them—not control them. And so, if you are aware of this framework as you grow, and you give that latitude, and you hire smart people, then you can see which ones you keep and which ones you don't. And honestly, the worst and hardest part of managing through growth is that selection and weeding-out process—of the people who worked in the first stage but don't work in the last stage. So that is the only kind of framework for me that has stood the test of time. It has worked in media, worked in healthcare, and worked in various other places. Does that make sense to you? Does it resonate with you?  Absolutely. And I was just working on a chapter in my new book, and I was actually writing about this very idea—why some companies are never able to grow, because they are not able to make these decisions, these painful decisions, as you described.  Super painful—the worst. It’s the worst part. Firing people is the worst part of being a CEO. If you enjoy that, you’re a bad CEO. You want to have a positive environment, so you want to everyone have a good time. And when there’s growth, usually there’s incredible optimism and great culture. So any CEO who enjoys that process is not a good CEO. Yeah, that’s so true. This is kind of a difficult thing. You have to be ruthless to some degree.  You do. Yeah. That's why this framework has helped me—and it's helped me be gracious and kind to people. Let's just call her Jane, right? A totally fictitious person. But you can go to Jane in stage three and say, “Jane, do you remember how much you loved it in the first phase?” I'm going to give you some time here. You are going to leave, but I'm going to give you some time to work on a special project. But you also need to find your next startup—because you love that environment. And I am going to put this bureaucracy in place, and you're going to fight it until the day you die. So I can't have you here—I just can't. I can give you this little thing to do and you can have some weeks to go do that and give you some time, but the framework helps you be gracious and helps you make those decisions as you grow. That’s an amazing framework. This is really unique. We've recorded, I think, close to 400 episodes with different frameworks—and this hasn't come up. Nothing similar has come up.  Woo-hoo.  Love it. So where are you now in your business? Which phase are you in?  I am in between the whiteboard and the PowerPoint phase. Maybe because I'm an optimist, I'm going to say I'm in the PowerPoint phase. But I know there's still part of me that's drawing things on the whiteboard. We have 12 people, so we're just at the edge of growing out of that phase. I don’t have that layer in the middle. We have half a dozen clients. I suspect that by the end of this year, we'll be fully in the PowerPoint phase. And it'll be another 18 months after that until we get to the next stage—and that's assuming we continue to grow. I mean, my whole raison d'être is to find these really special things, grow them, and make an impact. So let’s hope that happens. Yeah, well, you've had some practice in your previous six CEO positions, so I'm sure you'll figure this out. So what drives growth in your business?  Yeah, this goes a little bit back to phase one. So I've picked an area that's growing by itself. I mean, AI—there are more and more models being deployed in hospitals. Hospitals are growing. The number of models deployed in them is growing at about 2.2 times the rate of the general population. So good for me. There are federal regulations coming that say you need to control what your AI models are doing. That's also good for me. It's a lovely day when regulation is good for your business—it usually isn't. But it's not unusual in healthcare. If you look at electronic health records, that was driven by government regulation and funding. So this is a little bit like that. Federal, state, and other institutions are driving this trend. And then there are things happening inside healthcare organizations themselves that we can tap into. I always think that when you're selling, you should have a good story. So I'm going to tell you the kind of story we use.  When we meet with a chief information officer, we tell stories like the ones I'm about to share. And this really helps us tap into that growth. Because part of growth in a B2B environment is having a strong sales team, good engagement, and solid frameworks—like: do they have budget? Are you talking to the right decision-maker? All of those kinds of frameworks, which to me are more tactical—I've used a lot of them. But we go in and say things like: “Have you ever experienced a situation in radiology where a new model was released and no one told you about it—and now you have to monitor it?” This is happening. And they're like, “Oh my God—yes.” And then they tell you a story about it.  And then you say, “What about that note from CMS?”—that's the organization that runs Medicare and Medicaid, for those not in healthcare. “Did you hear that they're coming down to audit some of your peers?” And they're like, “Oh my God—we just got notice that we're being audited.” And then—how about your board? How's your board doing? Are they coming down and saying, “What are you doing in AI?” So you try to tell these stories and then you create this tension, where they have to grow and they have to control, and then that’s where we come in. We can help all of these companies manage all of these models. What we do—we have this product called ExplainerAI. We tap into the underlying data from the electronic health record—the EHR, or medical record. We tap into the models—the front end—and the logging files behind them. And then we can tell whether the model is exhibiting drift, and how it's performing across different areas. That could be geographic areas, or demographic areas. Is it performing the same with young men and older women? Is it performing the same over time? Is it degrading? Is it releasing personal health information when it shouldn't? Is it hallucinating, if it's an LLM? That’s what we do. And then we can send alerts out to people, saying, “Hey, listen, this model is making shit up right now, you need to deal with it.” And then they can talk to the vendor and handle it. So we're in a good space. And so growth is, to some extent, this idea of a rising tide lifting all boats. I've picked an area that's growing, so I can grow with it. And then part of it is being connected and having a good way of engaging with people who are buyers. And so we have these stories that we tell in our decks about how we help in these situations.  Have you had to pivot between the original idea and where you are?  Yeah, we have. And for anyone who's listening and thinking, “Oh my God, I'm going to have to pivot,” I use Google as my favorite example of someone who just got so lucky. They were like, “We're going to have this little thing that searches the internet,” and they never really changed—until they got so big they could do more. That is the exception, not the rule. And what’s interesting about the way we started is it’s still a core differentiator for us—we started with the ability to take data from an EHR, from a medical record, translate it, and store it in a common data model. It's called OMOP. It's the most common way that researchers structure this kind of data.  And we thought this technology would be widely adopted by researchers. We have contracts with people like Hopkins, Ohio State, NYU—big institutions—but it's not big enough. It’s not going fast enough. What it does do, though, is for our ExplainerAI, it gives us the technology—it's a moat—to connect to the source of truth, the electronic health record, so that you can get actual outcomes versus predictions. Many models cannot get the actual data out of the EHR. So they just say, “This is my prediction, this is my prediction, this is my prediction.” And over time—that's fine, those are predictions—but how do they actually compare to what really happened?  Yeah. What actually happened? And because of where we started, we have a way of efficiently and accurately getting that information. So it is still the bedrock. But it's definitely a pivot. And then you basically put an AI layer on top, and that's great. And how did you know when to pivot? How do you reach that tipping point? How do you know this is the moment—you have to pull the plug on this because it's not working?  First of all, I think on a personal level, I'm always late. So I think I could always have made this decision earlier. If I'm being self-critical at a high level. And I don't think I have a clean answer—but I'll tell you how I've done it. If you have a better way, I'd love to know. It’s about sales engagement. So you go to a hundred people, you have a hundred meetings, and you sell to two. That's not good enough. It's just not good enough. And those two are complaining. What you want to see in a product—and I think this is true of all great products, especially today—I use examples like Facebook and Tesla—is that products are pulled, not pushed. If you still find yourself, after nine months, pushing—and you don't have the momentum where your product is being pulled—you're wrong. You need your clients to be making referrals, and you need to be pulled into deals. In today's advertising and marketing world, it's too noisy.  Maybe back in the seventies you could do it, but now it's just too noisy—especially in B2B. There are so many people selling to the same buyers that they need to hear about your product from others, have people around them recommending it, and pulling you in. There's some time—and I usually take closer to a year, which is long. It would be better for me to do it in six months or even three months. I haven’t found a way to do that where you pivot if you’re just not getting traction, basically.  Yeah, okay. I love it. So what's one thing in your company that you're trying to figure out right now? One thing in my company that I'm trying to figure out right now is how to further ramp up sales. I'm cheating a little bit here, because I think we may already have it figured out—but leaving you with an unanswered question isn't very helpful. So we were having—and still are, to some extent—problems getting ExplainerAI rolled out. People were interested in it, but they wouldn't buy. So we tried to figure out why. And one of the things we found is this: For those of your listeners who may not know, healthcare is probably the largest portion of GDP in the country. Buyers are very large. We don't always think about it this way, but if you do—everyone goes to the doctor. It affects 100% of the population. And these large institutions—a hospital is usually a multi-billion-dollar organization—and there are about 6,500 of them in the country. So we've got 6,500 multi-billion-dollar companies in this country. It’s crazy, right? They don't want to buy from small companies—they want to buy from big companies. This is one of the things we found out. So we get to the finish line, they say yes—and then no one tells you the truth, right? No one says, “I'm not buying from you because you're small.” But we ended up figuring it out through triangulation. So we've been building partnerships. We started with Intel. We made some of our models work on Intel CPUs, and I'm actually pretty proud of that work. For the nerds out there—we're working on Xeon 6 chips, the Granite Rapids chips—running locally deployed LLM ensembles. Think of it as models like Qwen and LLaMA running inside their chips—what I'd call small-to-medium language models, not large language models.  Up to 32 billion parameters, running on a CPU, not a GPU. So that’s a big deal. Intel loves us, and we've been able to leverage their ecosystem to have their partners sell our product. So now you've got HPE selling ExplainerAI. You've got Lenovo selling ExplainerAI. And probably my favorite partner—love you, ePlus, if you're listening—I think you're the best. They're a Fortune 1000 reseller selling ExplainerAI. So now we have large companies selling our product, and that's starting to come to fruition. Now, it's not solved—my revenue isn't going boom yet—because if it were, I'd be firmly in the PowerPoint phase, heading toward the PDF phase. But it's looking really good, and I'm very excited.  Cognome Inside.  There you go. Cognome Inside—yes. Cognome Inside. Intel Inside—for those of you who remember. Yes.  Love it. Okay, so before we wrap up, I have one more question for you: What is a question that entrepreneurs should always be asking themselves?  I think the hardest thing about being an entrepreneur is dealing with the amplitude of the variance that happens inside it. There are incredibly high days, and there are incredibly low days. There are days when you don't even want to get out of bed in the morning. You don't have many clients, and one of them has just told you that you're a complete moron. Even if you've got the best product in the world, if you're in the whiteboard or PowerPoint phase, you're going to make mistakes. You just are. No one's perfect. And there are days when some combination of a client, an employee, or the product—something has failed, someone has left, something isn't working—and you feel awful. So what I'd say to entrepreneurs is this: if you really are an entrepreneur, it is your personality that you can still get through those and wake up in the morning and say, I believe in this. I know I can do it. I can keep doing it.  And one of the things that I think separates an entrepreneur from someone who isn't is this: When I go through these moments, I ask myself, “What's the worst that could happen?” And I usually start with: “Is anyone going to die?” And the answer is almost always no. No one's going to die. So it’s not that bad. And by the way, I remember giving that advice to a young person once—and I saw their face go white. And I thought, “Oh, that's not an entrepreneur.” That's the kind of person who hears that and thinks, “Oh my God, really? You think about the worst thing that could happen so you can deal with it?” And I'm like, yes.  Does that apply to the company itself? Is the company included in that “worst-case” question?  To me, the next step is: is an individual going to die? That's a higher stake than whether the company is going to die. But yes—is the company going to die? That's part of the thinking, because you're going through all the consequences. Am I going to lose all my money? Is the company going to fail? Those are escalations of that thinking. But to me, company death is less tragic than a human death.  Yeah, true.  Not everyone might agree with that, but I think so.  You can try again.  Yeah.  Start another company.  Yeah, exactly. Anyway, your question was: what is a question that an entrepreneur should always be asking themselves? For me, turning that upside down and inside out—it's: what's the worst that can happen, and can you get through it? Are you able to get through it? Do you have the drive and the imagination to keep going? That's the question I've continually found myself asking, as opposed to any other kind of existential question. And I think some of the other questions are not always the right way to look at it—like“Is this the best business?” Because there's a very big difference between an entrepreneur and an investor.  An entrepreneur has to keep going, while an investor might quit. Investors, they’re playing the portfolio game. They can say, “That's not working—I'm dropping that and keeping this.” As an entrepreneur, you can't really play that game with your time. I mean, Elon Musk is running four companies—so okay, fine—but most of us aren't. Most of us are running one or two, and we need more tenacity to make it work—to pivot or to find another path. That's a really big difference between an entrepreneur and other kinds of people. And it's why I've kept doing it. It comes back to the very first question: why do you do this? I can't help myself. I just can't. It's what I like to do.  Yeah, the contrast is addictive—the contrast between near-death and near-Nirvana, right? Yeah. I love it. I mean, you can't have euphoria without depression. You wouldn't know what it was—it would just seem normal.  Yeah, just a personal example of that—I was in Hungary, where I was born, for the election two weeks ago.  By the way, I'm so excited about that election, for many reasons.  The exhilaration that I felt—and that everyone else felt—was even greater than when the Berlin Wall came down, because the system was worse.  Yeah.  And if they hadn't lived through that for 16 years, they wouldn't have felt it. Now, we didn't experience it directly—but still.  But even I was paying attention to a lot of things, and I was following that one very closely. Even I felt that sense of euphoria. I was like, “That's great.” I was at the dinner table with my wife and kids—and I'm not Hungarian, it's not affecting me. I mean, Viktor Orbán isn't really having any effect on my life at all. Maybe he shows up at some conferences in the U.S., but still—not affecting me. But I'm sitting there at dinner like, “Did you hear what happened today? That's great.” Anyway.  Awesome. I'm glad you're on that side of the equation. James, if people would like to learn more—if they'd like to learn about Cognome and connect with you—where should they go? Where can they find you?  Yeah, so you can certainly go to cognome.com. You can email info@cognome.com. But if you've listened to this podcast, I'm always happy to hear from people. I answer every single email myself. And if you know my name—James Green—you can just put a dot in the middle and add @cognome.com at the end, and that will get to me. Delighted to hear from any of you—especially if you're a CIO in a hospital, you should reach out.  Well, all those hospital CIOs—please call James, or at least send him an email. And for those of you listening—this was an amazing framework: from whiteboard to PowerPoint to PDF. Definitely relatable. And remember—if no one's dying, it's okay. You can always pivot and live to fight another day. So, James, thanks for coming—and thank you for listening. Important Links: James' LinkedIn James' website James' email: info@cognome.com

This episode's Community Champion Sponsor is Ossur. To learn more about their ‘Responsible for Tomorrow' Sustainability Campaign, and how you can get involved: CLICK HEREEpisode Overview: Pharmaceutical launches are among the most complex, high-stakes endeavors in all of healthcare, and the difference between winning and losing often comes down to whether the right intelligence reaches the right people at the right moment.Jason Smith, CTO of AI and Analytics at Within3, has spent his career solving exactly that problem.A three-time co-founder whose companies have raised over $100 million in venture capital, Jason built rMark Bio from scratch before its acquisition by Within3, where his AI platform now powers launch decisions for all of the top 20 pharmaceutical companies.Join us as Jason discusses how Within3's Launch Intelligence platform unifies field insights, social signals, EHR data, and stakeholder engagement into one integrated layer, empowering pharma teams to move with clarity and confidence. Let's go!Episode Highlights:Jason sold his house, packed his dog in a U-Haul, and drove from Seattle to Chicago to launch rMark Bio in 2015.Within3 analyzes over 10 billion data points, filtered into hyper-focused disease community landscapes for pharmaceutical decision-makers.Life sciences AI differs from general models because context matters: how an MSL communicates is entirely different from a general user's query.Social listening gives pharma companies real-time aggregate patient and HCP sentiment, replacing slow, one-to-one relationship-based feedback loops.Jason is an 18-year cancer survivor and American Cancer Society advisor, making him personally invested in faster, better therapeutics for patients.About our Guest:Jason Smith is CTO of AI & Analytics at Within3, where he leads the team behind the company's most advanced AI capabilities serving life sciences organizations. Jason is a three-time co-founder who built Cryptocybernetics, GrayArea, and rMark Bio from inception to successful exit. He was later brought in as CEO of xSides to lead its sale. Over his career, his companies have raised more than $100 million in venture and strategic capital. In addition to Within3, Jason is a Venture Fellow at MATTER, Advisor to Capita3, and a recognized thought leader in AI and Healthcare with publications and speaking engagements at HIMSS, Reuters, and leading healthcare and pharmaceutical conferences.Links Supporting This Episode: Within3 Website: CLICK HEREJason Smith LinkedIn page: CLICK HEREMike Biselli LinkedIn page: CLICK HEREMike Biselli Twitter page: CLICK HEREVisit our website: CLICK HERESubscribe to newsletter: CLICK HEREGuest nomination form: CLICK HERE

Once again, the U.S. government is accusing China of being among several foreign entities that are looking to steal proprietary information on artificial intelligence models from American companies. Edward Graham, managing editor and Veterans Affairs Department reporter at NextGov/FCW, discovered a memo the White House sent to federal agencies that warns of distillation campaigns seeking to help create knockoff versions of AI models. Ed joins our Ross Wilkers for this episode to explain how those campaigns work and, more importantly, how they help illuminate the competition between the U.S. and China to lead the world in AI and robotics tech development. Ed also provides an update on where things stand with VA's rollout of a new electronic health record, which has had many fits and starts over the years to say the least. White House accuses China of ‘deliberate, industrial-scale campaigns' to steal US AI models US needs to flesh out strategy to counter China's robotics advances, lawmakers say AI capabilities are needed to counter drone threats, senator says House FY27 VA funding bill allocates $3.4B for EHR rollout VA resumes EHR rollouts at four Michigan medical sites

Dr. Courtney Barrett, founder of True Insight Direct Care in the Raleigh, North Carolina area, opens our May theme, The Tools You Use, with a conversation that redefines what tools means in a Direct Primary Care practice. As a DO offering osteopathic manipulative treatment alongside full-spectrum primary care, Dr. Barrett shares how OMT functions as both a clinical tool and a front door to membership.We dig into her EHR vetting process, her non-negotiables for tech stack decisions, and why patient experience optimization shaped every choice. Dr. Barrett also shares how her husband Jeff, a former 911 dispatcher, joined the practice full-time and built operational workflows that anticipate needs before they happen. From phlebotomy setup to OMT documentation, prior authorization handling, employer contracting, HSA-funded memberships, health share pairings, and her board work with Hope and Vine supporting young women aging out of foster care, this conversation covers the full spectrum of tools that make a DPC practice work.What You Will LearnHow non-member OMT services bring patients in who later become DPC membersHow to talk to patients about DO vs MD, and OMT vs chiropractic careWhy HSA-funded DPC memberships became a major enrollment driverHer tech stack philosophy: cohesive over fragmented, patient experience firstNon-negotiables: charting without juggling windows, automated patient communication, CSV file portabilityWorking directly with employers without middlemenPairing DPC with health shares for catastrophic coverageResourcesTrue Insight Direct Care blogmydpcstory.com Learn page: free business plan, BAA, and EHR rubricDr. Feneisha Franklin's episode on acquiring a DPC practiceVote in the Battle of the Support Stack running all month at mydpcstory.com.Learn more about VIVID VAULT HEALTH SOLUTIONS TODAY! Find a My DPC Story Event near you! State Summits in CA, IL, a My DPC Story LIVE event and the DPC Women's Summit are all coming! Learn more at mydpcstory.com/upcoming-events! The DPC Directory: If you're a DPC doctor, you'll find resources to grow your practice! If you serve the DPC world, grab a FREE listing today and get discovered by doctors who need your services.

The White House wants to revamp federal contracting practices by making cost-reimbursement structures the exception, not the rule, per an executive order signed Thursday. President Donald Trump's order calls on the federal government to view fixed-price contracts with performance-based considerations as “the default and preferred method of procurement.” Department of Veterans Affairs Secretary Doug Collins told lawmakers Thursday that the VA's beleaguered electronic health record modernization efforts have turned a corner with the successful rollout of the system this month at four Michigan facilities. Collins said the April 11 deployment of the EHR at hospitals in Detroit, Ann Arbor, Battle Creek and Saginaw “has been phenomenal, even by industry standard.” The Daily Scoop Podcast is available every Monday-Friday afternoon. If you want to hear more of the latest from Washington, subscribe to The Daily Scoop Podcast  on Apple Podcasts, Soundcloud, Spotify and YouTube.

In this episode, Jodie Hilliker, Senior Director of EHR Services at Healthrise, shares how RCM leaders can protect cash flow during EHR transitions by prioritizing operational readiness, aligning cross-functional ownership, and proactively addressing workflow and documentation gaps.This episode is sponsored by Healthrise.

Artificial intelligence is everywhere in healthcare conversations, but where is it actually delivering value? In this highlight episode of Constructing With Care, we move beyond the hype to explore how AI and digital innovation are actively reshaping healthcare infrastructure, operations, and patient outcomes. Drawing from past episodes with leaders across health systems and industry partners, this conversation connects strategy to real-world application, highlighting where AI is working today and what it takes to scale it responsibly. How AI is evolving from concept to operational necessity in healthcare systems Where digital innovation is directly impacting patient outcomes Why cybersecurity and infrastructure resilience are now clinical priorities How health systems are prioritizing capital investments across AI, EHR modernization, and analytics The unique challenges, and opportunities, of designing AI for pediatric care Real-world examples of AI in action, from virtual care to autonomous security systems

We need a “human in the loop” in mid-revenue cycle work, experts say. What they fail to answer is the more interesting question:  Where in the loop, exactly?Back-end AI fact-checker? Front-end query authorizer? Or, maybe something like my current OTR guest Penny Jefferson envisions: Medical record maestro. The end-to-end connectedness of the medical record, evolving API standards, increased use of prior authorization, episode-based reimbursement models, and review of all of this by AI and other tools makes documentation cohesion more important than ever.  CDI is changing with the times, but often not fast enough. It must change, or risk stagnation and possible extinction. The ready availability of vast amounts of information in the EHR and AI-enabled reviews and audits means a slip in a diagnosis can result in a big DRG downgrade or denial of stay altogether. But an AI powered enabled CDI maestro conducting the show can make a record strong, front to back. Penny has been writing like a fiend about many of these and other related topics for LinkedIn and the likes of ICD-10 Monitor--which is what led her back to the hot seat of the Off the Record studio. Listen in as we discuss:  A day in the life of: Penny's evolving role at UCDavis Her take on today's CDI work, where is it coming up short and/or at risk of being automated away? UR function/medical necessity and how CDI can assist case management without igniting a turf war. “Longitudinal episode integrity”: Episode-based bundled payments (TEAM, etc.) and how they shift chart review beyond discrete diagnosis review to breadth APIs and FHIR standards: Issues with fields that don't match throughout the medical record leading to denials, bringing it home with an encephalopathy example Where does CDI need to upskill to meet these new demands? Does the “traditional” chart review/query role need to change ... or do we need an entirely new role to evolve to meet these demands? And what about productivity metrics? Addressing the elephant in the room. Where is the human in the loop? For example: should a human review every AI query before it goes out? Spend more time on high dollar, 5+ day stays? Tiebreak a co-equal dx? Updates on admit type. Listeners might recall our previous show on this topic, with  loose NUBC definitions leading to urgent vs. elective categorization to minimize impact on quality metrics. Other fun stuff you only get on #OTR 

The gap between billion-dollar EHRs and the reality of life at the patient bedside has never been wider. While healthcare systems race toward AI and automation, nurses are often left fighting the very systems meant to support them. Join host Beth Friedman and DJ Tucker, Managing Director of Healthcare Informatics at Healthcare IT Leaders, for a conversation about technology and human-centered nursing. With over 40 years of combined clinical and informatics experience, Beth and DJ dig into the hard truths of EHR adoption and why go-live is just the starting point for true transformation. The bottom line? Technology doesn't just owe our nurses great efficiency; it owes them the capacity to be fully human with their patients. Tune in to discover how to move beyond “shiny penny” tools and build a digital environment that protects the heart of healthcare, our nurses. Find all of our network podcasts on your favorite podcast platforms and be sure to subscribe and like us. Learn more at www.healthcarenowradio.com/listen/

Private practice owners who rely on what they think is happening instead of real data to guide their marketing are often spending time on activities that are not helping to grow their private practice. If you are not bringing in as many clients as you feel you should be - then ask yourself, are you actually listening to real data or what you think is real? The data you need exists, but it is scattered across your booking system, your EHR, and your memory - not in one place where you can see what it is telling you. That gap means you have no clear picture of where your clients are coming from or why some consult calls are not converting into booked clients.This episode walks you through exactly how to track your consult call data using a free template - so your marketing decisions are based on real numbers, not guesses.You'll learn:How easy it is to track the data from your consult calls in a way that it guides your marketing and business decisionsWhat 12 numbers and data points are the key ones to track using a free templateHow to make consult call tracking a habit that actually sticks and grows your practiceStop making marketing decisions based on what you think instead of real numbers, and learn how easy it is to track those numbers so you can use them to grow your private practice. Be sure to check out the resources mentioned, including the free tracking template to download, which are all on our show notes page at http://MarketingYourPrivatePractice.com/180Grab the free template here - https://academy.pepperitmarketing.com/trackerClick here to send Kathy a text message about this Episode

The Brand Is Your Doctor Now: Provider-Agnostic Care and the Risk to Relationship-Based PediatricsOn The Pediatric Lounge, hosts discuss “provider-agnostic” or “physician-agnostic” care with pediatrician Dr. James Reilly, arguing corporate and private-equity models use protocols, EHR-driven algorithms, and lower-cost staffing to make clinicians interchangeable and reduce patient loyalty to individual physicians. They contrast efficient team-based support that preserves continuity with cost-cutting that sacrifices time, empathy, and physician satisfaction, and warn that “top-of-license” restructuring in psychiatry led to underfunding and a lasting mental health crisis. The conversation links critical pathways and Epic-style cognitive offloading to diminished clinical judgment, citing examples of inappropriate protocol orders, urgent-care misses, and MinuteClinic prescribing. They emphasize that longitudinal “thinking sciences” benefit from trust and wisdom that computers can't replace, and predict worse outcomes, burnout, and access problems if relationships are replaced by brand-driven, algorithmic care.00:00 Welcome to Pediatric Lounge00:45 Meet Dr. James Reilly01:53 Why Relationships Matter03:12 What Is Provider Agnostic Care04:40 Efficiency vs Assembly Line Care07:28 Private Equity and Interchangeability10:35 Top of License Mental Health Lesson14:58 How Protocols and EHRs Started It20:33 Algorithms vs Human Wisdom26:38 Pediatrics Funding and Algorithm Upsides31:00 Pediatrics Value Gap31:36 Telemedicine Eye Miss32:53 MinuteClinic Strep Mixup35:27 Brand Versus Doctor37:07 Thinking Sciences Model43:09 Continuity Catches Problems44:24 Lipoprotein A Wisdom48:50 Medicine Art And Science50:27 Interchangeable Doctors Burnout53:46 Humans Not Algorithms55:08 Wrap Up And CreditsSupport the show

Leigh Thomas Williams is Vice President and CIO at Augusta Health, a 255-bed community health system in Virginia's Shenandoah Valley.   She's also President of the HIMSS Virginia Chapter and an Ambassador for the Dr. Lorna Breen Heroes' Foundation. Leigh started her career in law, got recruited into healthcare at the University of Mississippi Medical Center, and found her calling in technology leadership during an EHR implementation when she realized IT was treating it like a software install and she was treating it like a chance to transform the way people work. Now she's leading Care Reimagined, a multi-year digital transformation built on three promises: meaningfully improve the professional workday, create compassionate patient journeys, and steward resources wisely for the community. When Augusta Health's Dr. Snodgrass told Leigh that their AI physician assistant was the first time technology had been done for her as opposed to to her, it became a watershed moment for the entire organization.   In this episode, Leigh talks about walkup songs for every day (not just keynotes), why she finally started baking brownies for her team, learning German at age 8 with no English-speaking teacher, and why now is not the time to take the foot off the pedal.   https://marxadvisory.com