The Illusion of Interoperability: Why Healthcare is Broken (and How FHIR APIs can Fix It)

Is TEFCA a Bridge to Nowhere? Why the National Health Network Could Collapse Under Success

In almost every traditional tech vertical, standardizing data integration is a engineering problem. In healthcare, it’s a coordination and business problem wrapped in regulatory tape. To unpack how this landscape is shifting under the new HTI-5 / HTI-6 regulations, I sat down with Ryan Howells, Principal at Leavitt Partners and a foundational leader behind the CARIN Alliance [https://www.carinalliance.com/]. We went deep on why healthcare product growth is broken, the structural shifts happening via the CMS Health Tech Ecosystem, and how the “Kill the Clipboard” framework is reshaping health tech. 1. The Core Bottleneck: Why Healthcare Innovation Suffers from a “Cert Program” Tax Historically, building an Electronic Medical Record (EMR) or digital health application meant pleasing a very specific buyer: the federal government, not the end user. [Old Regulatory Dynamic] Government Mandates -> EMR Product Roadmap -> Client Stifled Innovation -> Value-Based Care Blocked Under legacy ONC Certification guidelines, EMR platforms had to build rigid, monolithic internal workflows dictated directly by shifting rules. Ryan highlighted the structural downstream issues this causes for B2B health tech products: * Roadmap Strangulation: EMR vendors are constantly forced to balance three conflicting roadmaps: compliance updates from the federal government, core client requests, and actual standalone innovation. Compliance almost always wins, effectively paralyzing rapid iterations. * The Value-Based Care (VBC) Penalty: If a modern platform handles a multi-layered VBC structure (e.g., social determinants, specialized wearable data, and alternative reimbursement structures), they are trapped. Under legacy rules, they frequently have to buy and operate a traditional fee-for-service EMR alongside their custom platform just to settle billing destroying product margins. 2. The Structural Shift: Certify the Interface, Not the EHR Product Takeaway: By shifting the regulatory boundary to modern internet-standard interfaces, health systems can uncouple core data layers from monolithic vendors. This allows product builders to treat core EMR systems like a cloud data warehouse, spinning up specialized SaaS layers on top for revenue cycle, clinical intelligence, and analytics. The “Bridge to Nowhere” Risk A frequent error among product leaders is assuming that the network architecture behind national systems like TEFCA is built to handle heavy, continuous, high-volume automated data requests. As Ryan pointed out, if every provider and payer in the country simultaneously hit these pipes using dynamic record location services (RLS) under a traditional framework, large portions of the network infrastructure would collapse. The architecture was historically built around on-premise EMR infrastructure with highly constrained compute limits. The industry roadmap for the next decade is not simply about building more pipelines; it is about scaling cloud-native data lakes so that bulk datasets can be securely processed without taking down transactional medical systems. 3. The Implementation Blueprint: FHIR and CQL vs. SQL When it comes to processing massive data pipelines like calculating digital quality metrics or evaluating massive population health cohorts you will inevitably face an engineering fork in the road. ┌──► FHIR + CQL (NCQA + Vendor-Led) Data Pipeline ────┤ └──► FHIR + CQL + SQL (Nascent, Developer-Preferred Adoption) The Current Standard: FHIR + CQL * The State of the Art: Driven by communities like the Clinical Quality HL7 Community, Clinical Quality Language (CQL) combined with FHIR has matured over a multi-year effort. * The Friction: CQL is highly specialized. Finding engineers who can run, tune, and configure pure CQL logic at scale is incredibly difficult and highly expensive. The Up-and-Coming Challenger: FHIR + CQL + SQL * The Core Concept: Translating FHIR structures directly into relational or analytical SQL queries. * The Advantage: Every health plan, startup, and system in the world already has talented SQL developers. Moving to SQL drops the specialized vendor tax and makes quality measurement logic completely shareable as open-source code. * The Verdict: While architectural translation tools (like converting 100% of CQL measures to native SQL) are showing immense promise at conferences, the execution layer is still early. Product teams should plan a roadmap that adopts current FHIR/CQL standard pipelines today while designing their database schemas to consume raw relational analytics tomorrow. 4. The Next Product Frontiers: Identity and Digital Leaps If you are mapping out product opportunities in health tech over the next 2–3 years, pay closest attention to these structural changes rolling out via the CMS Health Tech Workspace: * Federated Digital Identity Over Patient Matching: Legacy health tech relies on complex, fragile statistical patient matching models to stitch medical histories together. By rolling out modern digital identity tech (e.g., identity proofing through login.gov or similar consumer engines), patient matching over time effectively disappears. Once a user validates their identity, they hold a single sign-on credential that unlocks both business and consumer endpoints natively. * National Provider Directories Connected to Active Endpoints: Finding a provider’s digital address book has been an operational nightmare, forcing every startup to manually maintain their own internal directory scrapers. CMS’s transition to identity-proofing individual clinicians and mapping them directly to verifiable FHIR API endpoints turns the directory problem into a utility infrastructure. * The “Africa Leap” Strategy for Regional Products: For product builders looking at regional healthcare or rural networks, do not attempt to replicate the technology path of legacy, suburban medical networks. Just as developing nations skipped desktop systems entirely and moved straight to mobile, rural healthcare initiatives can entirely skip the legacy on-premise, file-drop architecture. The smart move is to build purely on open standards (like the PIQI framework for data normalization), deploying cloud-first architectures natively packaged with AI-driven models right at the point of care. 🎧 Try the Interactive Audio Companion on NotebookLM To experience this conversation in a completely new format, check out the NotebookLM Interactive Audio Companion for this episode [https://notebooklm.google.com/notebook/c0507dbc-8610-411f-a4e3-b6560d197f00]. This AI-generated explainer workspace acts as a dynamic companion to the podcast, instantly generating deep-dive overviews, structured study guides, and interactive timelines of the massive policy shifts discussed by Ryan and Gene. If you are a visual learner who wants to instantly query the transcript for specific implementation guides, map out the timeline from the High Tech Act to HTI-6, or generate custom summaries of the “Kill the Clipboard” initiative, this notebook lets you interact directly with the episode data to fast-track your health tech product strategy. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit evestel.substack.com/subscribe [https://evestel.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2]

The Illusion of Interoperability: Why Healthcare is Broken (and How FHIR APIs can Fix It)

Today’s episode of Out of the FHIR brings a massive milestone: I’m thrilled to officially welcome my new co-host, Benji Graham, to the show. Benji is one of the few true, certified FHIR experts in the country, and he’s joining me to ensure we dive as deep into the technical weeds as humanly possible. To celebrate, we brought on a true legend in the health tech space: Dr. Don Rucker. Don has seen it all. He’s been an ER doc for decades, he helped build the first Windows-based EMR in the late 1980s, he served as the National Coordinator for Health IT (ONC) under the Cures Act, and he is currently the Chief Strategy Officer at 1Up Health. In this episode, Don pulls back the curtain on why healthcare IT is fundamentally different from the rest of the tech world, why the “patient identity problem” is mostly an illusion, and why RESTful JSON APIs are the ultimate forcing function that will inevitably change how care is delivered. Top Takeaways from Our Conversation * Healthcare settles for bad tech because consumers don’t control the money: In a functioning consumer-driven economy, interoperability is demanded by the buyer. In healthcare, the 1942 Stabilization Act shifted payment to employers, breaking the connection between user satisfaction and technology quality. * True interoperability requires a market forcing function, not just regulation: Radiology achieved seamless data exchange (DICOM) in the 90s because radiologists acted as true market-bearing consumers and refused to buy proprietary hardware. The rest of healthcare lacks this buyer pressure, leaving the government to act as a highly inefficient proxy. * The “Patient Identity Problem” is a myth for those with a right to data: The three core stakeholders under HIPAA Patients, Payers, and Providers already have perfect identity verification through the financial clearance loop. The identity matching crisis primarily exists for third-party scrapers and legacy networks trying to aggregate data without explicit consumer consent. * TEFCA is building a “network of networks” for a problem the internet already solved: True security and data exchange are achieved through point-to-point zero-trust networks and OAuth 2.0 consumer verification, not brokered document clearinghouses. 1. The Historical Accident of Healthcare Tech To understand why your cell phone works seamlessly in the mountains of Utah but your medical record can’t cross the street to a competing hospital, you have to look back to 1942. “We settle for stuff in healthcare because we have no control over it. In a functioning consumer-driven economy, interoperability is demanded and provided.” — Don Rucker During WWII, the Stabilization Act of 1942 inadvertently made employer-sponsored health insurance pre-tax to attract wartime labor. Because consumers stopped holding the purse strings, the industry stopped optimizing for consumer satisfaction. When computing arrived in medicine during the 70s and 80s, it didn’t start in the clinic—it started in the back office. The first EHR systems were optimized entirely for line-item billing, setting off a multi-decade game of “CPT code warfare” between payers and providers. Clinical utility was a distant afterthought. 2. DICOM vs. TEFCA: How Markets Drive Standards Why did radiology successfully digitize and standardize via DICOM in the 1990s while the rest of clinical data remains fractured? It came down to consumer leverage: * The Radiology Model: When CT and MRI scans started generating massive amounts of digital data, legacy device manufacturers (GE, Siemens, Philips) tried to lock radiologists into proprietary data silos. The radiologists—acting as a true market market-bearing buyers—collectively said, “If you don’t build open standards, we aren’t buying.” The vendors folded immediately. * The Modern EHR Model: Because patients and doctors aren’t the primary buyers of modern health systems, there is no organic market pressure to share data. In fact, large health networks have an economic incentive to prevent interoperability to keep patients locked inside their system. Because the market won’t enforce openness, the government had to step in with the 2020 Cures Act Interoperability Rule, mandating “APIs without special effort.” 3. The Perfect Triangle: Debunking the Patient Identity Myth Whenever tech professionals enter healthcare, they ask: Why don’t we have a National Patient Identifier? How do we solve patient matching? According to Don, the problem is entirely misunderstood. If you look at the fundamental transaction of healthcare, there is a perfect triangle of identity that requires zero matching algorithms: [ Payer ] / \ / \ / \ [ Provider ] ---- [ Patient ] * Provider to Patient: A clinic will not put a patient on the calendar without running a 270/271 eligibility check. They know exactly who you are before you sit in the waiting room. * Payer to Provider: Payers know exactly which clinicians they credential and clear checks for. * Payer to Patient: Payers know exactly who they insure to protect their own bottom line. The people who have a legal and moral right to the data under HIPAA have zero identity matching problems. The matching crisis belongs to third parties trying to route data across networks without the direct digital consent of the patient. 4. The Future is Restful JSON and AI Agents While legacy systems lean on brokered networks and document architectures (like C-CDAs), the future belongs to RESTful JSON APIs running on FHIR standards. The proliferation of consumer-facing AI agents is going to serve as the next massive forcing function. When patients realize an AI agent can hit a local provider’s API, pull down their complete medical history in seconds, and give them personalized preventive insights, they won’t accept legacy portals anymore. “Water flows downhill. RESTful APIs that are secure and direct operate at one-thousandth the cost of brokered networks. Digital health is going to allow true prevention, and consumer demand is going to force it out of the system.” — Don Rucker Out of the FHIR is a reader-supported publication. To receive new posts and support our work, consider becoming a free or paid subscriber. FHIR IQ playbook is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. 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Nurses need AI too, and how it needs to be deployed at scale to ease administrative burden.

Gene Vestel sits down with Michelle Skinner, Chief Clinical Executive at TeleTracking, to unpack the operational side of healthcare execution. Michelle is a nurse by background with an MBA who spent decades running emergency departments and trauma centers before moving into health-tech leadership. In this episode, she breaks down how TeleTracking a rare, 35-year-old owner-operated pillar in a sea of PE-backed digital health firms is using computational twin technology to radically optimize hospital operations without breaking clinical workflows. Listen now on YouTube, Spotify, and Apple Podcasts. We discuss: * The Reality of Hospital Patient Flow: Why emergency department boarding is a symptom of systemic operational gridlock, not an ER failure. * Computational Twins in Action: How simulating real-time capacity scenario planning can drop a hospital’s length of stay by over a full day. * The Nursing Cognitive Load Crisis: Why AI strategies must pivot from administrative data logging to keeping nurses at the bedside. * The Business vs. Care Matrix: How having clinical leadership embedded directly within engineering teams alters how code is written. * The Imperative of Rural Healthcare Access: Why urban-centric health models collapse when applied to regional communities. My 3 Biggest Takeaways from This Conversation 1. Hospital crowding is a patient flow problem, not a capacity problem When patients are held in emergency department hallways for days, the default reaction is often to blame ER throughput or demand more physical beds. The tactical reality is that ER boarding is a lagging symptom of poor downstream operational orchestration. When a hospital cannot cleanly coordinate transitions from the post-anesthesia care unit (PACU) to intensive care or general medical floors, the entire pipeline backs up. TeleTracking’s deployment of computational twin software builds a predictive digital replica of a facility’s entire capacity landscape, running scenario trade-offs 48 hours in advance. The result isn’t just arbitrary data tracking; it’s a systematic blueprint that has driven over a 50% reduction in ED holds while simultaneously allowing hospitals to scale up overall volume. 2. If technology doesn’t actively reduce a nurse’s cognitive load, it’s a failure While ambient listening models have made incredible strides in reducing “pajama time” and burnout metrics for physicians, the wider health-tech ecosystem has largely ignored the operational burden placed on nursing staff. Nurses have been turned into administrative traffic controllers spending critical clinical hours manually tracking down bed availability, coordinating discharge paperwork, or calling radiology to check on exam slots. We must evaluate new technology platforms through a singular, hyper-focused product lens: Does this give clinical hours back to the patient, or does it add friction to the system?. If it doesn’t systematically strip administrative steps out of the clinical loop, it shouldn’t be built. 3. Engineering teams need immediate clinical guardrails A distinct trap for tech-first companies entering healthcare is treating healthcare metrics as abstract, unfeeling datasets or lines of code. True product maturity occurs when engineering squads have an operational bridge to the clinical frontline. Having nurses embedded directly into development processes creates a permanent shift in engineering empathy. When developers understand that a minor database lag or a clunky workflow pattern directly delays a bed placement for a critical trauma patient, the quality of execution spikes. We must build software with a human-in-the-loop mentality, ensuring code serves the explicit, real-world workflow realities of active caregivers. Where to find Michelle Skinner & TeleTracking: * LinkedIn: Michelle Skinner [https://www.linkedin.com/in/michelleskinner712/] * Website: TeleTracking [https://www.teletracking.com/] If you found this operational breakdown valuable, consider subscribing to Out of the FHIR for weekly technical product leadership deep dives. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit evestel.substack.com/subscribe [https://evestel.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2]

Navigating the Shift to Bulk Data and AI

Ron Urwongse, co-founder of Defacto Health, and I sit down to break down the rapid shifts hitting CMS regulations, the transition from standard FHIR APIs to national bulk data datasets, and how agentic AI workflows are compressing engineering timelines from months to an afternoon. Listen now on YouTube, Spotify, and Apple Podcasts. We discuss: * The Bulk Data Pivot: Why CMS is expanding beyond endpoint APIs into massive bulk NDJSON files for Medicare Advantage plans. * The National Provider Directory Ecosystem: A technical audit of the new data release, where it shines, and where the logical models are still failing. * AI as an Engineering Accelerator: How teams are using agentic workflows (like Claude Code) to build production-ready validation engines overnight. * Smart Scheduling Links: The inevitable roadmap toward universal, consumer-centric open appointment booking. * The CMS Feedback Loop: Why the newly established CMS Health Tech Ecosystem Slack channel is radically altering how regulations are refined in real time. My 3 Biggest Takeaways 1. Compliance cycles have compressed from six months to a single weekend In the legacy enterprise playbook, updating a platform to conform with newly dropped technical implementation guides took a quarter or more of roadmap planning. Today, that layout is dead. Ron noted that when CMS dropped updated technical guidance on a Friday afternoon, multiple forward-thinking payers had already fully conformed by Monday morning. The differentiator isn’t engineering headcount; it’s the shift toward AI accelerators. If your senior architects aren’t actively feeding CMS Implementation Guides into tools like Claude Code to interpret, write, and deploy schemas, you are building an operational bottleneck. 2. We are transitioning from simple Master Data Management to Federated Graphs The industry has long clamored for CMS to run a centralized database as a mastered system of record. Instead, the tactical reality looks much more like a federated graph across hundreds of independent nodes. CMS isn’t attempting top-down data cleansing; they are supplying the network scaffolding to link provider organizations, practitioners, endpoints, and digital footprints. Payers must now prioritize internal accuracy auditing because upcoming mandates like the Real Health Providers Act will require plans to publicly score and publish the validity of their directory data. 3. Open scheduling is the ultimate bottleneck for value-based care Up to 75% of open care gaps remain unfilled simply because of the high friction involved in patient engagement such as transcribing an identical medical history onto a 40-page clipboard during an intake cycle. Universalizing lightweight specifications like Smart Scheduling Links originally built to aggregate vaccine availability during COVID will allow insurance directories to natively embed real-time booking slots. The monetization model still needs guardrails to protect providers from high platform fees and patient acquisition gaming, but opening up EHR scheduling data to the wider ecosystem is an absolute necessity to drive actual consumerism in healthcare. Deep Dive: Auditing the National Provider Directory The launch of the National Provider Directory marked a major milestone for healthcare data liquidity, but looking under the hood reveals clear technical hurdles that the developer community is currently solving. [ Practitioner ] │ Is associated with ▼ [ Provider Organization ] ── Publishes ──► [ Bulk NDJSON Dataset ] │ │ Resolves endpoint to │ Contains ▼ ▼ [ Patient-Centric Endpoint ] ◄── Audited by ── [ AINPI.dev Engine ] The Architectural Gaps in the NPD Release To test the real-world utility of the new data, Gene imported the entire publicly available directory into an open-source tool built over a weekend to evaluate and audit conformance: AINPI.dev [https://ainpi.dev/]. The audit highlighted several distinct areas where the logical models require iteration: * Endpoint Association Confusion: There remains an ongoing architectural debate within CMS working groups regarding where endpoints should sit logically. Attaching a FHIR connection endpoint directly to an individual practitioner creates massive, unmanageable data duplication. The correct semantic approach maps endpoints strictly to the Provider Organization, which then establishes relationships down to the underlying practitioners. * The Specialty Taxonomy Mess: There is still no clean, unified consensus on processing specialty codes. Payers are left navigating multiple conflicting sources of truth published across PECOS, NPPES, and specialized CMS charts, leading to distinct fragmentation in search results. * Missing Endpoints: The front door to patient-directed data access relies on clean endpoint visibility. Currently, a vast percentage of active provider organizations feature zero mapped digital endpoints, making true interoperability a fragmented experience depending entirely on where a patient lives. Where to find Ron Urwongse & Defacto Health: * LinkedIn: Ron Urwongse [https://www.linkedin.com/in/rurwongse/] * Website: De facto Health [https://defacto.health/] Referenced in the show: * The Open-Source Audit Tool: AINPI.dev [https://ainpi.dev/] * The Agentic Healthcare Assistant Concept: HealthClaw.io [https://healthclaw.io/] * The Technical Repository Framework: Smart Health Connect [https://github.com/aks129] * Gene’s AI Builder Cohort: FHIRIQ Workshop [https://fhiriq.com/workshop] If you found this breakdown valuable, consider subscribing to Out of the FHIR for weekly deep dives into technical health-tech leadership. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit evestel.substack.com/subscribe [https://evestel.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2]

State of Prior Authorization with Mark Fleming (Availity)

Mark Fleming is Senior Director of Prior Authorization, Interoperability, and Portal Solutions at Availity, a leading healthcare clearinghouse and data network. With over 25 years of experience in healthcare IT and revenue cycle management starting back when Epic had only 500 employees Mark is one of the industry’s foremost experts on modernizing the administrative friction between payers and providers. Listen on YouTube [https://www.youtube.com/@OutoftheFHIRPodcast], Spotify [https://open.spotify.com/show/6GBZT7KA1Ug8xMZ4l5LThU?si=8d077e7044d84644], and Apple Podcasts [https://podcasts.apple.com/us/podcast/out-of-the-fhir-podcast/id1822845248]. We discuss: * Why a staggering two-thirds of prior authorizations are still stuck on manual faxes, phone calls, and isolated web portals. * The massive structural shift behind the CMS-0057 mandate and how standardized FHIR APIs will force standard authorization timelines from weeks down to a strict 72-hour window. * Moving from isolated transactions to real-time clinical transparency—letting providers query exact documentation and medical policy rules directly inside their EHR at the point of care. * How digitizing clinical data allows modern AI platforms to parse requirements instantly, letting patients schedule sensitive procedures within days rather than waiting for weeks. * The daunting scaling bottleneck of point-to-point connections, why the average health system routinely deals with 40 to 80 distinct payers each month, and why the industry must look toward centralized networks over customized developer builds. My biggest takeaways from this conversation: * The Stagnant State of Healthcare Administrative Friction: Despite immense technological progress in other areas of our daily lives, healthcare transactions remain stubbornly legacy. Currently, only about a third of prior authorization transactions utilize automated electronic X12 standards; the remaining two-thirds are split evenly between manual payer portals and decades-old faxes and phone calls. * The Clinical Shift of CMS-0057: The incoming federal FHIR API standards mandate a massive operational pivot. Historically, providers gathered documentation and “threw it over the fence,” resulting in back-and-forth rejections because of highly specific medical policies. By introducing Coverage Requirements Discovery (CRD) and Documentation Templates and Rules (DTR) directly into the point-of-care workflow, providers will instantly know exactly what clinical information is required before a submission occurs. * Real-Time Automated Care Approvals: Integrating real-time bi-directional FHIR streams with clinical decision platforms paves the way for immediate automated processing. By utilizing modern AI architectures to evaluate digital clinical datasets against explicit payer criteria, current production implementations (like Availity’s authAI tool) are already approving up to 78% of initial submissions within 60 seconds. This eliminates the safety buffer where providers schedule slots weeks out just to wait for a manual determination. * The Network Scalability Challenge: Point-to-point custom integrations simply do not scale for provider ecosystems. Because an average mid-sized health system must route documentation to 40 distinct payers every single month and larger ones route to up to 80 building out separate point-to-point lines of communication is logistically unfeasible. Centralized networks must step in to act as translation and trust clearinghouses to standardize operations between varying EHR versions and complex payer architectures. * The Cost-Burden Equivalence: Transitioning away from legacy administrative manual procedures can remove immense financial waste from the healthcare system. Current metrics show that a manual submission for a prior authorization costs an average of $9.00 per submission, whereas an fully electronic transaction utilizing standardized networks drops that cost to just $0.25. Where to find Mark Fleming: * LinkedIn: Mark Fleming on LinkedIn [https://www.linkedin.com/in/markjfleming/] * Website: Availity Official Portal [https://www.availity.com/] Referenced in the show: * CMS-0057 (Interoperability and Prior Authorization Final Rule): CMS Official Summary [https://www.cms.gov/priorities/burden-reduction/overview/interoperability/policies-regulations/cms-interoperability-prior-authorization-final-rule-cms-0057-f] * CMS-0062 (Proposed Rule Expanding FHIR to Medications): Federal Register Rule Details [https://www.cms.gov/priorities/burden-reduction/overview/interoperability/policies-regulations/cms-interoperability-standards-prior-authorization-drugs-proposed-rule-cms-0062-p] * HL7 Da Vinci Project & Burden Reduction Group: Da Vinci Framework Overview [https://build.fhir.org/ig/HL7/davinci-crd/en/burden.html] * Trebuchet Project: Trebuchet Connectivity Infrastructure Initiative [https://confluence.hl7.org/spaces/DVP/pages/234423487/Da+Vinci+Trebuchet+FHIR+Pilots] * HealthClaw: Open Source Fire Data Quality Assessment Layer [https://github.com/aks129/HealthClawGuardrails] * Epic Systems: Epic Corporate Page [https://www.epic.com/] * Athenahealth & Humana Joint Case Study: Reference Implementation Learnings [https://www.athenahealth.com/] * Medical Group Management Association (MGMA) Survey: Prior Authorization Burden Metric Report [https://www.mgma.com/] * TEFCA (Trusted Exchange Framework and Common Agreement): HealthIT.gov TEFCA Details [https://www.healthit.gov/] * FAST (FHIR At Scale Taskforce) Security Initiative: ONC FAST Security and Identity Working Group [https://www.healthit.gov/] This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit evestel.substack.com/subscribe [https://evestel.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2]