Out of the FHIR Podcast

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]

The AI Paradox: Why LLMs in healthcare actually require more structured data, not less | Ewout Kramer & Ward Weistra (Firely)

Ewout Kramer is the “head nerd” and founder of Firely [http://firely.com], and one of the original architects of the FHIR (Fast Healthcare Interoperability Resources) standard. Ward Weistra leads data modeling tools at Firely and curates the content for FHIR DevDays. Together, they have spent over a decade transitioning healthcare from messy legacy standards to a modern, developer-friendly ecosystem. Listen on YouTube [https://www.youtube.com/@OutoftheFHIRPodcast], Spotify [https://open.spotify.com/show/6GBZT7KA1Ug8xMZ4l5LThU?si=dc3e30c5d0554319], and Apple Podcasts [https://podcasts.apple.com/us/podcast/out-of-the-fhir-podcast/id1822845248] We discuss: * The Origins of DevDays: How a kitchen-table meetup 15 years ago turned into the canonical global event for health tech developers. * AI as a FHIR Catalyst: Why AI doesn’t replace the need for structured data—it actually makes the “Step Zero” of standardization more critical. * The Human Side of Interoperability: Why building trust between competitors is more important than the JSON schemas themselves. * The EHDS and Global Regulation: How the European Health Data Space and U.S. Cures Act are forcing a “bottom-up” shift in software engineering. * Community & Culture: From the “Nerd Awards” to student tracks and even forming a “FHIR band.” My biggest takeaways from this conversation: 1. Standardization is only “Step Zero” A common mistake in health tech is assuming that once data is standardized into FHIR, the job is done. Ewout argues that standardizing data is merely the baseline. The real work and the focus of this year’s DevDays is extracting meaning. This involves moving from static data to national-scale workflows, clinical decision support (CQL), and figuring out how data travels with a patient across institutions without losing context. 2. The AI Paradox: More AI requires more structure, not less There is a contrarian view that LLMs are now so good at reading unstructured text that we no longer need to invest in the “hard manual work” of FHIR mapping. Ward and Ewout share the results of their global “State of FHIR” survey, which suggests the opposite. Government leaders and engineers agree that AI actually increases the demand for FHIR. To prevent hallucinations and ensure clinical safety, AI agents need the “guardrails” of a structured schema to reason over data reliably. 3. Interoperability is an “Inter-human” problem Technology rarely solves the hardest problems in healthcare; communication does. Many data mapping issues stem from “decades of legacy data” where the original developers are gone, and no one knows how a specific field is used in a specific hospital. Solving this requires what Ward calls the “trust layer” getting competitors in the same room to agree on implementation guides so that the software actually talks to each other in the real world. 4. Regulation provides the “Bottom-Up” power The EHDS (European Health Data Space) is set to mandate that by 2030, every piece of health software in the EU must implement the same interfaces. While this is a top-down mandate, it empowers the “single developer” within a large organization to convince their management to do the right thing. It shifts FHIR from a “nice-to-have” innovation project to a legal requirement for market entry. 5. The “Tiny Core” of the FHIR Community Similar to how great products have a “tiny core” (like the Notion block or the GitHub PR), the FHIR community relies on a core group of “head nerds” who have grown from junior devs to national thought leaders over the last 15 years. Events like DevDays maintain this culture through informal “nerd-outs” like automating pet turtle enclosures or building “Back to the Future” garage doors ensuring the community remains focused on building, not just policy-making. Where to find Ewout and Ward: * Ewout Kramer: LinkedIn [https://www.google.com/search?q=https://www.linkedin.com/in/ewoutkramer] * Ward Weistra: LinkedIn [https://www.google.com/search?q=https://www.linkedin.com/in/wardweistra] * Firely: https://fire.ly [http://fire.ly] Referenced: * FHIR DevDays (June 15-18, Minneapolis): https://devdays.com [http://devdays.com] https://www.hl7.org/ [http://hl7.org] * The EHDS (European Health Data Space): Official Overview [https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space_en] * Vivian Lee’s “The Long Fix”: Book Link [https://www.google.com/search?q=https://www.amazon.com/Long-Fix-Solving-Healthcare-Affordable/dp/0393247458] * Kill the Clipboard Initiative: https://killtheclipboard.com * Simplifier.net: FHIR Registry [https://simplifier.net] Important Disclamer: DevDays is organized by both HL7 International and Firely 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]

Why TRUST is the most important element in enabling Healthcare Interoperability

Ever wondered why security is key for FHIR interoperability? Here’s what you need to know! Tag someone who needs to understand this! In the healthcare world, we face an ongoing challenge: scaling security effectively. Fast security initiatives aim to solve this age-old problem by automating processes that once required human intervention. With dynamic client registration, organizations can connect without lengthy manual steps, ensuring quicker and more secure data exchanges. But it’s not just about speed trust is essential. The FAST Security IG establishes a foundation for technical trust, allowing seamless interactions across the FHIR ecosystem. Imagine a world where you can access healthcare data without the friction of individual Portal Logins, or organization data sharing without CSV files or endless emails! SMART on FHIR solved the auth flow in 2015. It did not solve trust at network scale. Plaid proved network trust in finance. Visa proved it at the point of sale. Sabre and Amadeus proved it in travel. All three work because there is a cryptographically signed identity you present once, and the whole network honors it. UDAP is that identity layer for healthcare. SMART handles the authorization. FAST Security IG is the rulebook that composes them into something that can actually carry TEFCA, CMS-Aligned Networks, Patient Right of Access, and the full B2B / B2C / B2B2C surface without requiring a bilateral deal for every new endpoint. Let’s keep this conversation going what are your thoughts on the future of healthcare data exchange? Listen to the full episode for deeper insights! 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]