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

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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

* Website: TeleTracking

If you found this operational breakdown valuable, consider subscribing to Out of the FHIR for weekly technical product leadership deep dives.

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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. 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

* Website: De facto Health

Referenced in the show:

* The Open-Source Audit Tool: AINPI.dev

* The Agentic Healthcare Assistant Concept: HealthClaw.io

* The Technical Repository Framework: Smart Health Connect

* Gene’s AI Builder Cohort: FHIRIQ Workshop

If you found this breakdown valuable, consider subscribing to Out of the FHIR for weekly deep dives into technical health-tech leadership.

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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

* Website: Availity Official Portal

Referenced in the show:

* CMS-0057 (Interoperability and Prior Authorization Final Rule): CMS Official Summary

* CMS-0062 (Proposed Rule Expanding FHIR to Medications): Federal Register Rule Details

* HL7 Da Vinci Project & Burden Reduction Group: Da Vinci Framework Overview

* Trebuchet Project: Trebuchet Connectivity Infrastructure Initiative

* HealthClaw: Open Source Fire Data Quality Assessment Layer

* Epic Systems: Epic Corporate Page

* Athenahealth & Humana Joint Case Study: Reference Implementation Learnings

* Medical Group Management Association (MGMA) Survey: Prior Authorization Burden Metric Report

* TEFCA (Trusted Exchange Framework and Common Agreement): HealthIT.gov TEFCA Details

* FAST (FHIR At Scale Taskforce) Security Initiative: ONC FAST Security and Identity Working Group

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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

* Ward Weistra: LinkedIn

* Firely: https://fire.ly

Referenced:

* FHIR DevDays (June 15-18, Minneapolis):

https://devdays.com

https://www.hl7.org/

* The EHDS (European Health Data Space): Official Overview

* Vivian Lee’s “The Long Fix”: Book Link

* Kill the Clipboard Initiative:

https://killtheclipboard.com

* Simplifier.net: FHIR Registry

Important Disclamer: DevDays is organized by both HL7 International and Firely

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!

In Episode 24 of Out of the FHIR, we’re skipping the press releases. I’m breaking down the three biggest lies being told on the conference floor and highlighting the few innovators who are actually using FHIR to move the needle.

The “Spicy” Summary:

* Stop calling it AI if your data is a mess. Clean FHIR data is the tax you haven’t paid yet.

* Interoperability isn’t a tech problem anymore. It’s a “who-gets-paid” problem.

* The patient is still an afterthought. Until we stop building silos, “patient-centricity” is just a marketing slogan.

Listen to the conversations from the show floor!

While at HIMSS I had a chance to look at the National Health Digital Architecture discussion paper released by the National Academy of Medicine.

Below are my thoughts on it.

The National Academy of Medicine’s (NAM) perspective on a National Health Digital and Data Architecture serves as a direct counterpoint to what is currently unfolding at HIMSS 2026. While NAM proposes a unified, foundational “operating system” for healthcare data, the reality on the conference floor is a fragmented landscape of competing interests.

Here is a review of the NAM strategy and why it is not gaining traction in the current HIMSS environment.

The Suggested Strategy: A Unified National Architecture

The NAM strategy calls for a fundamental shift in how health data is managed:

* A Shared Public-Private Utility: Treating data infrastructure as a “common good” (like roads or the power grid) rather than a competitive advantage.

* Standardized Semantic Foundation: Moving beyond just “sharing files” to a universal understanding of data meanings (FHIR-native, semantically aligned).

* A Single “Architecture of Trust”: A unified framework for identity, consent, and security that removes the need for thousand-point-to-point connections.

* Separation of Data from Applications: Storing data in a vendor-neutral layer so that clinical apps can be swapped in and out without data loss.

Why This Isn’t Happening at HIMSS 2026

1. The “FHIR-Washing” of Legacy Silos

Strategy: Standardized semantic alignment. Reality: While every booth at HIMSS 2026 prominently displays “FHIR,” most vendors are still “FHIR-washing.” They use FHIR as a thin translation layer on top of their proprietary, legacy databases. This maintains the vendor lock-in that the NAM strategy aims to destroy. True digital transformation requires data to be FHIR-native from the start, but vendors have no financial incentive to rebuild their core architectures.

2. The AI Distraction (Infrastructure vs. Shiny Objects)

Strategy: Laying the foundation first. Reality: HIMSS 2026 is dominated by Generative AI. Companies are racing to sell the “shiny” end-product (AI clinical scribes, predictive models) while ignoring the “plumbing” (data architecture). AI built on the current fragmented architecture results in “Black Box Silos” AI that only works within one specific EHR or payer system, which is the exact opposite of NAM’s vision for a national learning health system.

3. Competitive Moats vs. Common Utilities

Strategy: Infrastructure as a public-private utility. Reality: In the HIMSS exhibit hall, data is still viewed as an asset to be guarded, not a utility to be shared. The business models of many HIMSS “Anchor Tenants” (large EHR and data aggregators) rely on being the gatekeepers of patient data. There is significant resistance to a national architecture that would commoditize the data layer and force companies to compete solely on the quality of their software applications.

4. The “Information Blocking” Dance

Strategy: Seamless interoperability by design. Reality: Despite CMS and ONC mandates, “information blocking theater” is in full effect. Companies are technically compliant but operationally obstructive charging high API fees or creating complex workflows that discourage third-party integration. The NAM’s “Architecture of Trust” is impossible to build when the underlying participants are still incentivized to keep data within their own ecosystems.

This is why the NAM emphasizes that surface-level technical solutions alone won’t work. You have to resolve the underlying incentive misalignments through policy levers, shared commitment frameworks, and cross-sector collaboration.

Max has deep roots in the FHIR ecosystem from academia at the Technical University of Munich, to Firely, to years of consulting across Europe. Now he’s building something bigger: a company focused not just on FHIR infrastructure, but on how that infrastructure enables real, visible impact for patients through AI, Smart on FHIR, and even wearable integrations.

But the real theme of this conversation wasn’t “FHIR adoption.”

It was this:

Healthcare is about to be reshaped not by standards alone, but by what AI can now do with standardized data.

FHIR Is No Longer Niche

Max pointed out something many of us have felt over the past year:

FHIR used to be a niche standard discussed at connectathons and DevDays.Now it’s the plumbing behind consumer AI health integrations from tools like ChatGPT and Claude.

For the first time, health data is entering general-purpose reasoning systems that people use every day. That changes the game. Not because FHIR is new, but because AI can finally use FHIR data at scale.

“Software Is Inflationary”

One of Max’s best lines:

What used to take a year to build can now be built in days.

This is showing up at hackathons, startups, and even among 18-year-old builders experimenting with AI, spatial awareness, and real-time health event detection. The barrier to building healthcare software has collapsed if you understand the underlying problems.

The differentiator is no longer coding skill.It’s domain understanding and knowing what problem is worth solving.

The Real Problem: Incentives, Not Technology

Max made a powerful comparison:

In aerospace, countries cooperate on standards to prevent planes from colliding mid-air.In healthcare, we still don’t have true global data standards because the financial incentives don’t demand it.

Healthcare costs continue to rise (approaching a quarter of U.S. GDP), yet most digital tools help people navigate the system, not reduce the need to use it.

The real opportunity for AI + FHIR is:

* Preventing unnecessary ER visits

* Supporting preventative care

* Giving patients usable access to their data to power their self care

* Enabling decision support before care becomes expensive

Where AI Changes Healthcare First

According to Max, the most immediate impact won’t be in replacing doctors.

It will be in:

* Extracting structured FHIR data from unstructured inputs

* Real-time scene and event understanding (AI + spatial awareness)

* Reducing overload in emergency systems

* Supporting remote diagnostics and distributed care models

* Turning patient data into actionable, evidence-based guidance

We’re moving from:

“How do we move data between systems?”to“How do we use this data to keep people out of the system?”

The Future of FHIR: Less Open, More Precise

We also discussed the direction of FHIR R6 and the move toward tighter, more stable core resources while allowing innovation to happen in defined spaces.

Max’s view: this won’t reduce innovation it will accelerate it. Builders will have more certainty that what they implement won’t shift under them, while AI makes data mapping across standards increasingly trivial.

A Global View

Having worked across Europe and now the U.S., Max sees strengths and weaknesses in both systems. What’s clear is that:

Both systems need better use of data, and patients need more control and transparency.

That’s a central theme of Max Health and why he remains deeply committed to open source and global standardization efforts.

Where to Find Max

* Newsletter: maxhealth.tech

* LinkedIn: Max Nussbaumer

* Conferences, connectathons, and anywhere FHIR builders gather

The Big Takeaway

This episode wasn’t about FHIR history.

It was about this moment.

AI has arrived at the exact time when healthcare finally has a usable data standard. The combination means we can stop talking about interoperability as a goal and start using it as a foundation to solve real problems.

And the people who understand both the data and the problems are about to build very quickly.

1) Healthcare didn’t lack data. It lacked urgency.The pandemic didn’t introduce new analytics capabilities. It changed the cost of being slow. When delay becomes lethal, organizations suddenly discover they can make decisions in hours instead of quarters. That tells you something uncomfortable: speed was optional until it wasn’t.

2) The winning move wasn’t better dashboards. It was deciding which questions mattered.Pre-COVID analytics chased curiosity. During COVID, analytics chased survival. The shift wasn’t technical sophistication it was ruthless prioritization. Moneyball lesson: when resources are constrained, focus beats breadth every time.

3) Interoperability works best when you shrink the problem space.Northwell didn’t unify 70+ EHRs. They built a currently admitted patient index a small, high-value dataset tied directly to decisions. That’s classic systems strategy: optimize the part of the system where leverage is highest.

4) Real-time analytics requires trust more than compute.Two daily huddles. Locked pipelines. Tight access controls. The goal wasn’t “more data.” It was shared belief in a small number of metrics. In complex systems, trust is the scarcest input.

5) AI turns data quality from a nuisance into a risk multiplier.Bad data used to waste time. Now it produces confident, well-phrased errors at scale. AI doesn’t clean your data it accelerates whatever state your data is already in. This changes the ROI math on governance overnight.

6) The most dangerous bias isn’t malicious. It’s missing context.Models assume you’ve provided enough information. Healthcare almost never does. Missing baselines, fragmented history, and unspoken nuance quietly distort outputs. This is the hidden error term no benchmark fully captures.

7) Consumer AI creates a parallel healthcare system with no referee.Patients are already using AI for triage, interpretation, and reassurance outside clinical workflows. There’s no visibility, no accountability, and no feedback loop when the model is wrong. That shadow system will shape outcomes whether clinicians like it or not.

8) Accountability in healthcare AI is misaligned and unstable.Clinicians and health systems bear liability once AI output enters care. Vendors largely don’t. Patients bear risk when they self-diagnose with consumer tools. That imbalance won’t survive contact with real harm. Regulation is coming but likely late and blunt.

9) AI exposes healthcare’s incentive structure, not just its data gaps.If AI reduces unnecessary visits, insurers benefit first. If it increases demand through anxiety, providers feel the strain. Like Moneyball, the advantage won’t come from better tools it will come from understanding who wins and loses under new rules.

10) The real competitive advantage isn’t smarter models. It’s judgment.AI can summarize, predict, and suggest. It can’t know what matters most right now. The organizations that win won’t be the ones with the fanciest AI. They’ll be the ones that combine clean data, tight feedback loops, and humans who know when not to trust the machine.

Here are the key takeaways from our conversation:

1. Pharmacists Are Care Team Members, Not Just Dispensers

We often view pharmacy as a transactional space, but Pooja highlighted the shift toward clinical services. From COVID vaccinations to managing chronic conditions, pharmacists are often the most accessible healthcare providers in rural communities. However, to fully integrate them into the care team, they need access to clinical data (labs, diagnosis) that currently lives trapped in the EHR.

2. The “Better Together” Story: NCPDP + FHIR

A common question Pooja gets is: “Why don’t we just do everything in FHIR?”. Her answer is crucial for architects to understand:

* NCPDP has mastered real-time transactions over the last 40 years (claims adjudication, eligibility). You don’t fix what isn’t broken.

* FHIR is the clinical bridge. It allows pharmacists to pull the clinical context needed for prior authorization or care coordination, which can then be wrapped into an NCPDP standard for the payer. It’s not an or; it’s an and.

3. The AI & Patient Empowerment Frontier

We discussed the controversial but exciting potential of AI in pharmacy. While headlines panic about “AI refills,” the reality is that basic maintenance meds (like statins) are perfect candidates for auto-refill workflows, provided the pharmacist is looped in to check labs and vitals. Pooja and I both agreed that the “Holy Grail” is an AI agent that acts as a proactive partner scheduling appointments, reconciling meds, and reminding you to pick them up so caregivers aren’t left managing complex logistics alone.

4. Interoperability is a Business Problem, Not a Tech Problem

I shared my recent nightmare trying to schedule a colonoscopy stuck in phone queues and filling out 50-question Word documents because the referral data didn’t move. Pooja noted that while the “pipes” (TEFCA, FHIR) are being built, the business incentives to share that data are still lagging.

What’s Next? Keep an eye on the NCPDP Collab (a workflow-focused event in February) and the new consumer-facing real-time benefit check pilots.

🌶️ Spicy Takes

* The Quality Measurement Farce: Gene points out the absurdity of current metrics, like ADHD “management” only requiring two follow-up visits a yeah a bar so low it’s practically meaningless.

* The AI Scribe Illusion: A recent study suggests AI scribes don’t actually save physicians time; they just shift the workload from documenting during a visit to reviewing and editing after it.

* The NP Training Gap: Dr. Mui highlights the concern over the “boom” in nurse practitioner programs with minimal in-person training compared to the rigorous, repetitive clinical hours required in traditional residency.

* “Vibe Coding” vs. Healthcare Reality: While AI-assisted prototyping (vibe coding) is great for front-end ideas, the back-end healthcare infrastructure remains notoriously difficult and rigid.

💡 Key Takeaways

* Innovation via Proximity: The most effective solutions come from those closest to the problem. Clinicians who understand both medical and technical workflows are best positioned to fix healthcare’s bottlenecks.

* Skills-Intelligence for Quality: Healthcare should move away from just “checking boxes” and toward measuring specific competencies (like pattern recognition or antibiotic stewardship) to identify where clinicians need support.

* Stop Reinventing the Plumbing: Healthtech startups shouldn’t have to rebuild back-end infrastructure (FHIR connectivity, etc.) from scratch. Standardized “plumbing” allows for faster innovation.

* The Power of a “Beginner’s Mind”: Dr. Mui encourages experts to lead with inquiry. Writing and sharing simple questions often leads to deeper insights than trying to maintain an “expert” persona.

* Sustainability is the Goal: With healthcare spending nearing a third of the US GDP, technology’s primary role must be to create a sustainable model that actually improves patient experience and clinical outcomes.

If you’ve followed my work, you know I’m obsessed with the intersection of FHIR and Analytics. But there is often a disconnect between the “high priests” of standards and the data engineers trying to run SQL queries in relational databases.

In this episode, we bridge that gap.

We discuss:

* The SQL on FHIR Conference: Why the industry is standardizing “View Definitions” to make FHIR portable across different data warehouses.

* CQL vs. SQL: Why Clinical Quality Language is great for requirements, but SQL (aided by AI) is another way to execute at scale.

* The “Dump Truck” Theory: Why APIs aren’t always the answer for analytics teams who just want bulk data access.

* Automating Interop: How I’m using AI Agents to do the grunt work of mapping and flattening data, so you don’t have to wait on “Larry” (the fictional, overworked hospital IT admin) to run a report for you.

It was a blast recording with the High Dimensional team they bring a refreshing, “not just drinking the FHIR Kool-Aid ” reality to the conversation that is often missing from standards discussions.

Enjoy the listen!

Gene

Spicy Takes

* The “Windows ME” of Standards: We discussed how the industry seems to be collectively skipping FHIR R5 (like Windows 95/98 to 2000) and waiting for FHIR R6 to become the normative standard, avoiding the “trough of disillusionment” associated with intermediate versions.

* The “Larry” Bottleneck: We personified the interoperability problem as “Larry” the overworked IT guy at the health system who smokes two packs a day, takes three lunches, and ignores your ticket for a claims feed. The goal of Bulk FHIR is to bypass the “Larry problem,” but we aren’t quite there yet.

* Phil’s Hot Take: In a moment of pure chaos, Phil claimed Windows ME was the best operating system ever made. (We agreed to disagree).

Fun Facts & Why You Should Listen

* The Portuguese Connection: I shared a story about meeting Grahame Grieve (the “Father of FHIR”) at a FHIR camp in Portugal, highlighting how the “open” nature of the standard was intentional, even if it creates headaches for data modelers today.

* Real-Time Solutions: We discuss actual tools (like the Tuva Project and Health Samurai) that are solving these problems now, not just theoretically.

* Why Listen: If you have ever stared at a nested JSON object and wondered how to get it into a dashboard without crying, this episode explains exactly how the industry is solving that problem.

The Journey to Value-Based CareMichael Westover’s path to his current role began with a strong foundation in healthcare consulting, where he guided states and municipalities on healthcare benefits. His desire to be closer to the action led him to pursue an MBA with a healthcare emphasis, eventually joining a startup focused on data integration and analytics. Westover highlights the importance of understanding both the business and clinical sides of healthcare, stating that the business of healthcare differs significantly from patient care.

Challenges of Data Integration One of the most pressing issues in healthcare today is the complexity and fragmentation of data systems. Westover describes the cumbersome process of sharing data with vendors, where a simple request for longitudinal data can take months to fulfill due to the vast number of different systems involved. He emphasizes that healthcare organizations often struggle with outdated methods of data exchange, relying on CSV files and manual queries that can lead to errors and inefficiencies.

The Importance of StandardizationWestover advocates for standardizing data practices to improve interoperability. He notes that while there are national initiatives like TEFCA aimed at enhancing data exchange, there remains a significant gap in sharing the right data. By collaborating with payer partners to identify crucial data needs, Providence has successfully established strong relationships that benefit both parties. This collaboration allows for more accurate data exchange, which is essential for effective value-based care.

Success Stories notable example of successful data integration is Providence’s partnership with Humana. Their collaboration focuses on using national data standards, such as FHIR, to streamline data exchange through standardized APIs. This approach enables both organizations to access and share critical information, such as member rosters and clinical data, efficiently. Westover explains that aligning incentives and fostering open communication have been key to this success, as both parties recognize the importance of accurate data in achieving high-quality patient care.

Key Takeaways: Westover’s insights shed light on the vital role of data integration in advancing value-based care. Key takeaways include:

1. The journey to effective data integration requires a deep understanding of both clinical and business aspects of healthcare.

2. Standardizing data practices is essential for overcoming the challenges of fragmentation and inefficiency.

3. Building strong partnerships with payers can lead to improved data exchange and better patient outcomes.

4. Successful collaborations, like that between Providence and Humana, demonstrate the power of aligning incentives and fostering open communication.

As the healthcare industry continues to adapt to value-based care models, the importance of effective data integration cannot be overstated. By learning from successful partnerships and advocating for standardized data practices, healthcare organizations can work together to improve patient outcomes and streamline operations.

FHIR Data guy brings together two heavy-hitters from his past, Eric Melymuk Principal Engineer at Progyny and John Dobak Senior FHIR Analytics Engineer at b.Well a trio of veterans who all came up in the “bad old days” of HEDIS and traditional quality measurement.

This is the story of a technology stack (and a mindset) that defined healthcare data for decades, and the new stack that’s poised to replace it. They dive deep into the messy, complicated, and fascinating transition from the world of custom SQL, claims data, and regulatory checklists to the new frontier of FHIR, CQL, and true clinical interoperability.

Hot Takes

* The “Mind Shift” Thesis: Perfectly stated by Eric, is that the move from traditional SQL-based reporting to FHIR + CQL (Clinical Quality Language) isn’t just a syntax change. It’s a fundamental mind shift away from “logic customized to my organization” and toward “universal clinical intent.” This is the unlock.

* Beware the “Gray Water”: The biggest risk of the new FHIR-based world is creating “dirty FHIR data.” It’s data that is technically compliant (it passes the validator!) but is clinically useless. He calls this “gray water,” and it’s the next great crisis. Organizations treating FHIR as a low-cost, check-the-box “cost center” are just propagating this gray water downstream, poisoning the well for everyone else.

* The “Consumer Reports” Blind Spot: For decades, HEDIS and Star Ratings have been the “Consumer Reports for healthcare” (John’s analogy). The problem? We’ve been hiding the magazine from the consumers. The real promise of FHIR isn’t just better back-end reporting; it’s finally putting that quality data into the hands of the patients making the decisions.

* LLMs as the Co-pilot, Not the Pilot: ”Do we even need measures in the age of AI?” Yes. But AI and LLMs aren’t the replacement for clinicians or measures. They are the co-pilot (Eric’s term) that helps builders create the logic and test cases, and they may become the translator that finally makes this data understandable to a patient.

* The Cultural Battle: This isn’t a technology problem; it’s a culture problem. The entire panel agrees: if your organization views FHIR and interoperability as a regulatory “cost center,” you’ve already lost. The winners will be the ones who find executive champions (Eric’s point) who understand this is a new product and the strategic foundation for everything that comes next.

From Brittle SQL to a FHIR Future

This conversation is a journey from the past to the future of healthcare data.

Act 1: The Old World (The HEDIS Grind) The guys reminisce about the “hardest problem” in health tech: traditional quality reporting. It was a world of brittle, custom-built SQL engines, siloed (and non-technical) quality departments, and a total dependency on claims data not because it was the best data, but because it was the only consistent data.

Act 2: The New Stack (The FHIR/CQL Unlock) Gene pivots the conversation to the “new stack.” The difference is night and day.

* Old Way: NCQA hands you a 200-page book (Volume 2) and says, “Good luck.” You have to design the schema, the logic, the whole thing.

* New Way: NCQA hands you the code. The schema is the FHIR Implementation Guide (IG). The logic is the executable CQL file. The entire low-level, error-prone interpretation is abstracted away.

Act 3: The Messy Middle (The “Gray Water” Problem) But this transition is where the real drama is. The panel identifies two massive risks:

* The Data Itself: We’re moving from clean claims data to messy, unstructured clinical (EHR) data. The old logic (built on claims “artifacts” like Place of Service codes) simply doesn’t map.

* The “Cost Center” Trap: Because of regulatory mandates, organizations are scrambling to create FHIR data. They’re doing it cheap, fast, and without clinical governance. This creates John’s “gray water”FHIR bundles that are valid but meaningless, which will break everything downstream.

Act 4: The Real Goal The episode concludes by refocusing on the why. Eric makes the passionate case that we’ve “lost sight” of the goal. It’s not about checking boxes for payment; it’s about improving care. The new stack (FHIR, CQL) is the first real chance to build systems that give data back to providers and patients in real-time, finally fulfilling the original promise of quality measurement.

The Playbook: Learnings from the HEDIS OGs

* 1. Don’t Replace the Warehouse, Feed It. This is a key strategic insight from John. CQL and FHIR do not replace your SQL data warehouse. They work together. Use the CQL engine to process the messy, real-time FHIR data, and then use the output (the FHIR MeasureReport resources) to feed your traditional SQL warehouse for population-level analytics. It’s the best of both worlds.

2. Your FHIR Implementation Is Your Product. The panel’s biggest warning: do not treat your FHIR implementation as a “cost center” or an IT project. It is a product. It requires governance, clinical input, and executive champions. If you don’t, you’ll spend millions building a “dirty data” engine that provides no value.

* 3. Use the EOB as Your Bridge. How do you get from a claims-based world to a clinical-data world? Eric points to the bridge: the ExplanationOfBenefit (EOB) FHIR resource. It’s designed to model adjudicated claims. This allows you to “dip your toe in” by mapping your existing, clean claims data into a FHIR structure first, before you tackle the mess of raw EHR data.

4. Get Your Feet Dirty (The Community is Open). The single best way to learn is to do. The HL7 FHIR community (on Zulip) is famously open. As John notes, you can even feed an entire Implementation Guide into an LLM and start asking it questions. The barrier to entry has never been lower.

Mark Scrimshire’s Journey to FHIR®

Mark recounts his two-decade career, which provides a vivid history of modern interoperability. His journey began around 2009 during the American Recovery Act, where early initiatives like CMS’s “Blue Button 1.0” allowed patients to download their claims history—often as a massive, unusable text file thousands of pages long.

His pivotal moment came as an Entrepreneur in Residence at CMS, where he was tasked with building a modern API for Medicare beneficiaries. Instead of creating a proprietary standard, he chose to adopt the emerging HL7® FHIR® standard. This project evolved into Blue Button 2.0, a landmark API that proved the viability of using FHIR® at a national scale. He was instrumental in defining key resources like the Explanation of Benefit..

This work at CMS laid the groundwork for the 2020 CMS Interoperability and Patient Access final rule, which mandated that payers provide patient access via FHIR® APIs. Seeing this regulatory shift coming, Mark co-founded Onyx to build a platform that helps healthcare organizations comply with these rules and leverage the new data-sharing paradigm.

The Power of Regulation as a Catalyst

A central theme of the conversation is the role of government regulation in driving change. Mark argues that mandates are essential for overcoming industry inertia. He uses the upcoming Prior Authorization rule as a key example. While the rule only mandates the API for certain government-funded plans, he predicts that the efficiency gains (getting an answer in 15 seconds via an API versus days via fax) will create immense market pressure for payers to offer the same capability for their commercial plans. This transforms a compliance requirement into a strategic business transformation.

Interesting Observations

* Flipping the Script on Data Integration: Mark powerfully articulates a common problem in healthcare IT: an “80/20 rule” where organizations spend 80% of their effort just trying to get the data and only 20% actually using it for analysis and insight. He argues that standardized FHIR® APIs and trust frameworks are designed to “flip this script,” allowing organizations to spend only 20% of their effort on connectivity and 80% on creating value from the data.

* The Core Challenge is Trust, Not Technology: Mark asserts that the biggest barrier to seamless interoperability is not a lack of technology but a lack of scalable trust. Establishing individual connections between thousands of payers and providers is a manual, time-consuming process that costs “man-years” of effort. The solution lies in building robust trust frameworks (like TEFCA or security protocols like UDAP) where an entity can be vetted once and then securely connect with anyone else in the network, dramatically reducing friction.

* Regulation as a “Bridgehead” for Market Transformation: Mark views regulations not as the end goal but as a “bridgehead” a starting point that establishes a new technical and business reality. Once the infrastructure (like FHIR® APIs) is in place due to a mandate, the market naturally finds innovative and more efficient ways to use it, pushing adoption far beyond the original scope of the rule.

* FHIR® as an Enabler for AI, Not a Relic: Addressing the question of whether AI makes standards obsolete, Mark argues the opposite. He states that having well-structured, standardized data from FHIR® APIs makes AI more efficient, accurate, and less resource-intensive. Without standards, AI models would have to waste immense computational power on parsing unstructured documents (like PDFs) and de-identifying data, a process FHIR® makes unnecessary. Standardized data is the clean, reliable fuel that will power the next generation of healthcare AI.

In our latest episode of Out of the FHIR, I had a refreshingly candid conversation with Scott Rossignol of Farseen Advisors. With over 15 years in the trenches, including a formative period at Epic, Scott brings a dose of reality to the often-hyped world of FHIR and interoperability.

We didn’t just talk about standards; we talked about strategy, pitfalls, and the hard-earned lessons from building and deploying real-world healthcare applications. If you’re tired of the buzzwords and want to know what it actually takes to succeed, this episode is your guide.

Here are some of the spiciest, most crucial takeaways from our discussion:

Hot Take #1: Stop Building for Interop. Build a Product People Want.

One of the most powerful points Scott made was a fundamental shift in mindset. Too many companies lead with their integration strategy, believing that connecting to data is the product itself. Scott argues this is backwards.

He shared an experience of getting a SMART on FHIR app live in just three days. The secret? It wasn’t just the tech. It was a great product solving a clear problem, with motivated stakeholders who wanted it.

As Scott put it, the goal isn’t just to “do interoperability.” The goal is to “build a product that works and pleases your customers, and improves with integration.” Your interoperability strategy should serve your product, not the other way around. If the core product doesn’t provide value on its own, no amount of data integration will save it.

Hot Take #2: FHIR is a Tool, Not the Whole Toolbox.

For developers diving into health tech, it’s easy to assume FHIR is the one-and-only answer. But as Scott rightly points out, clinical and administrative workflows are far too complex for a single standard to solve everything.

The perfect example? Real-time notifications. Scott explained, “If you need event-based pushes, like a notification for a patient admission, you’re not going to get that from a standard FHIR API. You’ll need to look at other standards like HL7v2 ADT feeds.”

Until FHIR Subscriptions are universally adopted and supported (which is still a ways off), the decades-old HL7v2 standard remains a critical part of the ecosystem. True interoperability experts know how to build a hybrid system, leveraging the best tool for the job whether it’s a modern FHIR query or a classic, event-driven ADT feed.

Hot Take #3: National Networks are Promising, But Don’t Hold Your Breath.

Initiatives like TEFCA and the CMS Aligned networks promise a future where data flows freely across a “network of networks.” The vision is powerful, but Scott provides a necessary reality check on the current state.

While progress is being made behind the scenes, what’s publicly available is often limited. Scott notes that much of the CMS Aligned network’s visibility has been “just a few press releases so far.” The real work of establishing trust, defining mature use cases, and getting participants to exchange meaningful data is a slow, arduous process.

His advice for organizations today? Don’t wait for a national network to solve your problems. Identify your specific data needs and find the most direct path, whether that’s through patient-authorized access, regional HIEs, or direct BAA-gated connections. These networks will become incredibly valuable, but you need a strategy that delivers value in the meantime.

* On Implementation Reality: “You could have the perfect application that works flawlessly in a sandbox, but if the health system has a six-month security review process, your timeline is six months. That’s the reality.”

* On EHR Vendor Differences: “I love that Epic has a great user experience... I love that Cerner provides great write-back capabilities... I love that Athenahealth has a one-click deployment. The fact that I can’t say all those things about one EHR tells you everything you need to know about the challenges of integration.”

Don't miss this episode as they discuss:

* The radical idea that human language might replace complex healthcare codes. Could the conversational abilities of large language models make traditional coding systems like SNOMED and CPT obsolete?

* A "back to the future" approach to patient care. How can AI-powered conversational interoperability bring back the deep, personalized conversations that have been lost in the current fee-for-service, time-crunched healthcare system?

* The slow pace of FHIR adoption and the future of healthcare standards. With FHIR implementation guides languishing, is conversational interoperability the catalyst needed to unlock the true value of healthcare data?

Get ready to challenge everything you thought you knew about healthcare interoperability and the role of AI.Podcast Platform Links

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Must read articles on Conversational Interoperability and report out from the HL7 Connectathon

https://www.linkedin.com/pulse/conversational-interoperability-takes-shape-read-out-from-mandel-md-379fc

https://www.linkedin.com/pulse/why-conversational-interoperability-essential-future-mark-kramer-retre

Where We’re At

* CQL maturity: Finally stable enough to calculate without different engines giving different answers. But DEQM (Data Exchange for Quality Measurement) is still a labyrinth powerful, but too flexible and waiting for industry alignment.

* QI Core vs reality: Full QI Core adoption is a fantasy today. The pragmatic move is USCDI+ Quality subsets stop boiling the ocean, start with buckets.

* NCQA & CMS pressure: Both are turning the screws. NCQA’s push for FHIR-only HEDIS reporting and CMS’s signals on hospital quality programs mean “claims-only” days are numbered.

The Payer/Provider Dilemma

Legacy organizations face two doors:

* Stand up a FHIR-native system

* Slap a FHIR façade on legacy infrastructure

The advice: don’t wait. Off-the-shelf solutions exist, but the hurdle is domain expertise and data quality. Bad data = garbage measures. As Gene put it, we’re “building a new house while still living in the old one” but you still have to start building.

Holy Grail Moments Ahead

Two tipping points will define the dQM journey:

* When digital quality becomes cheaper and easier than today’s bloated admin burden.

* When FHIR measures double as care gap alerts in real time. Imagine a doctor getting an instant ping “you’re overdue for a colonoscopy” instead of an end-of-year spreadsheet.

And let’s not forget the patient. Today’s care gap lists never reach them. The new paradigm? Agents, APIs, and FHIR empowering patients to close their own gaps.

FHIR + AI = Frenemies or Besties?

* Bill’s take: AI won’t kill standards it’ll sharpen them. Use LLMs to craft better, transparent measures.

* Benji’s take: AI + FHIR are a perfect match. FHIR’s granular, graph-like structure is exactly what agents and LLMs love. The future is FHIR-fueled AI workflows, not black-box medicine.

Industry Spice

* TEFCA & endpoint discovery: The dream is making FHIR directories “as trivial as Google Maps.” Until then, duplicative tests will keep costing patients pain and payers billions.

* Gene’s personal mic drop: waiting two weeks for a CD-ROM of med records in 2025. That’s why interoperability still sucks.

Final Notes

* Benji is launching a FHIR for Business Analysts course this fall finally, something for the non-technical crowd.

* Bill continues running DMV “FHIRside dinners” and teaching internal FHIR courses.

* And the trio is hyped for the Pittsburgh Connectathon, where theory meets war stories in real time.

In this special deep dive, we're joined by Chad Sanderson and Mark Freeman of Gable AI to explore the solution: the Shift Left movement and Data Contracts. We break down how this new paradigm treats data as a first-class product, creating a clear, enforceable agreement between the data engineers who produce the data and the teams who consume it.

For our listeners in health tech, this conversation is especially critical. Think of the HL7® FHIR® standard itself as a powerful, industry-wide data contract. It defines the precise structure, terminology, and API rules for exchanging healthcare information. Applying a 'Shift Left' mindset means treating FHIR compliance not as a final validation checkpoint, but as a core requirement from the very beginning of the development process. By enforcing the 'contract' of FHIR at the source—before data ever leaves the EHR or clinical system we can build more reliable interoperable systems, reduce costly data errors downstream, and ultimately deliver better value and more effective AI solutions.

Tune in to learn:

* Why the modern, federated data stack broke our old processes.

* How to bridge the gap between software engineering and data teams.

* Practical steps to start implementing a "data as a product" culture that delivers real value to your clients.

Learn More: Our guests literally wrote the book on this! Check out Chad Sanderson's new O'Reilly book, "Data Contracts," for the complete guide. ADDITIONAL Reading

* Gable AI – Data Contracts: Data Contracts Book (Chad Sanderson & Mark Freeman)

* Shift Left Data Manifesto: Shift Left

* HL7 FHIR Overview: HL7.org FHIR Overview

* FHIR Contract Resource: FHIR Contract Definition

* FHIR Profiles / StructureDefinition: FHIR Profiling Guide

Technical Tools

* HL7 FHIR Validator: FHIR Validator CLI

* Gable Data Contract Examples: Example Contract Specs

* FHIR JSON Schema (useful bridge): FHIR JSON Schema

AI + Healthcare Context

* NCQA Digital Quality Ecosystem: NCQA Digital Measurement

* SMART on FHIR: SMART Health IT

FHIR Contracts App

* 🔧 FHIR Data Contract Generator (beta): appA playground to generate FHIR-based data contracts aligned with shift-left methodology.

Kerry and Gene jump right into the big questions: Can an AI really understand healthcare without deep medical knowledge? They explore the wild, unpredictable nature of human health data (it's not your average software log!) and Kerry shares a killer story from her time at Amgen about why context is EVERYTHING when building predictive models.

The conversation then tackles the big one: AI agents giving medical advice. From today's clinician-in-the-loop systems to the future of patient-facing bots, they discuss the promises, the perils, and the fact that the AI genie is already out of the bottle.

Plus, get the inside scoop on the buzzworthy Model Context Protocol (MCP). Is it the next big thing or just a lot of hype? Kerry breaks down the good, the bad, and the tricky parts of getting AI to speak fluent FHIR without making stuff up. This isn't just a tech talk it's a fun, inspiring, and refreshingly real conversation about the passion, purpose, and incredible people building the future of healthcare.

Tune in for:

* A masterclass on the nuances of AI in health.

* The real story behind starting a health-tech company.

* An optimistic look at the vibrant and growing FHIR community.

* ...and maybe even plans for a future live-coding session on Twitch!

This week, I am thrilled to be joined by a true visionary and expert in the health-tech space, Jill DeGraff. If you follow the conversation around healthcare data and policy, you've undoubtedly come across Jill's insightful analysis and forward-thinking perspectives.

In this episode, we jump straight into the topic that's lighting up every health-tech feed: the new CMS Interoperability Framework. Just days after its announcement at the White House, Jill helps us cut through the noise and understand what this "moral commitment" from industry leaders and the government truly means for the future of healthcare data.

Is this just another initiative, or is it the start of a genuine "race to the top"?

In our conversation, we cover:

* The 'Why' Behind the New Framework: Jill explains the crisis-level urgency driving this initiative, from the national disease burden to the economic unsustainability of Medicare.

* What's Different This Time? We discuss why this framework is more than just "TEFCA 2.0," focusing on its shift from slow, consensus-based iteration to a model of rapid execution and accountability.

* "Kill the Clipboard" and Beyond: A breakdown of the key pledges, including the National Healthcare Directory, modern digital identity verification, and the push for a truly seamless patient experience.

* Leaders vs. Laggards: Jill offers a compelling analysis of the business implications, arguing that organizations that have invested in modern architecture will thrive, while those reliant on legacy systems will face significant challenges.

* Career Insights: We kick off the episode with a fascinating look at Jill's career path from law to health-tech, and she shares invaluable advice for anyone considering a career in consulting.

"This is a moral commitment... It's about unleashing the creative energy of the private sector to do what it does best, which is innovate and build value for people." - Jill DeGraff

This isn't just a policy discussion; it's a look at the seismic shift happening right now in our industry. Whether you're a developer, a clinician, a policy wonk, or an executive, this conversation provides a crucial map to navigate the new landscape of healthcare interoperability.

Listen now to get the full story!

In this fascinating conversation, we travel to Israel to explore its unique and rapidly evolving healthcare ecosystem. With a new law mandating nationwide, FHIR-based data portability, Israel is at the forefront of a healthcare data revolution. Eyal provides an unparalleled, on-the-ground perspective on this monumental undertaking, discussing the real-world challenges and strategic decisions involved in modernizing an entire nation's health data infrastructure.

Tune in to learn about:

* The Clalit Scale: The immense scope of managing data for 14 hospitals and a massive community network within Israel's public health system.

* A National FHIR Mandate: How a new law is compelling the entire country to rebuild its data foundation on FHIR, moving from legacy systems to real-time, structured data exchange.

* The AI Race vs. Data Foundations: Eyal breaks down the critical tension between the rush to deploy AI solutions and the essential, foundational work of ensuring data quality and interoperability through standards like FHIR.

* A Unique Patient Consent Model: A deep dive into Israel's planned Patient Consent Manager (PCM) and why putting the patient in control of their data is a non-negotiable cornerstone of their new ecosystem.

* Global Lessons in Interoperability: How Israel's journey offers powerful insights for healthcare systems worldwide, including the US and its TEFCA initiative, on what it truly takes to build a connected health network.

This is a must-listen episode for anyone interested in health data interoperability at scale, the practical realities of implementing FHIR, and the crucial relationship between structured data and the future of trustworthy healthcare AI.

In this episode of Out of the FHIR, Gene sits down with Ron Urwongse founder of Defacto Health, a self professed Provider Data Nerd, health tech strategist, and one of the sharpest minds in provider data infrastructure to unpack why provider data remains stubbornly inaccurate, fragmented, and operationally painful across the healthcare ecosystem.

💡 Ron exposes the root problem: most healthcare APIs are still glorified databases with business logic missing from the stack. He explains how data accuracy isn’t just about syncing directories it's about rebuilding the underlying operational model with intention, alignment, and contextual relevance.

🔎 Featured Topics:

* Why provider data is only 50% accurate and why that state hasn’t changed in a decade

* The myth of a universal provider directory and why a national directory won’t solve what’s fundamentally a business logic problem

* How plans, vendors, and digital health players are stuck pushing CSVs and duct-taping endpoints

* What it would take to build an AI-native, agent-based model for provider data stewardship

* The limits of FHIR and the opportunity to use it as an orchestration layer, not just a data exchange format

💥 Key Quote:"You can’t clean up provider data until you change the incentives, rebuild the system logic, and stop pretending it’s just a data formatting issue."

This episode is a must-listen for anyone touching provider directories, network management, quality measurement, prior auth, or digital health operations. Ron doesn’t hold back and it’s the perspective the industry needs to hear.

🎧 Listen now and rethink what it really means to fix provider data.

In Part 2 of our special double-header episode, Eugene Vestel dives even deeper into the bold vision of a connected healthcare ecosystem with two of the industry's sharpest minds:

* 🧠 Brendan Keeler – Interoperability Practice Lead at HTD Health

* 🚀 Jason Kulatunga – Founder of Fasten Health, Techstars NYC '24

We go beyond the buzzwords and into the infrastructure truths that will define the next decade of healthcare:

🔹 Why the FHIR movement is both essential and already obsolete in some ways 🔹 The brutal reality of building consumer-first health tech in a system that wasn't designed for it 🔹 How we might look back in 3–5 years and think, "What were we thinking?" 🔹 Lessons from Plaid’s playbook and what not to copy in healthcare 🔹 The emergence of an autonomous data layer that lets AI act without human crutches

This isn’t hype. It’s the kind of grounded, uncomfortable, and ultimately liberating conversation we need to have if we want to break through the ceiling of digital health and actually build something better.

🔥 If Part 1 sparked the idea, Part 2 sets it on fire.

In this episode of Out of the FHIR, we go deep into the future of healthcare interoperability with two visionaries at the forefront of change: Brendan Keeler, Interoperability Practice Lead at HTD Health, and Jason Kulatunga, Founder of Fasten Health and Techstars NYC alum.

We explore:

* 🧩 What a "Plaid for Healthcare" could really look like

* 🔄 Why control, transparency, and patient-directed access are non-negotiable

* ⚙️ The architectural and cultural hurdles standing in the way

* 📊 How Fasten Health is creating a personal health API layer that empowers individuals with their own data

* 🔮 Where we're headed next and what needs to happen to get there

This conversation is a raw, practical, and energizing look at what it means to rebuild trust and functionality into the healthcare data experience.

Whether you're a product leader, developer, or policy wonk, this episode unpacks the real implications of a truly open health data ecosystem and what it’ll take to get us there.

Listen now, and join the movement.

🚀 What’s Joshua Excited About?

* Payer-to-Payer ExchangeA pivotal shift enabling payers to consume data—not just expose APIs—transforming how charts are transferred and reducing manual burden. Huge potential impact on chart chase inefficiencies and member onboarding.

* FHIR-Specific AI Model Evaluations (FIRE Evals)Joshua outlines Flexpa's work on evaluating LLMs for FHIR tasks like:

* Extracting structured data from FHIR resources

* Generating FHIR bundles from text

* Using reinforcement learning with verified rewards (not RLHF) to improve smaller, private models that outperform general LLMs in FHIR use cases

* FHIR validate operation as a reward signal in reinforcement learning, opening the door for better automated resource generation.

💡 Underrated Innovations

* Smart Health Cards & LinksJoshua sees these as massively underutilized. He envisions paper-based lab results with QR codes enabling seamless data transport—a simple but transformative workflow for patients and providers.

* Patient “Sneaker Net” as an Interop HackPatients as the natural transport layer—bringing their own data (e.g., via QR codes or printed links) could rival national HIE efforts in utility.

* ChatGPT for Patient InsightReal-world use: Eugene shares how he used GPT to interpret his child’s lab results. This is already happening—users are moving ahead of systems.

🏦 Financial Interop Lessons for Healthcare

* OAuth SuccessHealthcare learned from fintech, starting with OAuth, unlike fintech’s slow rollout.

* Why Plaid’s Model Doesn’t Fully Translate

* Fewer, more cohesive accounts in finance (e.g., Amex, Chase)

* Healthcare data is fragmented across insurers, providers, labs, often with confusing branding (e.g., the "Blue Cross problem")

* The Real Challenge? Data Locator ConfusionEven insiders can’t always identify where data lives. This limits the utility of personal health apps or wallets—too niche for mass adoption.

🔧 Tech That Needs More Love

* SQL-on-FHIRJoshua ranks this as his #2 underrated tech (after Smart Health Cards). While not truly “SQL,” it simplifies FHIR's nested structures into tabular formats—crucial when working with bulk FHIR data.

* Flexpa’s Open Resources

https://my.flexpa.com

* : Free tool to access your health plan data

* Flexpa's Payer Directory: Public directory of payer APIs, location access endpoints, etc.

🔚 Final Thoughts

* The promise of interoperability is not just connectivity—it’s actionable insight, better care, and easier access.

* More orgs should share implementation experiences like Flexpa does to reduce duplication across the FHIR ecosystem.

In this episode, Gene Vestel sits down with Joshua Kelly, CTO of Flexpa, to unpack the complexities of healthcare interoperability. They discuss real-world challenges, cutting-edge strategies, and practical solutions for seamless data exchange across healthcare systems. Josh shares valuable perspectives on the future of interoperability, highlighting opportunities to transform patient care through technology and collaboration. We dive deep with Josh into why healthcare is so different from other industries and the critical role terminology plays in interoperability. Plus, we nerd out on issues around coding concepts and the misuse of value sets in FHIR!

Episode 3 of Out of the FHIR is for the builders, skeptics, and believers in making healthcare data actually usable.

🎙️ We talk about:

* Why you can’t build real AI with garbage data even if it’s from Epic’s APIs

* How 400+ custom Epic installs = a fake sense of interoperability

* Why FHIR is the glue and how Cody's team at Medplum is building with it from day one

* The uphill battle to get structured data into workflows that actually help providers (instead of adding to burnout)

🔥 We also compare TEFCA and HIEs to supermarkets sorting rotten bananas. Yes, really. And it works.

👇 I’d love to hear from you:What’s the worst data mess you’ve seen while building with FHIR or integrating with an EHR?

In this episode of Out of the FHIR, Eugene sits down with Darren Devitt — a seasoned med tech and Health IT consultant, widely recognized for his practical, no-fluff approach to FHIR education and implementation. Known for his engaging presentations at HL7 Dev Days and his hands-on training content, Darren brings a rare combination of deep technical insight and sharp industry perspective.

We dive into:

* Darren’s journey in the world of FHIR

* The real-world gaps between standards and implementations

* Why FHIR education still has a long way to go

* Lessons learned from working with large vendors and small teams alike

* How to empower devs, analysts, and leaders through smarter training—not more jargon

This episode is a must-listen for anyone looking to bridge the gap between theory and practice in FHIR adoption. Whether you’re building, learning, or just trying to survive in the world of interoperability, Darren’s insights will resonate.

Grahame offers insights into why FHIR quickly became a global standard, addressing not just technological but also practical and strategic challenges faced by healthcare systems worldwide. He discusses how FHIR enables streamlined, efficient data exchange, fostering innovation and improving patient outcomes across diverse healthcare settings.

The conversation also explores the future of healthcare technology, including the transformative impact of AI and analytics powered by structured data exchange enabled through FHIR. Grahame emphasizes the role of a vibrant, collaborative community in driving continuous innovation and shares his vision for the next evolution of interoperability and health tech.

Tune in to discover why FHIR is foundational to the future of healthcare, unlocking powerful possibilities for innovation, efficiency, and improved patient care.

Listen now and join the conversation on how FHIR continues to shape the future of health technology.