HIMSS26 made 1 thing unmistakably clear: the center of gravity in U.S. health IT is shifting to FHIR‑native digital quality measures, interoperable data pipelines, and AI tightly embedded into clinical and administrative workflows.

For anyone working in the CMS quality and value‑based care ecosystem, the “nice‑to‑have” era is over. FHIR and digital quality measures (dQMs) are becoming the operating system.

Dr. Oz’s challenge: Make technology deflationary

In a plenary conversation with CMS leaders, Dr. Mehmet Oz set the tone for the week by contrasting healthcare with the rest of the economy. Banking, entertainment, and logistics have used technology to become deflationary. Healthcare has mostly used technology at the end of the cascade — more imaging, more complexity — while remaining inflationary

His challenge to the industry was simple and bold:

1. Use technology to prevent bad care, not just document it.
2. Meet patients where they live — at home, in their daily decisions — not just when they hit the ED or ICU.
3. Deploy AI against mundane friction and preventable deterioration so it becomes a stabilizing, cost-reducing force, not an added expense.

He tied this directly to macroeconomics. Keeping the average working American healthy and in the workforce for 1 additional year could add nearly a trillion dollars to GDP. That’s the backdrop for CMS’ push toward digital quality and FHIR. Better quality data isn’t a compliance exercise; it is core to affordability and access.

CMS, FHIR, and the end of clipboard medicine

CMS leaders described how today’s interoperability still forces patients to do the work. One speaker shared a 16‑year personal care journey. Even after major policy efforts, care episodes still involved faxed records, CDs driven across town, and multiple paper clipboards capturing the same medications and history.

CMS wants to change today’s interoperability issues with a three-part technical strategy supporting dQMs:

1. Real‑world interoperability via modern identity and consent. CMS wants beneficiaries to verify themselves once and be able to click 1 button to send their records to a chosen app or provider. This would happen by using FHIR‑based APIs and USCDI/USCDI Quality elements under the hood.
2. “Kill the clipboard” with structured, FHIR‑based intake. Imagine patients arriving with a QR‑style credential that sends structured data like history, medications, and attachments straight into the EHR. That would eliminate re‑typing paper forms and feed digital quality measures.
3. CMS as infrastructure: directory, identity, fraud controls. Investments in a National Provider Directory and a modern Medicare login are built on the same rails. A soft launch of the new identity experience saw strong uptake and also surfaced a meaningful share of newly attempted accounts as likely fraudulent, blocked by improved identity proofing. That foundation supports secure, trustworthy data flows for dQMs.

This is how CMS plans to turn policy into practice and give dQMs reliable fuel.

Digital quality measures on FHIR: From vision to implementation

A panel of CMS, federal tech leaders, and a health system ACO executive shared where CMS is on its dQM journey and what’s next.

What’s in place

1. 70 QDMbased eCQMs converted to FHIR, with all new eCQMs now shipping with FHIR specifications
2. Extensive stakeholder feedback via RFIs across multiple payment rules, highlighting support for dQMs but concerns about conversion complexity, validation, and timelines
3. Tooling under active development, including MADiE for authoring/testing and open-source utilities like DedupliFHIR for deduplication and record matching, plus updated resources on the eCQI Resource Center

Transition strategy

CMS emphasized this won’t be a “light-switch” moment:

1. Optional FHIR dQM reporting alongside QDM‑based eCQMs, giving organizations a runway to test FHIR pipelines and workflows
2. Mandatory FHIR dQM reporting once standards, testing, and infrastructure are ready across programs
3. A North Star future where quality is measured from digital, interoperable data flows, producing reliable, cross‑program, cross‑payer metrics

USCDI Quality: A shared backbone  

ASTP’s work on USCDI Quality was described as the harmonization layer quality programs have lacked.

1. It captures data needs for quality reporting that sit outside core USCDI, then aligns them into a common, multi‑program list.
2. It’s designed to support CMS’ dQM strategy, HRSA’s UDS modernization, and other HHS efforts without duplicating QI Core or USCDI.
3. Evaluation criteria include coverage of CMS eCQMs, consistency with USCDI v3 and US Core, and inclusion of non‑QI Core elements that are still necessary for quality reporting.

For vendors and ACOs, USCDI Quality is the schema we need to design against.

MSSP on FHIR: What it takes in the real world  

One ACO leader shared a candid view of what at-scale FHIR‑based MSSP quality reporting looks like:

1. Two ACO entities with roughly 90,000 reportable lives, 29 practices, 11 EHRs, and 14 FHIR pipelines
2. A regulatory shift eliminating sample‑based reporting and requiring full‑population reporting across all assigned lives
3. An architecture built on cloud data services and FHIR, with steps to:
   1. Load CMS‑eligible populations into a master member data mart.
   2. Retrieve clinical data via regulated APIs, spreadsheets, and extractions from all network EHRs.
   3. De‑identify, deduplicate, cleanse, and validate data before measure calculation.
   4. Report results back to CMS via FHIR APIs.

They described 3 categories of progress:

1. Working: Connectivity across systems, FHIR‑based calculation using unflattened data, and standard workflows capturing the right discrete fields when clinicians used them consistently

2. “Kinda working”: Reaching the right contacts at small and specialty EHRs, handling de‑identification metadata, and dealing with unstructured or binary data built for legacy eCQMs

3. Crashing: Bulk API calls from on‑prem EHRs and misalignments between historical scoring/benchmarks and new FHIR‑based definitions

Their request of peers and vendors echoed Dr. Oz’s framing: give organizations standards, time, and reusable frameworks. That would allow investments in FHIR‑based quality infrastructure to lower long‑term costs instead of just adding another layer.

AI, LLMs, and FHIR-native workflows  

AI was everywhere at HIMSS26. The most useful conversations focused on how AI and LLMs can make digital quality on FHIR viable and safe at scale.

Ambient AI and documentation quality    

One large health system shared results from a comprehensive ambient AI evaluation and rollout.

They completed a 6-month, multi-vendor trial with 300 providers across specialties. It delivered: 

• • About 30% reduction in documentation time and nearly 50% reduction in “pajama time”
    • Roughly 80% utilization across 40 specialties, with psychiatry, family medicine, and internal medicine among the highest adopters
    • A 32% increase in patient face time, plus measurable improvements in note consistency and coding accuracy

Dr. Oz’s emphasis on using AI first for mundane friction points — drafting, summarizing, and capturing meetings — aligned with this story. AI is used as workflow infrastructure.

LLMs as ETL for mCODE and FHIR

A hands‑on LLM session went deep on using LLMs to convert unstructured oncology notes into mCODE‑compliant, FHIR‑ready data.

• • Use retrieval‑augmented generation to anchor the model in authoritative specs (e.g., the mCODE data dictionary).
  • Have it cite those documents when classifying disease progression or other clinical states.
  • Design pipelines with:
  • PII masking before the LLM call.
  • An LLM gateway and human review before data lands in registries or quality warehouses.
  • Monitoring for drift and bias using golden datasets and diverse patient cohorts.
  • Choose the smallest model that safely does the job to control cost and reduce risk.

For dQMs, that’s a practical pattern for bridging the gap between narrative clinical practice and the structured, coded reality that FHIR and dQMs require.

Value-based care ROI: Why digital quality matters

• Use retrieval‑augmented generation to anchor the model in authoritative specs (e.g., the mCODE data dictionary).
• Have it cite those documents when classifying disease progression or other clinical states.
• Design pipelines with:
  • PII masking before the LLM call.
  • An LLM gateway and human review before data lands in registries or quality warehouses.
  • Monitoring for drift and bias using golden datasets and diverse patient cohorts.
• Choose the smallest model that safely does the job to control cost and reduce risk.

For dQMs, that’s a practical pattern for bridging the gap between narrative clinical practice and the structured, coded reality that FHIR and dQMs require.

What this means for Healthmonix and our clients

Filtering HIMSS26 through my lens as a healthcare analytics and quality leader, a few priorities crystallized:

• Be dQM/FHIR‑first. With 70 measures already converted and a clear roadmap toward mandatory FHIR dQMs, solutions need to treat FHIR‑based quality as the default across MIPS, MVPs, MSSP, and emerging CMS models.
• Double-down on data quality and validation. The MSSP example illustrated how success hinges less on “having FHIR” and more on deduplication, validation, and alignment with evolving measure specs. We can provide real value by making digital quality trustworthy, not just technically possible.
• Integrate AI where it improves digital quality. Ambient documentation and LLM‑based ETL should be evaluated not only for time savings, but also for their impact on completeness, accuracy, and timeliness of FHIR‑coded data feeding dQMs.
• Align narrative and measurement. Dr. Oz’s framing — technology as a deflationary, access‑expanding force — matches the CMS dQM North Star and the practical wins shared in MSSP and VBC sessions. Our roadmaps and client conversations should connect those dots.
  
  I’ll unpack implications for VBC programs like MIPS, MVPs, and MSSP in future posts. If you’re leading a quality, analytics, or population health team, what aspect of this shift — CMS’ dQM roadmap, FHIR infrastructure, or AIenabled workflows — feels most urgent to you?  

Want to know how you can succeed in value-based care? 

Ask our clients. Or better yet, ask us. We’ll show you what we’ve done, what we’re doing, and what we can do with you. 

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