Summary

HL7 International embraces artificial intelligence as a powerful enabling technology for health data interoperability. Rather than restricting AI use of our standards, we are actively engineering our processes and content to be AI-ready. We believe that AI systems capable of understanding FHIR® and other HL7 standards ultimately serve our mission to improve health and well-being through better data exchange.

Our Perspective: Open Standards = Open Possibilities

HL7 International's mission is to create and promote the adoption of innovative interoperability standards that improve health and well-being while fostering a diverse and inclusive global community. We pursue this so that people everywhere can live in optimal health. Because open standards enable new digital freedoms, we want our standards to be as widely used and as deeply understood as possible. By humans and machines alike. Standards demonstrate the network effect — they become more valuable the more widely they are adopted.

Some standards development organizations (SDOs) restrict or prohibit the use of their intellectual property in AI training and inference. HL7 International takes a different approach. We view AI systems as legitimate and valuable consumers of our standards — partners in the global project of interoperability rather than threats to our business model.

Our licensing choices reflect this view. HL7 publishes our FHIR platform standard and related specifications under the Creative Commons Zero (CC0) public domain dedication, the most permissive licensing approach available. CC0 reflects a deliberate strategic commitment: by removing all licensing barriers, we maximize adoption potential and minimize friction for all implementers, including AI developers and the AI systems they build.

FHIR in the Wild: Standards-Aware AI Systems

HL7's flagship FHIR standard is unique not only as a modern API standard for healthcare, with innovations in open standards development, but also in its structure and publication format. FHIR reflects a fundamentally developer-native approach, setting it apart from most other health IT standards. Rather than static, paywalled PDFs, FHIR is published as a fully navigable website where every resource, data type, and operation has its own structured page — paired with computable, machine-readable definitions in JSON and XML that allow tools to programmatically interrogate the standard itself. Further, the platform specification and derivative implementation guides, along with their computable parts, are distributed as versioned NPM packages via a public registry, enabling reproducible builds and automated dependency resolution familiar to any software developer.

Additionally, we have embraced the expectation of co-developing and testing an open-source (typically licensed under Apache 2.0) reference implementation software alongside the standard. This ensures that the published standard is actually implementable, and gives implementers working software to learn from, test their code against, or even incorporate into their products.

Because FHIR is freely available, extensively documented online, and has corresponding open source reference software code bases on public platforms like Github, FHIR is already embedded in the training corpora of the world's leading large language models. Developers today can ask an AI assistant to generate a FHIR patient resource, write a FHIR search query, or explain the semantics of a SMART on FHIR authorization flow — and receive accurate, useful answers. This is a natural consequence of our open-first standards strategy at work.

In short, we see the standard not as a document to be read, but as an open platform to be built upon.

Today we honor the life and legacy of Clement J. McDonald, MD, who passed away on May 21, 2026.

Known worldwide as the one and only "Clem", he was a luminary in the field of biomedical informatics. Clem was a titan, world renowned for his innovations in electronic medical records, clinical decision support, multi-institution health data exchange, and especially in the global standards that enable computers to share and understand health data.

As a co-founder and life-long member of HL7 International, Clem's vision for interoperability enabled by consensus standards is encoded in our DNA.

A Pioneering Career

Clem grew up on Chicago's West Side, graduated from Notre Dame in three years, and attended the University of Illinois College of Medicine. He completed his internal medicine residency at Cook County Hospital and the University of Wisconsin, after which he joined Indiana University and the Regenstrief Institute in 1972. There he built one of the world's first electronic medical record systems and published the first randomized controlled trials demonstrating that computerized clinical decision support could improve care.

He rose through the academic ranks at the Indiana University School of Medicine to become Distinguished Professor of Medicine and the Sam Regenstrief Professor of Medical Informatics, and served as Director of the Regenstrief Institute from 1990 to 2006. He also developed the Indiana Network for Patient Care, a groundbreaking statewide health information exchange. Throughout this time, he also practiced primary care internal medicine in a safety-net clinic for more than 25 years (that ran on the EMR he created).

In 2004, Clem joined the U.S. National Library of Medicine where he first served as Director of the Lister Hill National Center for Biomedical Communications and Scientific Director of its intramural research program, and later serving as Chief Health Data Standards Officer — a position he held until his passing.

If you’ve spent any time in healthcare over the past year, you’ve probably felt it: the energy, the urgency, the curiosity around AI. Everywhere you look, teams are experimenting with new models, exploring new use cases and imagining what care could look like if we finally had the right data in the right place at the right time.

But there’s also a shared realization emerging across the industry that AI can’t transform healthcare unless the data behind it is trustworthy, connected and interoperable.

That’s why HL7 International launched the 2026 HL7 AI Challenge, now officially open for submissions through June 30, 2026.

Why an HL7 AI Challenge?

Last year’s inaugural Challenge showed us something powerful: when innovators build on HL7 standards, they can move faster, scale more easily and create solutions that actually work in the messy, real‑world environments where healthcare happens.

Healthcare organizations around the world are experimenting with AI, but many face the same barriers: fragmented data, inconsistent formats, and limited ability to integrate AI outputs into clinical systems. HL7’s standards are designed to address these challenges, making them a natural foundation for safe and effective AI adoption.

The HL7 AI Challenge aims to:

• Encourage innovation grounded in open, widely adopted standards
• Demonstrate how structured, interoperable data improves AI performance
• Highlight real‑world solutions that can scale across organizations and borders

New implementation community builds on global collaboration to improve
real-time device data exchange for AI-enabled care

Imagine a patient in an intensive care unit, monitored by a dozen devices generating streams of critical data every second.  From operating rooms and ICUs to ambulatory clinics and patient homes, clinicians and care teams rely on a growing ecosystem of medical and personal health devices. Now imagine that data is siloed, unable to flow into the EHR and unreachable by the analytics platform that might detect a dangerous trend before a clinician does.

This fragmentation is a well-known challenge in healthcare IT. On March 5, 2026, HL7 International took a major step toward solving it with the launch of the Caliper FHIR® Accelerator, a new implementation community dedicated to improving how data from medical and personal health devices is exchanged, integrated and used across healthcare systems. 

Why Caliper, and Why Now?

Caliper builds on HL7’s 2025 work with founding members to define a collaborative community focused on device interoperability. The need is clear: healthcare organizations are generating more high‑frequency device data than ever, but too often this information cannot flow cleanly into EHRs, analytics platforms or AI‑driven applications.

By leveraging HL7 FHIR alongside established device communication frameworks, Caliper aims to create a scalable, standards‑based foundation for real‑time device data integration. The goal is simple but transformative: ensure that data from critical care equipment and patient‑facing technologies can be shared consistently, reliably and safely.

“Healthcare systems are entering a new phase where access to high-quality, real-time data is essential to safely deploying advanced analytics and AI,” said Rachel Dunscombe, CEO of HL7 International. “The Caliper Accelerator represents an important step forward in ensuring that device-generated data, whether from critical care equipment or patient-facing technologies, can be shared and used consistently across care environments worldwide. This kind of foundational interoperability is critical to improving both clinical outcomes and operational resilience.”

HL7 International has announced the appointment of Patrick (Pat) McGinn, MBA, CAE, as its new Chief Operating Officer (COO), strengthening the organization’s leadership team during a pivotal period of transition.

Pat brings more than 20 years of executive leadership and operational experience across professional associations. He joins HL7 from the American Society of Civil Engineers (ASCE), where he most recently served as Director of the Utility Engineering & Surveying Institute, leading strategic planning, program development, education and standards initiatives, and member engagement.

In his role as COO, Pat will oversee HL7’s operational functions, including technology, membership services, and internal operations, while supporting the organization’s global mission to enable safe, scalable health data exchange.

Join Us for the January Da Vinci Project Community Roundtable on January 28, 2026, from 4 - 5:30 pm ET

As we enter 2026, the healthcare industry continues to move from planning to active implementation of standardized data exchange. Because the first phase of the CMS-0057-F Interoperability and Prior Authorization Final Rule took effect this month, the stakes for technical and operational alignment have never been higher. The HL7 Da Vinci Project Community Roundtable, taking place on January 28, 2026, from 4 – 5:30 pm. ET,  will provide insights and resources to navigate this pivotal year.

What You'll Learn

Our previous sessions focused on how using Da Vinci burden reduction and payer data exchange IGs are transforming healthcare and how best to meet prior authorization regulatory requirements. This January session builds on that foundation by outlining key use cases, IGs, and the progress made in standardized data exchange.

Gain valuable knowledge on operational enhancements and resources to optimize your planning for smoother HL7 FHIR implementations, including implementation guides referenced in federal regulations. Additionally, hear about Da Vinci's 2026 priorities and the HL7 Da Vinci Community Champions program. 

HL7 has announced the appointment of Professor Rachel Dunscombe as its new Chief Executive Officer effective January 19, marking the start of the organization’s next phase of growth in global health data interoperability. She succeeds Charles Jaffe, MD, PhD, who concludes nearly two decades of leadership at HL7.

Rachel brings extensive global experience in digital health, interoperability and standards-based transformation. Prior to joining HL7, she served as CEO of openEHR International, where she helped accelerate worldwide adoption of open health data standards and strengthened collaboration among governments, standards bodies and healthcare organizations.

Her career includes senior roles in healthcare delivery, national digital strategy, executive education and leadership in international standards communities. She has been recognized for her ability to unite diverse stakeholders and advance practical implementation of health data standards at scale.

 Overall Winner

The  Integrated Medical Management and Educational Gateway (IMMEG) Venous Management System (I-VMS) is an AI-enabled, standards-based platform that projects a vectorized atlas of the deep thoracic venous system onto routine chest radiographs. Using deep-learning landmark detection and HL7 FHIR®/DICOM interoperability, the system lets clinicians visualize the catheter trajectory and tip position in patient-specific anatomy, record planned versus actual placement, and build a reusable, longitudinal venous access record across organizations. The project was developed for Vanguard with support from Xzyos.ai. 

Clinical Problem and Context 

Central venous access is essential for chemotherapy, parenteral nutrition, dialysis and critical care, yet malposition and related complications—venous injury, thrombosis, infection, and device dysfunction—remain common and costly. Post-procedure assessment usually relies on plain chest X-rays, which do not directly visualize venous structures. As a result, clinicians infer anatomy indirectly; documentation is inconsistent; and comparing procedures over time is difficult. There is no consolidated, spatially normalized record of a patient’s venous history to guide future decisions.

Core Innovation

 I-VMS predicts anatomical landmarks (e.g., carina, first thoracic vertebra T1, lateral edge of the right rib) on a radiograph with a modified DenseNet121 model implemented in MONAI. These coordinates establish a patient-specific basis for an affine transformation that overlays a standardized, vector-based venous atlas onto the image. Clinicians can accept or adjust landmarks and annotate intended and actual entry and tip positions. Because annotations are stored in a normalized coordinate space, results are comparable across encounters and over time, enabling longitudinal analysis and population-level learning.

Reflections from the ADAPT Chief AI Officers on Innovation Panel Discussion, November 2025

After decades of working toward seamless health data interoperability, we find ourselves at another pivotal moment. The rapid adoption of Artificial Intelligence (AI) in healthcare presents us with a familiar challenge wearing a new face: how do we ensure these powerful new tools work together transparently, accountably, and in the service of better health for people everywhere?

At a recent ADAPT conference panel, I had the opportunity to reflect on what our interoperability journey can teach us as we venture into standardizing intelligence, not just data. Here are some key insights from that conversation.

The Journey Continues

First, a grounding perspective: this is a journey, not a destination. Despite all the progress we've made in healthcare interoperability, too often, people still move faster and further than their health information. The ability for any digital tool—including AI—to help people make better health decisions is always limited by the scope of data in its purview and its capability to make sense of it.

Even the most powerful AI we can imagine must overcome the same boundaries we've always faced: technical, organizational, business, and jurisdictional barriers that prevent us from seeing the complete picture of health information relevant for individuals or populations.

However, HL7's decade-plus journey with Fast Health Interoperability Resources (FHIR® ) has taught us something crucial: open standards are a potent fuel for innovation. The vibrant, open, collaborative community around FHIR wasn't just a nice byproduct—it was the key force that created a well-tuned specification and enabled it to flourish in the marketplace.

Open standards level the playing field, reduce barriers to participation, and free organizations from proprietary formats. They unlock new connectivity, preserve data sovereignty, and most fundamentally, enable new digital freedoms. As we approach AI standardization, maintaining this commitment to openness isn't guaranteed, but it's the future we're fighting for.

 Winner of the Transformative Impact in Healthcare Award

What do you do when the people who taught you everything start to forget? My father is fighting Parkinson's and early dementia. My mother manages diabetes and congestive heart failure. My three siblings and I are a team — a family armed with love but disarmed by the chaos of a dozen medications, forgotten instructions, and missed questions. We were losing the battle against complexity, and that feeling is where this story truly begins.

 The seed for what would become Let's Talk Doc was planted six or seven years ago. My friend and partner, Diane Nguyen, and I saw the cracks in the system through our own eyes. I saw it in my parents' home, and she saw it as an immigrant facing the silent fear that a single misunderstood word on a form could alter her family's care. We tried to build something back then, a small solution born from our shared frustrations. But the technology wasn't ready. The idea was a spark, but we couldn't yet build the engine.  

Years passed. Then, earlier this year, Diane reached out. The world had changed. Technology had finally caught up to our ambition. "It's time," she said. "Let's try again."

 This is not a business venture for us. It’s a mission.