HL7 vs. FHIR: The Difference, and Why It Matters

In healthcare, data interoperability is a foundational challenge.

Healthcare data exchange standards like HL7 (Health Level Seven) and FHIR (Fast Healthcare Interoperability Resources) define how healthcare systems exchange patient information. While both serve the same fundamental goal in facilitating communication between different software systems, they are distinct approaches in how data is formatted, transmitted, and used.

HL7 is a longstanding framework designed for transmitting healthcare data between systems, most commonly in hospital or lab environments. It’s highly flexible, but that flexibility can lead to inconsistent implementations and limited scalability. FHIR, by contrast, is a newer standard that leverages modern web technologies like RESTful APIs and JSON. It’s built for interoperability at scale, with a focus on consistency, modularity, and easier developer adoption.

Understanding HL7 and FHIR is critical for any organization managing healthcare data. As the industry evolves toward more structured, interoperable systems, the ability to integrate and act on data—accurately and securely—directly impacts care quality, workflow efficiency, and platform extensibility.

What Is HL7?

Health Level Seven (HL7) is a set of international standards that define how clinical and administrative data is exchanged between healthcare software systems.

Developed by the non-profit organization Health Level Seven International in 1987, these standards have become foundational to healthcare interoperability, allowing disparate systems to communicate and share patient data consistently.

Overview of HL7 Standards

HL7 includes multiple standards, each developed to serve specific interoperability needs across healthcare organizations.

The most widely adopted versions are HL7 Version 2 (V2), Version 3 (V3), and Clinical Document Architecture (CDA).

• HL7 V2, introduced in 1989, is the most widely implemented. It uses a pipe-delimited format and defines messages for tasks like patient admission, lab orders, and result reporting. Its success lies in its simplicity and adaptability, but this same adaptability has led to inconsistent implementations across systems.
• HL7 V3 was an attempt to bring greater consistency and structure through the use of XML. However, its complexity hindered adoption, and it never replaced V2 as the dominant standard.
• CDA is HL7’s approach to standardizing clinical documents. Unlike V2’s message-based format, CDA defines persistent, readable documents for clinical use, such as discharge summaries or progress notes.

The Role and Challenges of HL7 V2

HL7 V2 remains the dominant protocol for healthcare messaging, with an estimated 95%.) of healthcare organizations still relying on it.

Its strength lies in its customizability. Implementers can adapt message structures to meet local or organizational requirements. This flexibility supports a wide range of use cases, from lab result reporting to admission and discharge notifications.

However, this same flexibility introduces complexity. Because implementations often deviate from one another, interoperability requires manual mapping and interface customization. This fragmentation—commonly referred to as the HL7 V2 problem—creates long-term maintenance burdens and limits scalable integration across systems.

Clinical Document Architecture

Clinical Document Architecture (CDA) is HL7’s framework for standardizing the structure and semantics of clinical documents. Based on XML, CDA supports both machine readability and human interpretation, enabling healthcare organizations to exchange structured patient data while preserving the context and meaning of the original document.

The standard supports varying levels of structure, from largely narrative documents to fully coded entries. This flexibility allows organizations to transition gradually from free-form clinical notes toward fully structured, computable data. A common CDA implementation is the Continuity of Care Document (CCD), which provides a standardized snapshot of a patient’s clinical history that can be exchanged between systems.

What Is FHIR?

FHIR (Fast Healthcare Interoperability Resources) is a modern interoperability standard developed by HL7 to support secure, consistent, and modular data exchange across healthcare systems.

Designed around widely adopted web technologies, FHIR applies RESTful principles and structured data models to make healthcare information more accessible, both for systems and developers.

FHIR emerged in response to the limitations of earlier HL7 standards, which often relied on proprietary formats and inconsistent implementations. By contrast, FHIR enables healthcare organizations to treat clinical and administrative data as individual “resources” (standardized, discrete objects such as Patient, Medication, or Observation) that can be accessed using familiar web protocols.

FHIR’s Resource-Based Approach

FHIR organizes health data into over 140 well-defined resources, each with a consistent structure.

For example, a Patient resource always contains core demographic fields such as name, birthdate, and gender, with optional extensions that allow for customization without sacrificing compatibility.

Resources can reference each other to create structured, linked data sets. For example, an Observation may reference both a Patient and an Encounter. This structure supports both granular access (e.g., fetching one lab result) and bundled transactions (e.g., submitting an encounter record), giving developers full control over data scope and performance.

This model offers a significant advantage: it decouples application logic from rigid messaging formats, allowing systems to retrieve and act on only the data they need, supporting both efficiency and scalability.

Canvas and FHIR

Canvas Medical uses FHIR as a foundational standard for structured healthcare data exchange, ensuring compatibility with modern interoperability requirements. With tools like the SDK, FHIR-based APIs, and the Canvas Developer Sandbox, care delivery organizations can adapt and extend their EMR environment to fit specific operational models and automation goals.

HL7 vs. FHIR: A Comparison

Comparing HL7 and FHIR reveals more than just a difference in technical specifications. These standards reflect fundamentally different models for how healthcare data should be structured, exchanged, and managed, particularly as care delivery becomes more distributed and software-driven.

Architecture

Traditional HL7 standards, especially HL7 V2, rely on message-based communication, where entire datasets are sent as single payloads. Each message type has a predefined format, often customized by each organization.

In contrast, FHIR is structured around discrete, modular resources. Each resource represents a specific clinical or administrative concept—such as Patient, Encounter, or Observation—that can be independently accessed, updated, or linked using standard web protocols.

This architecture supports more flexible data workflows, fine-grained access, and faster iteration cycles.

Data Format and Interoperability

HL7 V2 uses a proprietary, pipe-delimited format that requires specialized parsers and domain-specific expertise. Variability across implementations often necessitates significant interface mapping, custom tooling, and ongoing maintenance.

FHIR’s design around discrete, granular resources allows systems to exchange only the relevant pieces of information instead of an entire patient record. This resource-based approach can reduce payload size in certain contexts, particularly for mobile and cloud-native applications, and enables more targeted data sharing.

Implementation Complexity and Developer Experience

Implementing HL7 V2 often requires long timelines, custom interfaces, and extensive site-specific configurations. The lack of standard transport protocols also means organizations must separately implement and secure the delivery mechanism.

FHIR simplifies this process. Built on RESTful web services, FHIR supports existing security protocols like OAuth2 and integrates easily with modern development stacks. Its modular design allows phased adoption—teams can begin with core resources and extend capabilities over time without refactoring the entire system.

Use Cases Across Outpatient Care Organizations

HL7 V2 continues to be the standard for high-throughput scenarios like lab interfaces, pharmacy integrations, and ADT (admission, discharge, transfer) feeds. Its longevity and widespread adoption make it a dependable choice for outpatient care organizations with existing infrastructure.

Some of the key features of HL7 V2 for outpatient services include:

• Patient Identification (PID) for transmitting patient demographics
• Event-driven updates (following patient registration, available lab results)
• Messaging if outpatient is transferred to inpatient care, or vice versa

FHIR, however, offers clear advantages in outpatient use cases that demand adaptability and developer access. These include:

• Specialty-specific EMR modules
• Clinical decision support tools
• Population health and outcomes analytics
• Data sharing across external systems and partners

Canvas Medical’s platform, which supports diverse care models ranging from primary care to behavioral health, exemplifies how FHIR enables scalable, specialty-specific application development while maintaining interoperability and governance.

HL7 vs. FHIR: Similarities

Although HL7 and FHIR differ architecturally, they still share some foundational objectives.

Both standards were developed by Health Level Seven International (HL7) to address the long-standing challenge of interoperability in healthcare systems. Their shared mission: to enable safe, accurate, and structured exchange of patient data across fragmented environments.

Understanding these commonalities reframes the question from HL7 vs. FHIR to HL7 and FHIR. Many healthcare organizations operate in hybrid environments where both standards coexist, each used for distinct purposes based on system architecture, resource constraints, and clinical needs.

Shared Goals in Healthcare Data Exchange

At their core, HL7 V2 and FHIR are built to support secure and consistent transmission of clinical information between systems. Whether handling lab results, admission and discharge notifications, or medication updates, both standards aim to reduce errors. They also maintain data fidelity and support care coordination across facilities.

They also rely on shared clinical vocabularies (such as LOINC for lab tests, SNOMED CT for clinical terminology, and ICD for diagnoses), which ensures semantic alignment even when systems use different syntaxes. This common foundation allows for partial interoperability even in heterogeneous data environments.

Segments and Resources

While HL7 V2 organizes data into segments (such as the PID segment for patient identification), FHIR replaces this model with modular resources, such as the Patient resource. Both serve to encapsulate clinical data elements, including names, identifiers, addresses, and demographic details.

The difference lies in how these elements are defined, accessed, and extended. FHIR resources are designed for clarity, consistency, and programmability. Each resource has a defined schema and can be accessed independently via standard web protocols.

This modular approach improves traceability and reusability while reducing implementation ambiguity and longstanding challenges in HL7 V2.

How We Support Hybrid Environments

It’s rare for organizations to live purely in a FHIR world overnight. Many of our customers operate in hybrid environments where legacy systems still produce HL7 V2 messages.

To bridge this gap, we partner with integration vendors and provide APIs and SDK tools that let customers translate and map HL7 data into Canvas’s FHIR-native data layer. This avoids disruptive rip-and-replace transitions and allows gradual modernization.

Compatibility and Translation Between Standards

Many FHIR resources have direct conceptual mappings to HL7 V2 segments. This backward compatibility allows for translation between the standards, which is critical in real-world deployments where legacy systems persist.

HL7 V2 remains widely adopted, especially in large health systems with embedded infrastructure.

FHIR adoption is growing rapidly in parallel, particularly among digital health platforms and ambulatory care networks building new capabilities.

Transitioning from HL7 to FHIR

Migrating from HL7 V2 and Clinical Document Architecture (CDA) to FHIR is a critical step toward modernizing healthcare data exchange—but it’s not a simple switch. It requires architectural rethinking, technical planning, and stakeholder coordination. The transition must preserve existing workflows while enabling scalable, programmable infrastructure.

Why Migration Is Complex

Many healthcare systems rely heavily on HL7 V2 and CDA. These standards are deeply embedded in clinical and administrative operations, often with years of customization layered on top.

Key challenges include:

• Rigid legacy infrastructure: Many systems lack the flexibility to support RESTful APIs and resource-based models.
• Workflow dependencies: Clinical processes are often tightly coupled to HL7 V2 message structures and batch processing logic.
• Customization debt: Interfaces include non-standard fields and site-specific extensions that complicate translation.
• Staff retraining: Teams must adapt to new tools, standards, and data models.
• Parallel system management: Maintaining V2 while rolling out FHIR demands added resources and oversight.

Strategies for Successful HL7 to FHIR Mapping

Effective data migration requires precision and clinical context. Mapping must go beyond field-to-field equivalence to ensure the preservation of clinical meaning.

Best practices include:

• Audit existing interfaces: Identify message types, data flows, and custom segments in current HL7 V2 and CDA implementations.
• Build robust mapping specifications: Align HL7 segments (e.g., PID, OBX) and CDA sections with FHIR resources (e.g., Patient, Observation), and use FHIR extensions to accommodate unmatched data fields.
• Validate with real clinical scenarios: Test mappings in live clinical workflows—not just technical environments. Involve clinicians in review to ensure usability and accuracy.
• Use a phased approach: Implement translation gateways that support bidirectional conversion between HL7 and FHIR. Start with low-risk data exchange scenarios and expand over time.

Choosing the Right Standard for Your Healthcare Delivery Organization

Choosing between HL7 and FHIR is rarely a binary decision. The right approach depends on your organization’s technical foundation, interoperability requirements, and long-term goals. Most outpatient healthcare organizations will need to support both, applying each standard where it fits best.

When to Use HL7 vs FHIR

HL7 V2 remains the default for many established organizations. It’s well-suited for high-volume, transactional workflows—like lab results and ADT feeds—especially in environments where the infrastructure is already built around it.

FHIR, by contrast, supports more modular, API-driven workflows. Its use of web standards like HTTP, JSON, and OAuth2 makes it ideal for external integrations, such as patient engagement tools or third-party applications. Digital health startups and cloud-native platforms often choose FHIR from the start because it shortens development timelines and enables greater control over system behavior.

The decision often depends on more than just technology. Organizational readiness, regulatory requirements, and vendor ecosystems all shape implementation strategies. Development teams with strong web experience tend to adopt FHIR faster, while systems with entrenched HL7 configurations may prefer a phased migration approach.

The Trajectory of Healthcare Interoperability

FHIR is increasingly shaping the future of healthcare data exchange. Regulatory frameworks—like the 21st Century Cures Act and CMS interoperability rules—are accelerating adoption across payers and healthcare providers.

Major EHR vendors now offer FHIR APIs alongside their traditional HL7 interfaces, and new applications are being built with FHIR as the default for the healthcare industry.

Beyond simple data sharing, FHIR enables structured access to clinical data for analytics, machine learning, and population health reporting. Bulk FHIR operations support scalable data use, while SMART on FHIR applications are powering ecosystems of interoperable tools.