Inferensys

Glossary

Fast Healthcare Interoperability Resources (FHIR)

A modern RESTful API standard developed by HL7 that structures healthcare data into discrete, web-friendly resources to enable seamless, developer-friendly interoperability across electronic health records.
Data scientist building training data pipeline on laptop, data preprocessing visible, technical workspace.
INTEROPERABILITY STANDARD

What is Fast Healthcare Interoperability Resources (FHIR)?

A modern, RESTful API standard developed by HL7 that structures healthcare data into discrete, web-friendly resources to enable seamless, developer-friendly interoperability across electronic health records.

Fast Healthcare Interoperability Resources (FHIR) is a next-generation interoperability standard created by HL7 that defines a set of modular, web-based Resources representing granular clinical and administrative concepts like Patient, Observation, or MedicationRequest. By combining the best features of previous standards with modern web technologies, FHIR leverages a RESTful API architecture using standard HTTP protocols, JSON, and XML formats to enable stateless, real-time data exchange between disparate healthcare systems.

The standard is built around the concept of discrete, addressable resources that can be combined to solve complex clinical problems. FHIR supports multiple interoperability paradigms, including a RESTful API for direct access, a messaging paradigm for event-driven workflows, and a document paradigm for persistence. Its design prioritizes rapid implementation, with the SMART on FHIR framework adding an OAuth 2.0-based security layer to enable substitutable, third-party applications to run seamlessly within any compliant electronic health record system.

MODERN INTEROPERABILITY

Key Features of FHIR

Fast Healthcare Interoperability Resources (FHIR) revolutionizes healthcare data exchange by combining the best features of previous standards with modern web technologies. Its component-based architecture enables granular data access and seamless integration.

FHIR FUNDAMENTALS

Frequently Asked Questions

Clear, technical answers to the most common questions about the Fast Healthcare Interoperability Resources standard, its mechanisms, and its role in modern health IT ecosystems.

Fast Healthcare Interoperability Resources (FHIR) is a modern, RESTful API standard developed by HL7 that structures healthcare data into discrete, web-friendly "resources" to enable seamless, developer-friendly interoperability across electronic health records. FHIR works by defining a set of modular, granular data components—such as Patient, Observation, MedicationRequest, and Condition—that represent common clinical and administrative entities. Each resource is uniquely addressable via a URL and can be manipulated using standard HTTP verbs (GET, POST, PUT, DELETE). Data is serialized in either JSON, XML, or RDF, with JSON being the most common for web and mobile application development. This architectural approach allows developers to build applications that can query a patient's problem list or submit a lab result without needing to understand the monolithic, legacy HL7 v2 or CDA document paradigms. The standard is organized around a maturity model (FMM) and normative content, ensuring that production-grade stability exists for core resources while allowing innovation in emerging areas.

STANDARDS COMPARISON

FHIR vs. HL7 v2 vs. CDA

A technical comparison of the three primary HL7 healthcare data exchange standards across architectural style, data format, and implementation complexity.

FeatureFHIRHL7 v2CDA

Architectural Style

RESTful API

Event-driven messaging

Document exchange

Data Format

JSON, XML, RDF

Pipe-and-hat delimited

XML

Transport Protocol

HTTP/HTTPS

MLLP over TCP/IP

HTTP, SMTP, file transfer

Granularity

Discrete resources

Segments and fields

Whole clinical documents

Modern Web Support

Human Readable

State Management

Stateless

Stateful (acknowledgments)

Stateless

Implementation Complexity

Moderate

High

High

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.