Background

The LOINC Ontology developed through a collaborative initiative between the Regenstrief Institute and SNOMED International was established under a cooperative agreement signed in October 2022 which built upon previous agreements between the two organizations. This partnership aims to reduce duplication between the two terminologies and enhance their interoperability, enabling stakeholders to use LOINC and SNOMED CT together more effectively. This initiative builds on the strengths of both terminologies, aiming to streamline clinical data exchange and enhance the utility of standardized healthcare information.

A key deliverable of this collaboration is an extension that integrates LOINC’s granular laboratory observables into SNOMED CT’s framework, increasing LOINC’s computability in a SNOMED-compatible structure.  By aligning these terminologies, the extension supports data harmonization, advanced analytics, and improved patient care. The first production version of the LOINC ontology, released in March 2025 focuses on laboratory observables, covering terms representing at least 75% of test volume based on LOINC’s Top 20,000 usage rankings, derived from U.S. data.

More information about this project can be found at https://loincsnomed.org/ along with downloadable content and the LOINC Ontology Browser.

Objective

The harmonization of LOINC and SNOMED CT helps tackle inconsistent coding, interoperability issues, and data silos that make it difficult to share and use healthcare information effectively. Without this alignment, lab results and clinical data are often recorded in different formats, leading to errors, redundant mappings, and gaps in patient care and research.

The overall clinical and practical objective of the LOINC Ontology and the harmonization of LOINC and SNOMED CT is to support a more interoperable, standardized, and semantically precise healthcare data ecosystem. This harmonization effort aims to bridge the gap between structured clinical terminologies and laboratory/observation data to enhance patient care, research, and data exchange across healthcare system

The objective of the SNOMED CT Implementation Guide for the LOINC Ontology is to describe the structure and content of the LOINC Extension for SNOMED CT and provide practical guidance for its application. The guide explains how the integration of LOINC and SNOMED CT works in both directions, supporting standardized representation of observations in SNOMED CT while also enhancing the clinical utility of LOINC. 

Scope

The scope of this guide is to provide a foundational framework for understanding and implementing the LOINC Extension to SNOMED CT, addressing both conceptual and practical aspects of its use.

As a dynamic document, its content will evolve in alignment with updates to the extension, ensuring it remains current and relevant. This adaptability allows the guide to incorporate new features, use cases, and advancements as the extension develops, supporting users in effectively applying the latest capabilities.

By remaining flexible and responsive, the guide ensures ongoing alignment with the evolving needs of healthcare interoperability and data standardization.

This guide outlines the foundational framework for understanding and implementing the LOINC Extension for SNOMED CT, focusing on both conceptual and practical aspects.

The document will evolve alongside updates to the extension, incorporating new features and use cases to ensure relevance. This adaptability supports users in leveraging the latest capabilities for healthcare data standardization and interoperability.

Audience

This guide is designed for a diverse range of stakeholders involved in the implementation and application of the LOINC Extension to SNOMED CT. It provides practical insights and best practices tailored to specific user groups, helping them understand and utilize the extension effectively in their respective domains:

• SNOMED International Members seeking clear guidance and uniform best practices for implementing and documenting the integration of LOINC and SNOMED CT, ensuring consistent application across healthcare systems.
• Clinicians interested in understanding how the integration can enhance clinical data collection, improve patient care workflows, and support comprehensive clinical documentation.
• Information Managers responsible for incorporating SNOMED CT into health information models and workflows, aiming to optimize data standardization and interoperability in laboratory and clinical settings.
• Software Developers focused on embedding SNOMED CT and LOINC integration into healthcare applications, ensuring robust support for interoperability and seamless data exchange.
• Researchers normalizing data to support population analytics within a given organization and across multiple organizations as part of multisite clinical trials.
• Healthcare Organizations in various scenarios:
  • Those evaluating coding systems for capturing laboratory data.
  • Organizations already using SNOMED CT but needing to support or report using LOINC.
  • Current LOINC users considering migration to SNOMED CT.

  • Organizations with established systems using either SNOMED CT or LOINC that now seek to enhance data exchange with other entities by supporting both terminologies.

     

Attribution

This SNOMED CT Implementation Guide and the development of the LOINC Extension to SNOMED CT are the result of a collaborative effort between key contributors from SNOMED International and the Regenstrief Institute. Their expertise and dedication have been critical in establishing the cooperative agreement and advancing the extension.

Guide Overview

This SNOMED CT Implementation Guide is specifically designed to support the implementation of the LOINC Ontology within SNOMED CT. The guide provides a structured framework for understanding the integration of LOINC’s granular laboratory observables into SNOMED CT’s Logical design. It is organized into five main chapters:

• Chapter 1: Introduction - This chapter provides an overview of the LOINC Ontology, including its development through the collaboration between SNOMED International and the Regenstrief Institute. It outlines the objectives, scope, and intended audience of this guide.
• Chapter 2: What is LOINC? - This chapter provides a foundational understanding of LOINC, detailing its purpose, structure, and role in healthcare data standardization. It explains how LOINC supports the representation of laboratory tests, clinical observations, and other coded health data. Additionally, the chapter introduces the LOINC Extension within SNOMED CT, outlining how it enhances interoperability and aligns with broader clinical terminology standards.
• Chapter 3: Clinical Use Case - This chapter describes key use cases that demonstrate the value of integrating LOINC into SNOMED CT. It covers scenarios where the LOINC Extension enhances data interoperability, improves laboratory result standardization, and facilitates clinical decision support.
• Chapter 4: Content Development Principles - This chapter explains how SNOMED CT incorporates LOINC content, detailing the structure and organization of LOINC concepts within SNOMED CT. It describes the approach used for modeling LOINC codes and representing this in compliance with the SNOMED CT Extension mechanism.
• Chapter 5: Information Models and Terminology Binding - This chapter explores the integration of the LOINC Ontology with various health information models. It provides an in-depth discussion on logical modeling, terminology binding strategies, and best practices for aligning LOINC observables with information model structures. Additionally, it examines the application of HL7 FHIR for laboratory data representation, covering FHIR resources used for ordering laboratory tests and structuring laboratory results in a standardized manner.
• Chapter 6: Technical Application - This chapter provides technical implementation guidance, including best practices for integrating the LOINC Extension into SNOMED CT-enabled systems. It includes practical strategies for adoption, system configuration, and real-world application examples.

Review

We encourage and welcome feedback from all readers to ensure this guide remains accurate, relevant, and useful. The review process is an ongoing effort to engage the community in improving the content. A feedback button is provided on each page of the guide, allowing readers to share their comments and suggestions directly related to the content.

Comments or inquiries that are not relevant as feedback on specific page content should be directed to info@snomed.org for further assistance.

All feedback will be carefully reviewed, and updates will be incorporated as appropriate. The guide will be revised and updated with every new release of the LOINC Extension to SNOMED CT, ensuring it reflects the latest developments and enhancements. Your input is invaluable in helping us maintain a resource that meets the needs of its users and supports the effective implementation of this extension.