SNOMED in action

EMEA

Advancing Procedure Concept Modeling in SNOMED CT: Standards, Best Practices and Evolution.

As a key component of the Quality Improvement project, the SI Content Team is engaged in the ongoing evaluation and refinement of the 71388002 |Procedure (procedure)| hierarchy. This process aims to standardize and normalize content, ensuring strict compliance with established Editorial Guidance and Policy to uphold terminology structural quality, consistency, accuracy and usability. Furthermore, user feedback is systematically analyzed to optimize modeling patterns, resulting in the creation and dissemination of updated Model Patterns, Editorial Guidance and Templates designed to support SCT authors and implementers.

APAC

A Comprehensive Framework for LOINC and SNOMED CT Implementation in Integrated Laboratory Information Systems

This framework presents a systematic approach for nationwide laboratory data standardisation, demonstrated through the Ministry of Health's (MOH) implementation of LOINC and SNOMED CT. The methodology provides a structured pathway to bridge interoperability gaps across diverse healthcare facilities regardless of existing Laboratory Information Systems (LIS). By establishing the model for terminology integration, this framework transforms fragmented clinical data into cohesive information that enables analytics capabilities, enhances diagnostic precision, and supports evidence-based decision-making applicable to various healthcare environments

EMEA

A Patient Cohorting Tool for Scalable, Transparent, and Reproducible Research

The Patient Cohorting Tool has been developed to enable efficient and reproducible identification of patient populations for research data projects at Barts Health NHS Trust. The tool currently runs on a synthetic database and allows selection by demographic and clinical variables such as gender, ethnicity, age, admission dates, and disorders and findings using SNOMED. The tool is currently in the production phase, and work is underway to connect it to the data warehouse so that it can be applied to real-world datasets. Future work includes extending search criteria to incorporate medication records to extend the search capabilities of the tool. By combining clinical detail with user-friendly options, this tool supports faster, more transparent, and more reproducible research, helping teams focus on insights rather than data wrangling.

Americas

A Prescription for Progress: Expanding the SNOMED CT Canadian Edition with A New Drug Extension

Canada Health Infoway has approved a project to work with Quebec and other interested jurisdictions in Canada to develop a Canadian SNOMED CT drug extension, known as the Canadian Medicines Terminology (CMT). The CMT project is a substantial, multi-year initiative with an objective to deliver long-term value to the healthcare ecosystem. This will be achieved by enabling digital health systems to leverage a pan-Canadian drug data standard, which aligns with SNOMED CT's formal concept model and contains logical definitions for computable queries and clinical decision support. The CMT's design is based directly on SNOMED International's standardized national drug extension model, with additional enhancements to support Canadian-specific requirements. This is an essential enabler for supporting both national and international semantic interoperability. The presentation will describe the CMT's requirements, use cases, schedule, data model, development process, challenges, and achievements.

EMEA

A SNOMED driven clinical dashboard system to support the identification and management of diabetic patients with possible foot disease in a large academic health system in east London

Diabetic patients require constant monitoring and foot screening on hospital admission by clinicians to identify injuries that might lead to infection and ulceration and to prevent progression of foot disease. Studies show that the correct management of diabetic foot patients (DFPs) in the UK could lead to a decrease in amputation rates, hospital admission avoidance, and a drastic reduction in the NHS cost of managing patients with diabetic foot problems1. The aim of this work is to develop and deploy a fully automated, API-based dashboard system that supports management of DFPs at Barts Health NHS Trust.

Our algorithm extracts a list of patients from the Trust Data Warehouse who have been admitted to the hospital. For each patient, the free-text clinical notes created in the past 6 months are identified and processed with CLiX, a commercial natural language processing tool developed by Clinithink which uses post coordinated SNOMED-CT expressions to extract clinical terms from unstructured clinical notes. Demographic, clinical, and automated risk assessment data are presented in a secure and customizable dashboard. Preliminary results at the Royal London Hospital in east London showed that, in one month, the dashboard correctly identified 42 DFPs. Of these, 33 received an immediate clinical intervention and 9 had no recorded intervention. There were 7 incorrect identifications, mostly involving gestational diabetes, congenital conditions or non-diabetic patients. Our data-driven clinical tool continues to identify inpatients with diabetes and shows strong potential for improving early clinical intervention for DFPs.

1 - Guest JF et al., 2018.

EMEA

A Semantic Strategy is a key element of interoperability in electronic healthcare: A proposal for an approach in Germany

Data interoperability is a crucial element for the success of a digitalization strategy. Achieving data interoperability requires clear semantic and syntactic rules. Therefore, digitalization in healthcare should be accompanied by a semantic strategy to outline a foundation of agreed reference coding systems and valuesets for specific domains and segments of the electronic health record. The electronic healthcare system in Germany is evolving. With legal agreement for a European Health Data Space in 2025 also a harmonization in Europe will be pursued. The BfArM as the National Competence Center for medical terminologies has issued a position paper, which is currently discussed with stakeholders. The paper proposes a gradual convergence of standardized documentation in healthcare towards a binding set of basic coding systems and valuesets. Requirements should be identified and semantic reference sources agreed by the domain expert community. A consented semantic framework will support users and software providers in their decision-making and aims to reduce the diversity of individual solutions. A long-term strategy will give decision-makers certainty in their planning and will support a gradual alignment of electronic data towards a mandatory set of basic coding systems. A central asset in this strategy is a central terminology server for the healthcare domain. It will act as a single “source of truth” and reliable platform for semantic references. A central platform will serve as “on-stop-shop” for automatic technical distribution and consumption and also as reliable long-time versioning archive during the live-cycle of electronic healthcare record items.

EMEA

A Unified Framework for SNOMED CT Terminology Bindings in Pathology: Enhancing EHDS Secondary Use Through Standardized Oncology and Non-Oncology Datasets

The European Health Data Space (EHDS) aims to harness health data for improved care and research, yet fragmented pathology reporting practices hinder interoperability. Non-core elements in pathology and molecular datasets‚ critical for secondary use‚ often lack standardization, particularly in structured reporting formats. They can fulfill the minimal international datasets from the CAP, ICCR, etc.

EMEA

Addressing SNOMED CT Structural Anomalies Through AI -Enabled Methods

High-quality clinical terminologies are essential for a connected and efficient healthcare ecosystem. Previous studies have shown the impact of quality issues on downstream applications, such as reduced recall and precision in cohort queries over EHRs. The literature presents several proposals to detect and/or resolve these quality issues. While these proposals typically employ either a lexical, structural, or machine learning-based approach, some combine different techniques to address the issues. This presentation outlines a series of experiments we conducted to improve the quality and clinical accuracy of the SNOMED CT terminology by identifying key areas for enhancement and proposing AI-enabled methods to automate the detection and correction of structural anomalies, such as primitive, misaligned, missing, and redundant concepts.

APAC

An approach to monitor obstetric clinical indicators using SNOMED CT encoded clinical notes

This use case describes the automation of monitoring obstetric clinical indicators using SNOMED CT encoded clinical notes. The semi-structured clinical notes were extracted and encoded with SNOMED CT using a natural language processing system called MyHarmony. A tri-partite arrangement was established, which involved the informaticians at the Health Informatics Centre (Pusat Informatik Kesihatan, PIK) in the Ministry of Health Malaysia, the clinicians headed by the head of Malaysia’s obstetric speciality, and the Information Technology (IT) engineers of the hospitals involved. Each party has specific roles and responsibilities. There were four benefits identified with using this approach: (1) No data entry required. (2) Saved time and resources due to the automation process for data submission, codification, and reporting. Hence, there is no effect on clinical workflow or requires additional workload. (3) Richer analysis due to the granularity of the data written in free text. (4) Timely result which enables clinicians to monitor and intervene faster. However, there were several expected challenges during this project. First is the incompleteness of the documentation by the doctors which need can be improved with continuous awareness. the documentation and the resulting reports. Secondly, the team must standardize the semi-structured clinical notes from different hospitals. Therefore, it is important that the informatician understand the clinical requirement to ensure the right data are extracted by the IT engineers. This approach will be introduced to other clinical specialities so they can experience the same benefit when using SNOMED CT.

EMEA

An innovative approach to Continuous Professional Development on Digital Health Interoperability: Insights from the XiA Project

This presentation introduces the XiA project's innovative framework for delivering continuous professional development (CPD) in digital health interoperability, with a focus on standards used in European Electronic Health Record Exchange Format (EEHRxF), such as SNOMED CT. Central to the approach is the XiA Library, a curated repository of Microcontent Learning Blocks (MLBs) targeted, modular content designed to be combined into tailored learning pathways. These pathways support professionals across the EU in preparing for the European Health Data Space (EHDS) through flexible, engaging, and accredited training. The model promotes scalable, self-paced learning adapted to the needs of various stakeholder groups, including healthcare providers, developers, and decision-makers.

EMEA

An investigation into the use of chromosome band nomenclature in SNOMED CT disorder concepts

A growing number of SNOMED CT disorder concept names include fragments of chromosome band nomenclature (CBN) of varying complexity. Using a mixture of text matching techniques this work has identified such concepts, performed a number of analyses on the concepts returned, and considered the implications of these findings for SNOMED CT and related products. The number of disorder concepts including CBN fragments has increased significantly since 2015. The majority of CBN fragments follow a basic 'chromosome number + arm + region + band + sub-band' format, predominantly naming partial chromosome deletions or duplications implicated in rare diseases. The following findings are presented and discussed: (1) Term forms and synonymy: for many deletions and duplications, the same abnormality is represented using multiple different term forms, with no agreed 'preferred' or 'canonical' term representation. This sort of variation can be problematic, both for content management (notably redundancy/duplicate detection) and for content use (notably display, readability and discovery). SNOMED International has an opportunity to negotiate and agree standard term forms for such concepts, and the more common CBN fragment pattern 'favourites' are discussed. (2) Curation and classification: coarse-grained chromosome structure (number and arm) is already used in concept definitions. Identifying detailed CBN fragments makes possible more precise analysis of each affected concept. Opportunities for such an approach are presented, along with implications for redundancy detection and classification (including gene locus classification and annotation of standard ideogram tools); (3) Term display and interaction: the implications of CBN nomenclature on concept use and readability are discussed.

EMEA

Associating specific tests codes to the LOINC ontology (i.e. the SNOMED extension) and linking them to claimed test results & specimen

This work intention is to support laboratory data interoperability and later data analytics. Ultimately, we aim at enabling our tests data to be understood in any of the three data dictionaries (bioMerieux, LOINC, SNOMED CT)

Following its involvement in the FDA SHIELD program, bioMerieux (world leading IVD systems manufacturer) has a track record of publishing maps associating its test catalogue to LOINC codes and publishing them as LIDV files. We also published several posters where we associated part of our test results (nominal results) to SNOMED CT.

We take benefit of the LOINC-SNOMED collaboration to investigate an association embedding our test catalogue & LOINC codes (cf. LIVD publication), SNOMED observations, claimed test results (binary, ordinal, nominal) and claimed supported specimen.

Firstly, a conceptual model is created and implemented in a graph data model. Secondly, our different mappings are re-used, completed and qualified (exact match, border match and narrow match). Their covering is described based on metrics such as: precision and recall.