The integration of standardized clinical terminologies such as SNOMED CT into electronic health record (EHR) systems has become increasingly important in advancing healthcare data management. Despite its extensive theoretical foundation, evidence detailing practical applications in clinical settings remains limited. This systematic review explores recent literature to identify how SNOMED CT is being utilized across different phases of application within EHRs, highlighting its benefits, challenges, and potential for broader adoption.
Introduction
SNOMED CT, or the Systematized Nomenclature of Medicine–Clinical Terms, is a comprehensive, multi-hierarchical clinical terminology system designed to provide a uniform and scientifically validated approach to representing clinical information [1]. Its application spans various domains, including clinical documentation, knowledge representation, data aggregation, and analysis, supporting interoperability and continuity of care across healthcare organizations [2-5]. The core strength of SNOMED CT lies in its ability to standardize clinical data, enabling effective information sharing and decision support [2], which in turn promotes patient safety and improves care quality.
The potential of SNOMED CT to enhance data quality and facilitate interoperability has been recognized by healthcare authorities such as the European Union and the US Healthcare Information Technology Standards Panel, emphasizing its role in promoting semantic interoperability [1,7,8]. Nonetheless, despite its widespread adoption in over 50 countries, scientific evidence demonstrating its practical benefits in real-world clinical workflows remains scarce. Existing literature primarily focuses on theoretical frameworks or pre-implementation assessments, leaving a gap in understanding the tangible outcomes of SNOMED CT deployment in clinical environments [1,2,8,9].
This review aims to bridge this gap by systematically analyzing recent studies that document SNOMED CT’s application in EHR systems and related clinical software. By examining use cases from the past five years, we seek to identify the primary objectives of SNOMED CT integration, the stages of its implementation, and the benefits realized, including improvements in data quality, clinical productivity, and patient safety [3,5,13].
Methods
The review followed a structured methodology based on the Cochrane review protocol [16], tailored to investigate SNOMED CT use in the context of EHR systems and clinical applications. Our interdisciplinary team, comprising clinical experts and health informatics specialists, developed a comprehensive search strategy to identify pertinent literature published between 2016 and 2022. Key search terms included variations of “EHR,” “EMR,” and related phrases, combined with “SNOMED CT,” to maximize retrieval relevance.
The search was conducted in PubMed, yielding 162 articles after removing duplicates. Inclusion criteria mandated that articles present original research documenting SNOMED CT being tested, piloted, or actively used within a clinical setting, excluding purely theoretical or validation studies. The screening process involved initial abstract review, followed by full-text analysis, ultimately leading to 17 articles that met all criteria.
In analyzing these articles, we extracted data on clinical context, user groups, implementation phases, purpose of use, and reported benefits. The categories for use purpose and phase were adapted from existing frameworks 3,5,13], with particular attention paid to contextual details such as the type of EHR system and stakeholder involvement. For a detailed description of the search process and criteria, see [Multimedia Appendix 1.
Results
Characteristics of Included Publications
The 17 selected studies were published between June 2017 and February 2022, with the highest publication volume in 2021. The majority originated from the UK (5 studies), followed by the US (3), Australia (2), and Spain (2), with single studies from Canada, Denmark, Switzerland, Korea, and Germany. Publications appeared across 13 peer-reviewed journals, reflecting diverse clinical and informatics disciplines.
Contextual Factors of Clinical Use Cases
Most studies (14/17) provided descriptions of the clinical settings. Specialties ranged from neurology, pulmonology, cardiology, oncology, general medicine, pediatrics, to emergency care, with some cases involving primary or tertiary care. EHR descriptions varied considerably, often lacking uniformity; only one study explicitly named the product—a hospital information system supporting care coordination. Other systems included prehospital records, outpatient clinics, or general practice software.
User groups involved clinicians, clinical coders, or multidisciplinary teams, although details on specific professional roles were sometimes vague. Seven studies detailed exact user profiles, including physicians, nurses, or combined clinical teams. These contextual details are summarized further in Multimedia Appendix 3.
Purpose of SNOMED CT Use
All studies outlined specific objectives for implementing SNOMED CT. The most common purpose (8/17) was adopting SNOMED CT as a standard terminology for EHRs to facilitate consistent clinical documentation and data exchange [2]. This standardization aimed to improve communication across care settings, enhance diagnostic accuracy, and support data migration efforts.
Additional use cases included enabling clinical research through data retrieval and analysis (2 studies), classifying and coding patient information for building clinical pathways or patient selection (2 studies), and automating clinical coding processes to support documentation and billing (1 study). Several articles emphasized the importance of verifying the merit of SNOMED CT for improving data quality, supporting interoperability, and increasing coding productivity, often through automated or semi-automated tools.
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Implementation Phases of SNOMED CT
The application stages of SNOMED CT ranged from development, pilot, implementation, to routine use. All studies (17/17) reported on at least one phase. The predominant stage was “in development” (6 studies), describing iterative processes of standardization, validation, and stakeholder coordination that can extend over years. “In use” phases were identified in 5 studies, with some describing the deployment of SNOMED CT to enhance clinical documentation, while others evaluated its impact post-implementation.
Notably, several studies reported pilot projects aimed at testing SNOMED CT in specific clinical modules, assessing data completeness, or detecting inconsistencies. Evaluation studies focusing on the impact of SNOMED CT on data quality and workflow were also observed, emphasizing the evolving maturity of these applications.
Core Benefits of SNOMED CT
The literature consistently highlighted benefits related to improved data quality, including enhanced standardization, richer clinical detail, and better data retrieval capabilities [3]. Many studies (8/17) reported increased accuracy and coherence of clinical data, facilitating safer patient care. Benefits also extended to enabling consistent indexing, storing, and aggregating data across systems, which supports population health management and research [5].
Some studies documented increased coding productivity, either through automation or decision support tools, and noted improvements in clinician satisfaction over time, although adoption required time and training. The evidence suggests that SNOMED CT’s comprehensive scope supports multiple facets of clinical documentation, ultimately contributing to better continuity and safety of care.
Discussion
Summary of Findings
This review synthesizes recent empirical evidence on SNOMED CT’s role in clinical settings, confirming its application primarily as a standard for EHR interoperability, data analysis, and automated coding. The majority of use cases are in developmental or early implementation stages, with tangible benefits observed in data quality and clinical workflow support. However, detailed descriptions of the clinical environments and system functionalities remain inconsistent, hindering broader generalizations.
Clinical Context and Implementation Challenges
A recurring theme is the variability in system descriptions and user involvement, which underscores the need for standardized reporting frameworks. Applying models like EMRAM could facilitate more systematic evaluations of EHR maturity and SNOMED CT integration success [36]. Moreover, involving clinicians from diverse specialties early in development may accelerate adoption and improve the relevance of terminological tools.
Future Directions
While SNOMED CT’s potential to standardize and enrich clinical data is evident, more robust and detailed evidence is needed to demonstrate its impact on patient safety and care outcomes. Future research should emphasize comprehensive descriptions of the clinical environment, stakeholder engagement, and contextual factors influencing implementation success. In addition, evaluating disadvantages, risks, and barriers—such as integration costs or user resistance—is critical.
Given the ongoing large-scale European initiatives, systematic evaluation and reporting of SNOMED CT deployment will be vital. Such efforts can inform best practices and facilitate scalability across healthcare systems.
Limitations
This review’s scope was limited to articles indexed in PubMed, possibly omitting relevant studies from other databases or grey literature. Variability in reporting detail posed challenges in data extraction, particularly regarding system descriptions and clinical contexts. Despite these limitations, the systematic approach and strict inclusion criteria strengthen the reliability of our findings.
Conclusions
This review confirms that SNOMED CT is increasingly being integrated into clinical workflows, offering benefits in data standardization, quality, and interoperability. Nonetheless, further systematic research with detailed contextual descriptions is essential to fully understand its impact on clinical care and patient safety. Emphasizing transparent reporting and standardized evaluation frameworks can accelerate the evidence base, ultimately promoting broader, more effective implementation.
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