PCEtoFHIR: Decomposition of Postcoordinated SNOMED CT Expressions for Storage as HL7 FHIR Resources.

BACKGROUND: To ensure interoperability, both structural and semantic standards must be followed. For exchanging medical data between information systems, the structural standard FHIR (Fast Healthcare Interoperability Resources) has recently gained popularity. Regarding semantic interoperability, the reference terminology SNOMED Clinical Terms (SNOMED CT), as a semantic standard, allows for postcoordination, offering advantages over many other vocabularies. These postcoordinated expressions (PCEs) make SNOMED CT an expressive and flexible interlingua, allowing for precise coding of medical facts. However, this comes at the cost of increased complexity, as well as challenges in storage and processing. Additionally, the boundary between semantic (terminology) and structural (information model) standards becomes blurred, leading to what is known as the TermInfo problem. Although often viewed critically, the TermInfo overlap can also be explored for its potential benefits, such as enabling flexible transformation of parts of PCEs. OBJECTIVE: In this paper, an alternative solution for storing PCEs is presented, which involves combining them with the FHIR data model. Ultimately, all components of a PCE should be expressible solely through precoordinated concepts that are linked to the appropriate elements of the information model. METHODS: The approach involves storing PCEs decomposed into their components in alignment with FHIR resources. By utilizing the Web Ontology Language (OWL) to generate an OWL ClassExpression, and combining it with an external reasoner and semantic similarity measures, a precoordinated SNOMED CT concept that most accurately describes the PCE is identified as a Superconcept. In addition, the nonmatching attribute relationships between the Superconcept and the PCE are identified as the "Delta." Once SNOMED CT attributes are manually mapped to FHIR elements, FHIRPath expressions can be defined for both the Superconcept and the Delta, allowing the identified precoordinated codes to be stored within FHIR resources. RESULTS: A web application called PCEtoFHIR was developed to implement this approach. In a validation process with 600 randomly selected precoordinated concepts, the formal correctness of the generated OWL ClassExpressions was verified. Additionally, 33 PCEs were used for two separate validation tests. Based on these validations, it was demonstrated that a previously proposed semantic similarity calculation is suitable for determining the Superconcept. Additionally, the 33 PCEs were used to confirm the correct functioning of the entire approach. Furthermore, the FHIR StructureMaps were reviewed and deemed meaningful by FHIR experts. CONCLUSIONS: PCEtoFHIR offers services to decompose PCEs for storage within FHIR resources. When creating structure mappings for specific subdomains of SNOMED CT concepts (eg, allergies) to desired FHIR profiles, the use of SNOMED CT Expression Templates has proven highly effective. Domain experts can create templates with appropriate mappings, which can then be easily reused in a constrained manner by end users.

Development of a SNOMED CT Mapping Process and Tool at a Data Integration Centre - Lessons Learned.

INTRODUCTION: 16 million German-language free-text laboratory test results are the basis of the daily diagnostic routine of 17 laboratories within the University Hospital Erlangen. As part of the Medical Informatics Initiative, the local data integration centre is responsible for the accessibility of routine care data for medical research. Following the core data set, international interoperability standards such as FHIR and the English-language medical terminology SNOMED CT are used to create harmonised data. To represent each non-numeric laboratory test result within the base module profile ObservationLab, the need for a map and supporting tooling arose. STATE OF THE ART: Due to the requirement of a n:n map and a data safety-compliant local instance, publicly available tools (e.g., SNAP2SNOMED) were insufficient. Concept and Implementation: Therefore, we developed (1) an incremental mapping-validation process with different iteration cycles and (2) a customised mapping tool via Microsoft Access. Time, labour, and cost efficiency played a decisive role. First iterations were used to define requirements (e.g., multiple user access). LESSONS LEARNED: The successful process and tool implementation and the described lessons learned (e.g., cheat sheet) will assist other German hospitals in creating local maps for inter-consortia data exchange and research. In the future, qualitative and quantitative analysis results will be published.

Incorrect and Sex-Inconsistent Mapping of Disorders: Identification of Sex Biases in the ICD-10, ICD-11 and SNOMED CT and How to Work Around Them.

In the international classifications ICD-10-WHO and ICD-11-WHO, many sex-specific diseases have incomplete coding. It is possible to further enhance semantic interoperability using SNOMED CT additionally to ICD. Part of the analysis of semantic interoperability of diagnoses in the ICD are Sexual Dysfunctions, Postpartum Depression, Sexual Assault, Premenstrual Tension Syndrome and Premenstrual Dysphoric Disorder, Female Genital Mutilation and Cutting, Gender Incongruence and Disorders of Breast. Labeling biases have been identified in all diagnoses, either in SNOMED CT or ICD. For mental disorders associated with pregnancy, gender incongruence and sexual violence the use of the GPS of SNOMED CT can help enhance semantic interoperability additionally to ICD.

From Clinical Information Systems to Personalized Health Knowledge Graphs.

This paper presents a versatile solution to formally represent the contents of electronic health records. It is based on the knowledge graph paradigm, and semantic web standards RDF and OWL. It employs the established semantic standards SNOMED CT and FHIR, which warrant international interoperability. A graph-based form is not only useful to feed different target visualizations, but it can also be subject to AI-powered services such as (fuzzy) retrieval and summarization.

Protocol for a Validation Study of an Ontology of Administrable Dose Forms of Medicinal Products.

Administrable dose form can be obtained after (no-)transformation from pharmaceutical dose form. Building on the creation of a small ontology of 428 pharmaceutical dose forms from EDQM to support alignment with other dose form ontologies (SNOMED-CT, RxNorm), the present study is focused on a simple ontology of 308 administrable dose forms, 27 Intended Sites and an intermediary level of 65 dose form groupers. The ontology was created after 432 pharmaceutical dose forms, 65 combined pharmaceutical dose forms and 73 combined terms were linked by EDQM to administrable dose forms during the UNICOM project. The article describes these resources, the resulting ontology, the differences between its top-level concepts and the source's. It presents the protocol for a validation study through expert review, as a preparation for use case studies.

SIMpat: A Synthetic Benchmark for Similarity Metrics on Patient Representations.

Similarity and clustering tasks based on data extracted from electronic health records on the patient level suffer from the curse of dimensionality and the lack of inter-patient data comparability. Indeed, for many health institutions, there are many more variables, and ways of expressing those variables to represent patients than patients sharing the same set of data. To lower redundancy and increase interoperability one strategy is to map data to semantic-driven representations through medical knowledge graphs such as SNOMED-CT. However, patient similarity metrics based on this knowledge-graph information lack quantitative evaluation and comparisons with pure data-driven methods. The reasons are twofold, firstly, it is hard to conceptually assess and formalize a gold-standard similarity between patients resulting in poor inter-annotator agreement in qualitative evaluations. Secondly, the community has been lacking a clear benchmark to compare existing metrics developed by scientific communities coming from various fields such as ontology, data science, and medical informatics. This study proposes to leverage the known challenges of evaluating patient similarities by proposing SIMpat, a synthetic benchmark to quantitatively evaluate available metrics, based on controlled cohorts, which could later be used to assess their sensibility regarding aspects such as the sparsity of variables or specificities of patient disease patterns.

Semantic Interoperability of Nursing Data - Mapping an Interface Terminology to SNOMED CT.

Using nursing intervention data within the scope of eHealth from a primary system (hospital information system) in a secondary system (electronic patient dossier) requires a common terminology across systems. For this purpose, nursing interventions from the LEP nursing interface terminology widely used in Germany, Switzerland, and Austria were mapped to the international reference terminology SNOMED CT with the support of the National Release Center Switzerland. The completed mapping comprises 467 nursing interventions from LEP Nursing Version 3.5.0 mapped to pre-coordinated SNOMED CT version 2022-12-31 procedures. Moreover, 83 new submissions were included in the international SNOMED CT edition and seven in the Swiss SNOMED CT extension. As a next step, the mapping will be tested to examine the possibilities of semantic interoperability in nursing practice.

Scalar Values in SNOMED CT: A Proposed Extension.

Representing numeric values such as scalars holds great importance for accurately depicting clinical data. While the result value itself will always be represented using an integer, decimal, or other scalar format, it needs to be linked to its corresponding data element. In SNOMED CT, as in most other terminology systems, this is done through an attribute relationship. While some scalar values are already included in this way, they only represent a small fraction of possibilities. Our intention is to expand the scope of scalar representation by validating new attributes using a previously established method. The result is a list of five attributes validated for local representation of scalar values, improving semantic representation and interoperability.

'SNOMEDizing' Questionnaires for Standardizing Stroke Registry Data.

International interoperability of healthcare and research data requires a commitment to standards. To this end, SNOMED CT was evaluated for representing questionnaire items of the European Registry of Stroke Care Quality using a complex annotation protocol. The agreement between validators and annotators was 72.4%. At least 64% of the information could be represented by using SNOMED CT only, including complex post-coordinations. 9% of the information would require an information model, and 14% the addition of new content to SNOMED CT. Next steps will be the creation of an annotation guideline for questionnaires, a specific reference set, and the combination of both with an information model such as HL7 FHIR.

Standardizing X-Ray and Angiography Procedure Coding in Estonia: Validating TermX Through the Implementation of SNOMED CT.

BACKGROUND: The existence of multiple code systems and standards has highlighted the necessity for innovative solutions to bridge these discrepancies. OBJECTIVES: This research investigates the utilisation of TermX to tackle the challenges of interoperability in radiology procedures, with a specific emphasis on angiography and X-ray modalities. RESULTS: The study produced a revised RadLex data model and mapping guide, designed to classify radiology services using TermX. In total, 380 concepts were required to comprehensively describe all 622 procedures examined. CONCLUSIONS: Our study demonstrates the effectiveness of TermX in simplifying the process of mapping between code systems, thus enabling more efficient analysis, and reporting of data.

Harmonization of Data Across Cohorts Using Standard Terminologies.

Korean National Institute of Health initiated data harmonization across cohorts with the aim to ensure semantic interoperability of data and to create a common database of standardized data elements for future collaborative research. With this aim, we reviewed code books of cohorts and identified common data items and values which can be combined for data analyses. We then mapped data items and values to standard health terminologies such as SNOMED CT. Preliminary results of this ongoing data harmonization work will be presented.

Feasibility of disease terminology systems for mapping orphan conditions and therapeutic indications of designated orphan medicines in the European Union.

BACKGROUND: In the European Union, rare diseases are defined as diseases that affect maximum 5 in 10,000 citizens. These diseases are typically associated with a high unmet medical need. To stimulate development and authorisation of medicines for rare diseases ('orphan conditions'), the European Commission (EC) can grant orphan designations. In order to enable systematic evaluation and communication of the diseases for which designated orphan medicines have (not) been developed and authorised, we aimed to investigate the feasibility of important disease terminology systems for mapping orphan conditions and therapeutic indications. METHODS: We selected all designated orphan medicines that were authorised by the EC during 2022-2023 from the EC's Union Register of medicinal products. For these medicines, we extracted orphan conditions and associated therapeutic indications at initial marketing authorisation. The orphan conditions and separate elements of therapeutic indications such as target disease or condition, severity criteria and target population were assessed for availability in six major disease terminology systems: ICD-10, ICD-11, MedDRA, MeSH, Orphanet nomenclature of rare diseases, and SNOMED CT. Descriptive statistics were used to describe the ability of each disease terminology system to map orphan conditions and elements of therapeutic indications. RESULTS: During 2022-2023, 37 designated orphan medicines were authorised that were designated for 40 orphan conditions (of which 37 unique) and granted 39 therapeutic indications (of which 37 unique). Overall, SNOMED CT covered most descriptions of orphan conditions (33/37, 89 %) and target diseases or conditions within therapeutic indications (28/37, 76 %). However, when allowing descriptions to be partly included and/or complemented by additional words, SNOMED CT, the Orphanet nomenclature, ICD-11 and MedDRA all had high coverage (92-97 %). Other elements than target diseases or conditions within therapeutic indications were mostly lacking. CONCLUSIONS: Regulatory data concerning orphan conditions and therapeutic indications of designated orphan medicines seem to be best covered by SNOMED CT. However, which disease terminology system best facilitates systematic evaluation and communication about development and authorisation of designated orphan medicines also depends on the specific use case. Given the frequent use of SNOMED CT in healthcare settings, it may also facilitate interoperability between regulatory and healthcare data, while for example ICD-11 may be better suited to generate statistics concerning drug development for rare diseases.

Creating a Modified Version of the Cambridge Multimorbidity Score to Predict Mortality in People Older Than 16 Years: Model Development and Validation.

BACKGROUND: No single multimorbidity measure is validated for use in NHS (National Health Service) England's General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR), the nationwide primary care data set created for COVID-19 pandemic research. The Cambridge Multimorbidity Score (CMMS) is a validated tool for predicting mortality risk, with 37 conditions defined by Read Codes. The GDPPR uses the more internationally used Systematized Nomenclature of Medicine clinical terms (SNOMED CT). We previously developed a modified version of the CMMS using SNOMED CT, but the number of terms for the GDPPR data set is limited making it impossible to use this version. OBJECTIVE: We aimed to develop and validate a modified version of CMMS using the clinical terms available for the GDPPR. METHODS: We used pseudonymized data from the Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC), which has an extensive SNOMED CT list. From the 37 conditions in the original CMMS model, we selected conditions either with (1) high prevalence ratio (≥85%), calculated as the prevalence in the RSC data set but using the GDPPR set of SNOMED CT codes, divided by the prevalence included in the RSC SNOMED CT codes or (2) conditions with lower prevalence ratios but with high predictive value. The resulting set of conditions was included in Cox proportional hazard models to determine the 1-year mortality risk in a development data set (n=500,000) and construct a new CMMS model, following the methods for the original CMMS study, with variable reduction and parsimony, achieved by backward elimination and the Akaike information stopping criterion. Model validation involved obtaining 1-year mortality estimates for a synchronous data set (n=250,000) and 1-year and 5-year mortality estimates for an asynchronous data set (n=250,000). We compared the performance with that of the original CMMS and the modified CMMS that we previously developed using RSC data. RESULTS: The initial model contained 22 conditions and our final model included 17 conditions. The conditions overlapped with those of the modified CMMS using the more extensive SNOMED CT list. For 1-year mortality, discrimination was high in both the derivation and validation data sets (Harrell C=0.92) and 5-year mortality was slightly lower (Harrell C=0.90). Calibration was reasonable following an adjustment for overfitting. The performance was similar to that of both the original and previous modified CMMS models. CONCLUSIONS: The new modified version of the CMMS can be used on the GDPPR, a nationwide primary care data set of 54 million people, to enable adjustment for multimorbidity in predicting mortality in people in real-world vaccine effectiveness, pandemic planning, and other research studies. It requires 17 variables to produce a comparable performance with our previous modification of CMMS to enable it to be used in routine data using SNOMED CT.

Cross-Mapping the Portuguese Nursing Ontology with ICNP, SNOMED CT and NANDA-I.

EHR Interoperability is crucial to obtain a set of benefits. This can be achieved by using data standards, like ontologies. The Portuguese Nursing Ontology (NursingOntos) is a reference model describing a set of nursing concepts and their relationships, to represent nursing knowledge in the Electronic Health Records (EHR). The purpose of this work was to define a set of correspondences between Nursing Ontology concepts of NursingOntos and other terminologies, which have the same or similar meaning. In this project, we are using the ISO/TR12300:2016 standard on the principles of mapping between terminological systems. Regarding the domain of "airway clearance", we can say that Portuguese Nursing Ontology has a good level of mapping with other terminologies. In conclusion, we can say that Portuguese Nursing Ontology can be used in EHR with the purpose of a global digitalization of health.

Going Beyond Surface Language: An Exploratory Evaluation of Nursing Ontology Mappings.

A range of approaches have been used to develop and evaluate terminology mapping. In seeking to enhance existing methods this exploratory feasibility study examined a small subset of existing equivalency mappings between the International Classification for Nursing Practice and SNOMED CT. To identify potential inconsistencies in allocation, comparisons were made for each concept in each equivalency mapping, through a manual review of a) compositionality and specificity of asserted and inherited relationships, and b) ancestors through to root. There were similarities and several differences across the mappings which were both structural and definitional in nature. In order to demonstrate practical utility, the approach piloted in the present study might benefit from scaling up and a degree of automation. However, the study has demonstrated it is both feasible and potentially useful when evaluating terminology mapping to go beyond the surface language of mapped terms, and to consider the deeper definitional features of the underlying concepts.

Promoting interoperability between SNOMED CT and ICD-11: lessons learned from the pilot project mapping between SNOMED CT and the ICD-11 Foundation.

OBJECTIVE: To explore the feasibility and challenges of mapping between SNOMED CT and the ICD-11 Foundation in both directions, SNOMED International and the World Health Organization conducted a pilot mapping project between September 2021 and August 2022. MATERIALS AND METHODS: Phase 1 mapped ICD-11 Foundation entities from the endocrine diseases chapter, excluding malignant neoplasms, to SNOMED CT. In phase 2, SNOMED CT concepts equivalent to those covered by the ICD-11 entities in phase 1 were mapped to the ICD-11 Foundation. The goal was to identify equivalence between an ICD-11 Foundation entity and a SNOMED CT concept. Postcoordination was used for mapping to ICD-11. Each map was done twice independently, the results were compared, and discrepancies were reconciled. RESULTS: In phase 1, 59% of 637 ICD-11 Foundation entities had an exact match in SNOMED CT. In phase 2, 32% of 1893 SNOMED CT concepts had an exact match in the ICD-11 Foundation, and postcoordination added 15% of exact match. Challenges encountered included non-synonymous synonyms, mismatch in granularity, composite conditions, and residual categories. CONCLUSION: This pilot project shed light on the tremendous amount of effort required to create a map between the 2 coding systems and uncovered some common challenges. Future collaborative work between SNOMED International and WHO will likely benefit from its findings. It is recommended that the 2 organizations should clarify goals and use cases of mapping, provide adequate resources, set up a road map, and reconsider their original proposal of incorporating SNOMED CT into the ICD-11 Foundation ontology.

Redefining Health Care Data Interoperability: Empirical Exploration of Large Language Models in Information Exchange.

BACKGROUND: Efficient data exchange and health care interoperability are impeded by medical records often being in nonstandardized or unstructured natural language format. Advanced language models, such as large language models (LLMs), may help overcome current challenges in information exchange. OBJECTIVE: This study aims to evaluate the capability of LLMs in transforming and transferring health care data to support interoperability. METHODS: Using data from the Medical Information Mart for Intensive Care III and UK Biobank, the study conducted 3 experiments. Experiment 1 assessed the accuracy of transforming structured laboratory results into unstructured format. Experiment 2 explored the conversion of diagnostic codes between the coding frameworks of the ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification), and Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) using a traditional mapping table and a text-based approach facilitated by the LLM ChatGPT. Experiment 3 focused on extracting targeted information from unstructured records that included comprehensive clinical information (discharge notes). RESULTS: The text-based approach showed a high conversion accuracy in transforming laboratory results (experiment 1) and an enhanced consistency in diagnostic code conversion, particularly for frequently used diagnostic names, compared with the traditional mapping approach (experiment 2). In experiment 3, the LLM showed a positive predictive value of 87.2% in extracting generic drug names. CONCLUSIONS: This study highlighted the potential role of LLMs in significantly improving health care data interoperability, demonstrated by their high accuracy and efficiency in data transformation and exchange. The LLMs hold vast potential for enhancing medical data exchange without complex standardization for medical terms and data structure.

Early evaluation of a natural language processing tool to improve access to educational resources for surgical patients.

PURPOSE: Accessible patient information sources are vital in educating patients about the benefits and risks of spinal surgery, which is crucial for obtaining informed consent. We aim to assess the effectiveness of a natural language processing (NLP) pipeline in recognizing surgical procedures from clinic letters and linking this with educational resources. METHODS: Retrospective examination of letters from patients seeking surgery for degenerative spinal disease at a single neurosurgical center. We utilized MedCAT, a named entity recognition and linking NLP, integrated into the electronic health record (EHR), which extracts concepts and links them to systematized nomenclature of medicine-clinical terms (SNOMED-CT). Investigators reviewed clinic letters, identifying words or phrases that described or identified operations and recording the SNOMED-CT terms as ground truth. This was compared to SNOMED-CT terms identified by the model, untrained on our dataset. A pipeline linking clinic letters to patient-specific educational resources was established, and precision, recall, and F1 scores were calculated. RESULTS: Across 199 letters the model identified 582 surgical procedures, and the overall pipeline after adding rules a total of 784 procedures (precision = 0.94, recall = 0.86, F1 = 0.91). Across 187 letters with identified SNOMED-CT terms the integrated pipeline linking education resources directly to the EHR was successful in 157 (78%) patients (precision = 0.99, recall = 0.87, F1 = 0.92). CONCLUSIONS: NLP accurately identifies surgical procedures in pre-operative clinic letters within an untrained subspecialty. Performance varies among letter authors and depends on the language used by clinicians. The identified procedures can be linked to patient education resources, potentially improving patients' understanding of surgical procedures.

Leveraging logical definitions and lexical features to detect missing IS-A relations in biomedical terminologies.

Biomedical terminologies play a vital role in managing biomedical data. Missing IS-A relations in a biomedical terminology could be detrimental to its downstream usages. In this paper, we investigate an approach combining logical definitions and lexical features to discover missing IS-A relations in two biomedical terminologies: SNOMED CT and the National Cancer Institute (NCI) thesaurus. The method is applied to unrelated concept-pairs within non-lattice subgraphs: graph fragments within a terminology likely to contain various inconsistencies. Our approach first compares whether the logical definition of a concept is more general than  that of the other concept. Then, we check whether the lexical features of the concept are contained in those of the other concept. If both constraints are satisfied, we suggest a potentially missing IS-A relation between the two concepts. The method identified 982 potential missing IS-A relations for SNOMED CT and 100 for NCI thesaurus. In order to assess the efficacy of our approach, a random sample of results belonging to the "Clinical Findings" and "Procedure" subhierarchies of SNOMED CT and results belonging to the "Drug, Food, Chemical or Biomedical Material" subhierarchy of the NCI thesaurus were evaluated by domain experts. The evaluation results revealed that 118 out of 150 suggestions are valid for SNOMED CT and 17 out of 20 are valid for NCI thesaurus.

Review of codelists used to define hypertension in electronic health records and development of a codelist for research.

BACKGROUND AND AIMS: Hypertension is a leading risk factor for cardiovascular disease. Electronic health records (EHRs) are routinely collected throughout a person's care, recording all aspects of health status, including current and past conditions, prescriptions and test results. EHRs can be used for epidemiological research. However, there are nuances in the way conditions are recorded using clinical coding; it is important to understand the methods which have been applied to define exposures, covariates and outcomes to enable interpretation of study findings. This study aimed to identify codelists used to define hypertension in studies that use EHRs and generate recommended codelists to support reproducibility and consistency. ELIGIBILITY CRITERIA: Studies included populations with hypertension defined within an EHR between January 2010 and August 2023 and were systematically identified using MEDLINE and Embase. A summary of the most frequently used sources and codes is described. Due to an absence of Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) codelists in the literature, a recommended SNOMED CT codelist was developed to aid consistency and standardisation of hypertension research using EHRs. FINDINGS: 375 manuscripts met the study criteria and were eligible for inclusion, and 112 (29.9%) reported codelists. The International Classification of Diseases (ICD) was the most frequently used clinical terminology, 59 manuscripts provided ICD 9 codelists (53%) and 58 included ICD 10 codelists (52%). Informed by commonly used ICD and Read codes, usage recommendations were made. We derived SNOMED CT codelists informed by National Institute for Health and Care Excellence guidelines for hypertension management. It is recommended that these codelists be used to identify hypertension in EHRs using SNOMED CT codes. CONCLUSIONS: Less than one-third of hypertension studies using EHRs included their codelists. Transparent methodology for codelist creation is essential for replication and will aid interpretation of study findings. We created SNOMED CT codelists to support and standardise hypertension definitions in EHR studies.