SNOMED CT Research Articles

Association between Hereditary Angioedema and Venous Thromboembolism in a Large Population-Based Case-Control Study

Introduction: Hereditary Angioedema (HAE) is a rare congenital disorder primarily caused by mutations in the SERPING1 gene that results in C1 inhibitor (C1INH) deficiency or dysfunction. C1INH is a multifunctional serine protease inhibitor that functions as a major endogenous negative regulator of the kallikrein-kinin, contact pathway of coagulation and complement systems. HAE typically presents with episodes of unpredictable subcutaneous and submucosal swelling. However, HAE patients also have evidence of systemic activation of coagulation including elevated plasma prothrombin fragments, thrombin antithrombin complexes and D-dimers. Further, patients with HAE have recently been reported to have an increased risk of venous thromboembolism (VTE). Methods: To evaluate the risk of VTE in patients with HAE we conducted a large population-based case-control study leveraging data from the United Kingdom Clinical Practice Research Datalink (CPRD). The CPRD Aurum dataset used here maintains anonymized electronic medical records of over 40 million individuals from over 1600 primary care practices in the United Kingdom. SNOMED CT medical codes were used to identify a cohort of 2,198 patients diagnosed with HAE between 1992 and 2022 in the CPRD Aurum dataset with a matched (age, gender, family practice, and index date for HAE) cohort of 4,376 controls selected. VTE events including deep vein thrombosis (DVT) and pulmonary embolism in the HAE case and control cohorts were identified using SNOMED CT medical codes. The Cox proportional hazards model was used to determine hazard ratios (HR) for VTE events in HAE cases versus controls adjusting for covariates age, sex, body mass index, blood pressure, smoking status and a number of co-morbidities specifically; neoplasm, type 2 diabetes, ischemic heart disease, chronic obstructive pulmonary disease, chronic kidney disease, autoimmune disorders and depression. Results: A greater proportion of the HAE cases were female (62%) with a mean age at HAE diagnosis of 37 years. The median follow-up period for both cases and controls was 17 years (interquartile range (IQR):8-28 years). The overall incidence of VTE in HAE cases was 1.1/1000 patient years versus 0.6/1000 patient years in the control cohort. In unadjusted analyses HAE was associated with a significantly increased risk of VTE (HR 1.70, 95% CI 1.37-2.22, P<0.0001). Importantly, in adjusted analyses HAE remained significantly associated with an increased risk of VTE (HR 1.44, 95% CI 1.12-1.83, P=0.004, Figure 1). Of note, this association is similar in magnitude to that previously reported for heterozygous carriers of FV Leiden, the prothrombin gene mutation and high confidence loss of function mutations in Protein C and Protein S in comparable population-based case-control studies. In adjusted sub-analyses HAE was associated with a significantly increased risk of DVT (HR: 1.42, 95% CI 1.09-1.87, P=0.01) with a trend towards an increased risk of PE (HR 1.37, 95% CI 0.84-2.27, P=0.2). Conclusions: The results of this population-based case-control study, roughly an order of magnitude larger in size than prior reports, demonstrate that HAE is associated with a significantly increased risk of VTE similar to that reported for common hereditary thrombophilias. These findings reinforce the important role of endogenous C1INH as a negative regulator of prothrombotic processes involved in VTE in the setting of HAE.

SNOMED CT and Basic Formal Ontology – convergence or contradiction between standards? The case of “clinical finding”

Background: SNOMED CT is a large terminology system designed to represent all aspects of healthcare. Its current form and content result from decades of bottom-up evolution. Due to SNOMED CT’s formal descriptions, it can be considered an ontology. The Basic Formal Ontology (BFO) is a foundational ontology that proposes a small set of disjoint, hierarchically ordered classes, supported by relations and axioms. In contrast, as a typical top-down endeavor, BFO was designed as a foundational framework for domain ontologies in the natural sciences and related disciplines. Whereas it is mostly assumed that domain ontologies should be created as extensions of foundational ontologies, a post-hoc harmonization of consolidated domain ontologies in use, such as SNOMED CT, is known to be challenging. Methods: We explored the feasibility of harmonizing SNOMED CT with BFO, with a focus on the SNOMED CT Clinical Finding hierarchy. With more than 100,000 classes, it accounts for about one third of SNOMED CT’s content. In particular, we represented typical SNOMED CT finding/disorder concepts using description logics under BFO. Three representational patterns were created and the logical entailments analyzed. Results: Under a first scrutiny, the clinical intuition that diseases, disorders, signs and symptoms form a homogeneous ontological upper-level class appeared incompatible with BFO’s upper-level distinction into continuants and occurrents. The Clinical finding class seemed to be an umbrella for all kinds of entities of clinical interest, such as material entities, processes, states, dispositions, and qualities. This suggests the conclusion that Clinical finding would not be a suitable upper-level class from an BFO perspective. On closer inspection of the taxonomic links within this hierarchy and the implicit meaning derived thereof, it became clear that Clinical finding classes do not characterize the entity (e.g. a fracture, allergy, tumor, pain, hemorrhage, seizure, fever) in a literal sense but rather the condition of a patient having that fracture, allergy, pain etc. This gives sense to the current characteristic of the Clinical Finding hierarchy, in which complex classes are modeled as subclasses of their constituents. Most of these taxonomic links are inferred, as the consequence of the ‘role group’ design pattern, which is ubiquitous in SNOMED CT and has often been subject of controversy regarding its semantics. Conclusion: Our analyses resulted in the proposal of (i) equating SNOMED CT’s ‘role group’ property with the reflexive and transitive BFO relation ‘has occurrent part’; and (ii) reinterpreting Clinical Findings as Clinical Occurrents, i.e. temporally extended entities in an organism, having one or more occurrents as temporal parts that occur in continuants. This re-interpretation was corroborated by a manual analysis of classes under Clinical Finding, as well as the identification of similar modeling patterns in other ontologies. As a result, SNOMED CT does not require any content redesign to establish compatibility with BFO, apart from this re-interpretation, and a suggested re-labeling. Regarding the feasibility of harmonizing terminologies with principled foundational ontologies post-hoc, our results provide support to the assumption that this does not necessarily require major redesign efforts, but rather a careful analysis of the implicit assumptions of terminology curators and users.

The impact of SNOMED CT on clinical coding quality and productivity

Background/Aims Studies have shown that using the clinical terminology SNOMED CT can improve clinical documentation, data quality and workforce productivity. This study aimed to evaluate the impact of a SNOMED CT-based digital tool on the quality, completeness and efficiency of clinical coding. Methods A SNOMED CT-enabled clinical coding tool (SNOMED CT - Clinical Coding Assistant) was created. The tool used nursing assessments to identify relevant conditions that should be coded. SNOMED CT concepts and the nationally published SNOMED CT to ICD-10 cross-maps were compared to clinical coding data generated by the typical clinical coding and audit processes. The tool's impact on coding quality, completeness and productivity was assessed using various statistical measures. A total of 1681 coded hospital spells, representing 1589 patients, were included, across a 2-month period. Results Use of the SNOMED CT - Clinical Coding Assistant led to ICD-10 codes being added or changed in nearly half (n=773, 46.0%) of the hospital spells. The tool identified that over a third of the spells (n=588, 35.0%) had missing ICD-10 codes. The tool significantly reduced the time taken to find and validate the presence of numerous conditions for subsequent coding (P<0.001). Conclusions Use of SNOMED CT-based tools can improve clinical coding accuracy, completeness and productivity. SNOMED CT has an important role to play in clinical coding transformation and effective system implementation is critical.

Building Quality Improvement Initiatives through Anti-Cancer Stewardship in the Veterans Health Administration (VHA): CML Pilot

The Department of Veterans Affairs (VA) Pharmacy Benefits Management (PBM) Services created a national stewardship with focus on anti-cancer therapeutics within a disease-specific cohort . Directing stewardship initiatives to a cohort of Veterans with a common malignancy is a novel approach. A multidisciplinary team with National PBM and VISNs (Veterans Integrated Service Network) promotes standardization of practice with accountability to monitor outcomes as a continual quality improvement process. A toolkit was built to support stewardship activities and includes: near-real-time process to identify a national disease-specific Veteran cohortdisease validation processcentralized MUE data collectioneducational resourcessite for communication and training materials Tyrosine Kinase Inhibitors (TKIs) account for the second highest utilization among outpatient oral anti-cancer therapies in VHA . Imatinib has demonstrated persistent efficacy and is the preferred TKI on the VA National Formulary. Utilizing our toolkit, we developed two MUEs for our national CML cohort to determine why Veterans were not prescribed imatinib as 1L therapy and reasons for switching from first to second line therapy. Methods Business rules identified VISN CML cohorts by using data sources and diagnostic coding from the VA Electronic Health Record (EHR): primary care and hematology/oncology visits (ICD-10), inpatient stays (ICD-10), and problem lists (ICD-10; SNOMED CT). Retrospective chart review of two subsets: Veterans who received a drug other than imatinib as their initial VA prescriptionVeterans who received at least two unique drugs to treat CML Data abstracted for MUE #1: Disposition of TKI prescriptionIf new initiation, documented reason(s) for drug selectionIf continuation, prior drug therapy history Data abstracted for MUE #2: Documented reason(s) for switch to 2LIf adverse drug reaction (ADR) included as a reason, is it documented in the EHR? Analysis combined responses with prescription data from the EHR. Results Of the 18 VISNs, 9 participated in MUE data collection. 365 patients were identified as not having received imatinib 1L and 360 were identified as having received at least two unique drugs to treat CML. These counts are each approximately 30% of the total CML patient cohort. In the total cohort, ~50% of patients received imatinib 1L and have not switched to 2L. Responses for 1L TKI therapy (N=365) 199 (55%) continued therapy originally started outside VA with 67% of these patients (n=133) without documentation of prior imatinib therapy166 (45%) were newly initiating CML therapy with top documented reasons for drug selection: Intermediate or high risk (e.g., SOKAL risk scoring): 77 (46%)46 (28%)18 (11%)Dasatinib was the most prescribed 1L therapy in this cohort (64%; 235), followed by nilotinib (23%; 85), which had a higher prevalence when therapy was continuation (30%).53 patients (15%) had subsequent imatinib therapyResponses for switch from 1L to 2L therapy (N=360) Top documented reasons for switch from 1L to 2L therapy 215 (60%)81 (23%)Resistance / inadequate response: 69 (19%)Only 37 (17%) who experienced an ADR had it documented in the EHRThe most common 2L drug was dasatinib (55%;198). The median number of days between 1L and 2L initiation was 275 (range: 7 - 2, 573)136 patients (38%) received third line therapy or beyond Conclusions A formalized oncology stewardship with focus on disease-specific initiatives within a national healthcare system is feasible. Responses from our 1L MUE indicate that despite geography, imatinib remains the preferred TKI and accounts for the highest utilization. Most veterans not receiving imatinib as 1L therapy, received dasatinib or nilotinib. Continuation of therapy due to transition of care to VA was the primary reason for 1L therapy with a non-preferred TKI. Other reasons included: FDA indication and SOKAL risk scoring. In our pilot 1L to 2L MUE, ADRs were the most common reason for CML patients to be switched to 2L (45%) followed by resistance/inadequate response and disease progression . Most ADRs were not documented in the EHR. Further interventions in TKI prescribing may be instituted in select sites . Following initiatives for CML, Anti-Cancer Stewardship will be expanded to other oncologic diseases.

Mapping Hungarian procedure codes to SNOMED CT

BackgroundData harmonisation is essential in real-world data (RWD) research projects based on hospital information systems databases, as coding systems differ between countries. The Hungarian hospital information systems and the national claims database use internationally known diagnosis codes, but data on medical procedures are recorded using national codes. There is no simple or standard solution for mapping the national codes to a standard coding system. Our aim was to map the Hungarian procedure codes (OENO) to SNOMED CT as part of the European Health Data Evidence Network (EHDEN) project.MethodsWe recruited 25 professionals from different specialties to manually map the procedure codes used between 2011 and 2021. A mapping protocol and training material were developed, results were regularly revised, and the challenges of mapping were recorded. Approximately 7% of the codes were mapped by more people in different specialties for validation purposes.ResultsWe mapped 4661 OENO codes to standard vocabularies, mostly SNOMED CT. We categorized the challenges into three main areas: semantic, matching, and methodological. Semantic refers to the occasionally unclear meaning of the OENO codes, matching to the different granularity and purpose of the OENO and SNOMED CT vocabularies. Lastly, methodological challenges were used to describe issues related to the design of the above-mentioned two vocabularies.ConclusionsThe challenges and solutions presented here may help other researchers to design their process to map their national codes to standard vocabularies in order to achieve greater consistency in mapping results. Moreover, we believe that our work will allow for better use of RWD collected in Hungary in international research collaborations.

Improving Causal Bayesian Networks Using Expertise in Authoritative Medical Ontologies

Discovering causal relationships among symptoms is a topical issue in the analysis of observational patient datasets. A Causal Bayesian Network (CBN) is a popular analytical framework for causal inference. While there are many methods and algorithms capable of learning a Bayesian network, they are reliant on the complexity and thoroughness of the algorithm and do not consider prior expertise from authoritative sources. This article proposes a novel method of extracting prior causal knowledge contained in Authoritative Medical Ontologies (AMOs) and using this prior knowledge to orient arcs in a CBN learned from observational patient data. Since AMOs are robust biomedical ontologies containing the collective knowledge of the experts who created them, utilizing the ordering information contained within them produces improved CBNs that provide additional insight into the disease domain. To demonstrate our method, we obtained prior causal ordering information among symptoms from three AMOs: (1) the Medical Dictionary for Regulatory Activities Terminology (MedDRA), (2) the International Classification of Diseases Version 10 Clinical Modification (ICD-10-CM), and (3) Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). The prior ontological knowledge from these three AMOs is then used to orient arcs in a series of CBNs learned from the National Institutes of Mental Health study on Sequenced Treatment Alternatives to Relieve Depression (STAR*D) patient dataset using the Max-Min Hill-Climbing (MMHC) algorithm. Six distinct CBNs are generated using MMHC: an unmodified baseline model using only the algorithm, three CBNs oriented with ordered-variable pairs from MedDRA, ICD-10-CM, and SNOMED CT, and two more with ordered pairs from a combination of these AMOs. The resulting CBNs modified using ordered-variable pairs significantly change the structure of the network. The agreement between the Modified networks and the Baseline ranges from 50% to 90%. A modified network using ordering information from all ontologies obtained an agreement of 50% (10 out of 20 arcs exist in both the Baseline and Modified models) while maintaining comparable predictive accuracy. This indicates that the Modified CBN reflects the causal claims in the AMOs and agrees with both the AMOs and the observational STAR*D dataset. Furthermore, the Modified models discovered new potentially causal relationships among symptoms in the model, while eliminating weaker edges in a qualitative analysis of the significance of these relationships in existing epidemiological research.

Understanding Severe Asthma Through Small and Big Data in Spanish Hospitals: The PAGE Study.

Data on the prevalence of severe asthma (SA) are limited. Electronic health records (EHRs) offer a unique research opportunity to test machine learning (ML) tools in epidemiological studies. Our aim was to estimate the prevalence of SA among asthma patients seen in hospital asthma units, using both ML-based and traditional research methodologies. Our secondary objective was to describe patients with nonsevere asthma (NSA) and SA over a follow-up of 12 months. PAGE is a multicenter, controlled, observational study conducted in 36 Spanish hospitals and split into 2 phases: a cross-sectional phase for estimation of the prevalence of SA and a prospective phase (3 visits in 12 months) for the follow-up and characterization of SA and NSA patients. A substudy with ML was performed in 6 hospitals. Our ML tool uses EHRead technology, which extracts clinical concepts from EHRs and standardizes them to SNOMED CT. The prevalence of SA among asthma patients in Spanish hospitals was 20.1%, compared with 9.7% using the ML tool. The proportion of SA phenotypes and the features of patients followed up were consistent with previous studies. The clinical predictions of patients' clinical course were unreliable, and ML found only 2 predictive models with discriminatory power to predict outcomes. This study is the first to estimate the prevalence of SA in hospitalized asthma patients and to predict patient outcomes using both standard and ML-based research techniques. Our findings offer relevant insights for further epidemiological and clinical research in SA.

Towards interoperability in infection control: a standard data model for microbiology

The COVID-19 pandemic has made it clear: sharing and exchanging data among research institutions is crucial in order to efficiently respond to global health threats. This can be facilitated by defining health data models based on interoperability standards. In Germany, a national effort is in progress to create common data models using international healthcare IT standards. In this context, collaborative work on a data set module for microbiology is of particular importance as the WHO has declared antimicrobial resistance one of the top global public health threats that humanity is facing. In this article, we describe how we developed a common model for microbiology data in an interdisciplinary collaborative effort and how we make use of the standard HL7 FHIR and terminologies such as SNOMED CT or LOINC to ensure syntactic and semantic interoperability. The use of international healthcare standards qualifies our data model to be adopted beyond the environment where it was first developed and used at an international level.

Evaluating Interoperability in German Critical Incident Reporting Systems.

In industrialised countries, one in ten patients suffers harm during hospitalization. Critical Incident Reporting Systems (CIRS) aim to minimize this by learning from errors and identifying potential risks. However, a lack of interoperability among the 16 CIRS in Germany hampers their effectiveness. This study investigates reports' syntactic and semantic interoperability across seven different reporting systems. Syntactic interoperability was examined using WHO's Minimal Information Models (MIM), while semantic interoperability was evaluated with SNOMED concepts. The findings reveal a low structural overlap, with only two terms correctly represented in the SNOMED CT terminology. In addition, most systems showed no syntactic interoperability. Improving interoperability is essential for increasing the effectiveness and usability of CIRS. The study suggests a unified data model such as MIM or using Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) resources and expanding SNOMED CT with patient safety-relevant terms for semantic interoperability. Given the current lack of both syntactic and semantic interoperability in CIRS, developing a patient safety ontology is recommended for efficient critical incident analysis too.

Identifying populations with chronic pain in primary care: developing an algorithm and logic rules applied to coded primary care diagnostic and medication data

BackgroundEstimates of chronic pain prevalence using coded primary care data are likely to be substantially lower than estimates derived from community surveys. Most primary care studies have estimated chronic pain prevalence using data searches confined to analgesic medication prescriptions. Increasingly, following recent NICE guideline recommendations, patients and doctors opt for non-drug treatment of chronic pain thus excluding these patients from prevalence estimates based on medication codes. We aimed to develop and test an algorithm combining medication codes with selected diagnostic codes to estimate chronic pain prevalence using coded primary care data.MethodsFollowing a scoping review 4 criteria were developed to identify cohorts of people with chronic pain. These were (1) people with one of 12 (‘tier 1’) conditions that almost always results in the individual having chronic pain (2) people with one of 20 (‘tier 2’) conditions included when there are also 3 or more prescription-only analgesics issued in the last 12 months (3) chronic neuropathic pain, or (4) 4 or more prescription-only analgesics issued in the last 12 months. These were translated into 8 logic rules which included 1,932 SNOMED CT codes.ResultsThe algorithm was run on primary care data from 41 GP Practices in Lambeth. The total population consisted of 386,238 GP registered adults ≥ 18 years as of the 31st March 2021. 64,135 (16.6%) were identified as people with chronic pain. This definition demonstrated notably high rates in Black ethnicity females, and higher rates in the most deprived, and older population.ConclusionsEstimates of chronic pain prevalence using structured healthcare data have previously shown lower prevalence estimates for chronic pain than reported in community surveys. This has limited the ability of researchers and clinicians to fully understand and address the complex multifactorial nature of chronic pain. Our study demonstrates that it may be possible to establish more representative prevalence estimates using structured data than previously possible. Use of logic rules offers the potential to move systematic identification and population-based management of chronic pain into mainstream clinical practice at scale and support improved management of symptom burden for people experiencing chronic pain.

Machine translation of standardised medical terminology using natural language processing: A scoping review

Standardised medical terminologies are used to ensure accurate and consistent communication of information and to facilitate data exchange. Currently, many terminologies are only available in English, which hinders international research and automated processing of medical data. Natural language processing (NLP) and Machine Translation (MT) methods can be used to automatically translate these terms. This scoping review examines the research on automated translation of standardised medical terminology. A search was performed in PubMed and Web of Science and results were screened for eligibility by title and abstract as well as full text screening. In addition to bibliographic data, the following data items were considered: 'terminology considered', 'terms considered', 'source language', 'target language', 'translation type', 'NLP technique', 'NLP system', 'machine translation system', 'data source' and 'translation quality'. The results showed that the most frequently translated terminology is SNOMED CT (39.1%), followed by MeSH (13%), ICD (13%) and UMLS (8.7%). The most common source language is English (55.9%), and the most common target language is German (41.2%). Translation methods are often based on Statistical Machine Translation (SMT) (41.7%) and, more recently, Neural Machine Translation (NMT) (30.6%), but can also be combined with various MT methods. Commercial translators such as Google Translate (36.4%) and automatic validation methods such as BLEU (22.2%) are frequently used tools for translation and subsequent validation.

Automating hybrid collective intelligence in open-ended medical diagnostics

Collective intelligence has emerged as a powerful mechanism to boost decision accuracy across many domains, such as geopolitical forecasting, investment, and medical diagnostics. However, collective intelligence has been mostly applied to relatively simple decision tasks (e.g., binary classifications). Applications in more open-ended tasks with a much larger problem space, such as emergency management or general medical diagnostics, are largely lacking, due to the challenge of integrating unstandardized inputs from different crowd members. Here, we present a fully automated approach for harnessing collective intelligence in the domain of general medical diagnostics. Our approach leverages semantic knowledge graphs, natural language processing, and the SNOMED CT medical ontology to overcome a major hurdle to collective intelligence in open-ended medical diagnostics, namely to identify the intended diagnosis from unstructured text. We tested our method on 1,333 medical cases diagnosed on a medical crowdsourcing platform: The Human Diagnosis Project. Each case was independently rated by ten diagnosticians. Comparing the diagnostic accuracy of single diagnosticians with the collective diagnosis of differently sized groups, we find that our method substantially increases diagnostic accuracy: While single diagnosticians achieved 46% accuracy, pooling the decisions of ten diagnosticians increased this to 76%. Improvements occurred across medical specialties, chief complaints, and diagnosticians' tenure levels. Our results show the life-saving potential of tapping into the collective intelligence of the global medical community to reduce diagnostic errors and increase patient safety.

Evaluating the representation of disaster hazards in SNOMED CT: gaps and opportunities.

Climate change, an underlying risk driver of natural disasters, threatens the environmental sustainability, planetary health, and sustainable development goals. Incorporating disaster-related health impacts into electronic health records helps to comprehend their impact on populations, clinicians, and healthcare systems. This study aims to: (1) map the United Nations Office for Disaster Risk Reduction and International Science Council (UNDRR-ISC) Hazard Information Profiles to SNOMED CT International, a clinical terminology used by clinicians, to manage patients and provide healthcare services; and (2) to determine the extent of clinical terminologies available to capture disaster-related events. Concepts related to disasters were extracted from the UNDRR-ISC's Hazard Information Profiles and mapped to a health terminology using a procedural framework for standardized clinical terminology mapping. The mapping process involved evaluating candidate matches and creating a final list of matches to determine concept coverage. A total of 226 disaster hazard concepts were identified to adversely impact human health. Chemical and biological disaster hazard concepts had better representation than meteorological, hydrological, extraterrestrial, geohazards, environmental, technical, and societal hazard concepts in SNOMED CT. Heatwave, drought, and geographically unique disaster hazards were not found in SNOMED CT. To enhance clinical reporting of disaster hazards and climate-sensitive health outcomes, the poorly represented and missing concepts in SNOMED CT must be included. Documenting the impacts of climate change on public health using standardized clinical terminology provides the necessary real time data to capture climate-sensitive outcomes. These data are crucial for building climate-resilient healthcare systems, enhanced public health disaster responses and workflows, tracking individual health outcomes, supporting disaster risk reduction modeling, and aiding in disaster preparedness, response, and recovery efforts.

Creating a SNOMED CT reference set for common endocrine disorders based on routine clinic correspondence.

Routine clinical coding of clinical outcomes in outpatient consultations still lags behind the coding of episodes of inpatient care. Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) offers an opportunity for standardised coding of key clinical information. Identifying the most commonly required SNOMED terms and grouping these into a reference set will aid future adoption in routine clinical care. To create a common endocrinology reference set to standardise the coding for outcomes of outpatient endocrine consultations, using a semi-automated extraction of information from existing clinical correspondence. Retrospective review of data from an adult tertiary outpatient endocrine clinic between 2018 and 2019. A total of 1870 patients from postcodes within two regional areas of NHS Grampian (Aberdeen City and Aberdeenshire) attended the clinic. Following consultation, an automated script extracted each problem statement which was manually coded using the 'disorder' concepts from SNOMED CT (UK edition). The review identified 298 relevant endocrine diagnoses, 99 findings and 142 procedures. There were a total of 88 (29.5%) commonly seen endocrine conditions (e.g., Graves' disease, anterior hypopituitarism and Addison's disease) and 210 (70.5%) less commonly seen endocrine conditions. Subsequently, consultant endocrinologists completed a survey regarding the common endocrine conditions; 28 conditions have 100% agreement, 25 have 90%-99% agreement, 31 have 50%-89% agreement and 4 have less than 59% agreement (which were excluded). Automated text parsing of structured endocrine correspondence allowed the creation of a SNOMED CT reference set for common endocrine disorders. This will facilitate funding and planning of service provision in endocrinology by allowing more accurate characterisation of the patient cohorts needing specialist endocrine care.

Standardized Clinical Annotation of Digital Histopathology Slides at the Point of Diagnosis

As digital pathology replaces conventional glass slide microscopy as a means of reporting cellular pathology samples, the annotation of digital pathology whole slide images is rapidly becoming part of a pathologist’s regular practice. Currently, there is no recognizable organization of these annotations, and as a result, pathologists adopt an arbitrary approach to defining regions of interest, leading to irregularity and inconsistency and limiting the downstream efficient use of this valuable effort. In this study, we propose a Standardized Annotation Reporting Style for digital whole slide images. We formed a list of 167 commonly annotated entities (under 12 specialty subcategories) based on review of Royal College of Pathologists and College of American Pathologists documents, feedback from reporting pathologists in our NHS department, and experience in developing annotation dictionaries for PathLAKE research projects. Each entity was assigned a suitable annotation shape, SNOMED CT (SNOMED International) code, and unique color. Additionally, as an example of how the approach could be expanded to specific tumor types, all lung tumors in the fifth World Health Organization of thoracic tumors 2021 were included. The proposed standardization of annotations increases their utility, making them identifiable at low power and searchable across and between cases. This would aid pathologists reporting and reviewing cases and enable annotations to be used for research. This structured approach could serve as the basis for an industry standard and be easily adopted to ensure maximum functionality and efficiency in the use of annotations made during routine clinical examination of digital slides.

Representation of Environmental Concepts Associated with Health Impacts in Computer Standardized Clinical Terminologies.

To evaluate the representation of environmental concepts associated with health impacts in standardized clinical terminologies. This study used a descriptive approach with methods informed by a procedural framework for standardized clinical terminology mapping. The United Nations Global Indicator Framework for the Sustainable Development Goals and Targets was used as the source document for concept extraction. The target terminologies were the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and the International Classification for Nursing Practice (ICNP). Manual and automated mapping methods were utilized. The lists of candidate matches were reviewed and iterated until a final mapping match list was achieved. A total of 119 concepts with 133 mapping matches were added to the final SNOMED CT list. Fifty-three (39.8%) were direct matches, 37 (27.8%) were narrower than matches, 35 (26.3%) were broader than matches, and 8 (6%) had no matches. A total of 26 concepts with 27 matches were added to the final ICNP list. Eight (29.6%) were direct matches, 4 (14.8%) were narrower than, 7 (25.9%) were broader than, and 8 (29.6%) were no matches. Following this evaluation, both strengths and gaps were identified. Gaps in terminology representation included concepts related to cost expenditures, affordability, community engagement, water, air and sanitation. The inclusion of these concepts is necessary to advance the clinical reporting of these environmental and sustainability indicators. As environmental concepts encoded in standardized terminologies expand, additional insights into data and health conditions, research, education, and policy-level decision-making will be identified.

Exploring the nuances of biomedical language: a study on the polysemy of the word pattern

PurposeEffective communication is crucial in the medical field where different stakeholders use various terminologies to describe and classify healthcare concepts such as ICD, SNOMED CT, UMLS and MeSH, but the problem of polysemy can make natural language processing difficult. This study explores the contextual meanings of the term “pattern” in the biomedical literature, compares them to existing definitions, annotates a corpus for use in machine learning and proposes new definitions of terms such as “Syndrome, feature” and “pattern recognition.”Design/methodology/approachEntrez API was used to retrieve articles form PubMed for the study which assembled a corpus of 398 articles using a search query for the ambiguous term “pattern” in the titles or abstracts. The python NLTK library was used to extract the terms and their contexts, and an expert check was carried out. To understand the various meanings of the term, the contextual environment was analyzed by extracting the surrounding words of the term. The expert determined the appropriate size of the context for analysis to gain a more nuanced understanding of the different meanings of the term pattern.FindingsThe study found that the categories of meanings of the term “pattern” are broader in biomedical publications than in common definitions, and new categories have been emerging from the term's use in the biomedical field. The study highlights the importance of annotated corpora in advancing natural language processing techniques and provides valuable insights into the nuances of biomedical language.Originality/valueThe study's findings demonstrate the importance of exploring contextual meanings and proposing new definitions of terms in the biomedical field to improve natural language processing techniques.

Digital Health Information Systems in the Member States of the Commonwealth of Independent States: Status and Prospects

This paper examines the status of the development of national digital health information systems (HIS) in Commonwealth of Independent States (CIS) member states. Data for research were collected using a questionnaire adapted from the questionnaire of the WHO’s Third Global Survey on eHealth. The results showed that the digital transformation of HIS was occurring in all seven CIS member states (participating in the study), which were financed by different resources. Laws and regulations on electronic medical records (EMR) use were present in almost all participating CIS member states. Various international standards and classifications were used to support development and the interoperability of digital health information system (d-HIS), including International Classification of Diseases (ICD), Digital Imaging and Communications in Medicine (DICOM), ISO 18308, Logical Observation Identifiers, Names, and Codes (LOINC), Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), and ISO TC 215. Several CIS member states had adopted a national information security strategy for the safe processing of both personal data and medical confidential information. The digital transformation of healthcare and the Empowerment through Digital Health initiative are taking place in all CIS member states, which are at different stages of introducing electronic medical and health records.

Learning important common data elements from shared study data: The All of Us program analysis.

There are many initiatives attempting to harmonize data collection across human clinical studies using common data elements (CDEs). The increased use of CDEs in large prior studies can guide researchers planning new studies. For that purpose, we analyzed the All of Us (AoU) program, an ongoing US study intending to enroll one million participants and serve as a platform for numerous observational analyses. AoU adopted the OMOP Common Data Model to standardize both research (Case Report Form [CRF]) and real-world (imported from Electronic Health Records [EHRs]) data. AoU standardized specific data elements and values by including CDEs from terminologies such as LOINC and SNOMED CT. For this study, we defined all elements from established terminologies as CDEs and all custom concepts created in the Participant Provided Information (PPI) terminology as unique data elements (UDEs). We found 1 033 research elements, 4 592 element-value combinations and 932 distinct values. Most elements were UDEs (869, 84.1%), while most CDEs were from LOINC (103 elements, 10.0%) or SNOMED CT (60, 5.8%). Of the LOINC CDEs, 87 (53.1% of 164 CDEs) originated from previous data collection initiatives, such as PhenX (17 CDEs) and PROMIS (15 CDEs). On a CRF level, The Basics (12 of 21 elements, 57.1%) and Lifestyle (10 of 14, 71.4%) were the only CRFs with multiple CDEs. On a value level, 61.7% of distinct values are from an established terminology. AoU demonstrates the use of the OMOP model for integrating research and routine healthcare data (64 elements in both contexts), which allows for monitoring lifestyle and health changes outside the research setting. The increased inclusion of CDEs in large studies (like AoU) is important in facilitating the use of existing tools and improving the ease of understanding and analyzing the data collected, which is more challenging when using study specific formats.

New developments and expansion of the GP in-hours syndromic surveillance system: collaboration between UKHSA and the Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub.

The UK Health Security Agency have established a partnership with the Oxford-Royal College of General Practitioners (RCGP) Clinical Informatics Digital Hub (ORCHID) to provide a new source of primary care data to enhance an existing GP in-hours syndromic surveillance system. To provide better coverage of the English population and more comprehensive information to inform public health action. We produced indicators using SNOMED CT codes grouped into syndromes and validated the outputs against the existing GP in-hours data. A daily data feed from the ORCHID system at Oxford to the GP in-hours syndromic surveillance system was established for routine surveillance. Population coverage increased from 6.8 to 18.8 million patients, representative of the English population. More granular data are available at postcode district and lower layer super output level (LSOA), providing better geographical and sociodemographic coverage, and enabling the investigation of social inequalities on conditions diagnosed in primary care. New indicators can be implemented rapidly in response to new public health threats. For decades the RCGP has run sentinel surveillance with integrated virology and now RCGP data are being used for syndromic surveillance. The addition of ORCHID data to the GP in-hours syndromic surveillance system has improved the utility of the system. There is better population coverage, additional syndromic indicators, and greater ability to interrogate the data. The new system will provide enhanced information to support public health in England and highlights the value of GP records for use in protecting the health security of the nation.