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.

Improving the interoperability of drugs terminologies: Infusing local standardization with an international perspective.

OBJECTIVES: The objective of this study is to describe how OCRx (Canadian Drug Ontology) has been built to address the dual need for local drug information integration in Canada and alignment with international standards requirements. METHODS: This paper delves into (i) the implementation efforts to meet the Identification of Medicinal Product (IDMP) requirements in OCRx, alongside the ontology update strategy, (ii) the structure of the ontology itself, (iii) the alignment approach with several reference Knowledge Organization Systems, including SNOMED CT, RxNorm, and the list of "Code Identifiant de Spécialité" (CIS-Code), and (iv) the look-up services developed to facilitate its access and utilization. RESULTS: Each OCRx release contains two distinct versions: the full and the up-to-date version. The full version encompasses all drugs with a DIN code sanctioned by Health Canada, while the up-to-date version is limited to drugs currently marketed in Canada. In the last release of OCRx, the full version comprises 162,400 classes; meanwhile, the up-to-date version consists of 36,909 classes. In terms of mappings with OCRx, substances in RxNorm and SNOMED CT fall below 40%, registering at 37% and 22% respectively. Meanwhile, mappings for CIS-Code achieve coverage of 61%. The strength mappings are notably low for RxNorm at 40% and for CIS-code at 28%. This affects the mapping of clinical drugs, which are predominantly alignable through post-coordinated expressions: 56% for RxNorm, 80% for SNOMED CT, and 35% for CIS-Code. The main support service of OCRx is a look-up service known as PaperRx that displays OCRx's entities based on description logic queries (DL-queries) performed through the classified structure of OCRx. The look-up services also contain a SPARQL endpoint, an OCRx OWL file downloader, and a RESTful API. DISCUSSION: The OCRx ontology demonstrates a significant effort towards integrating Canadian drug information with international standards. However, there are areas for improvement. In the future, our focus will be on refining the structure of OCRx for better classification capability and improvement of dosage conversion. Additionally, we aim to harness OCRx in constructing an ontology-based annotator, setting our sights on its deployment in real-world data integration scenarios.

Transformer-based time-to-event prediction for chronic kidney disease deterioration.

OBJECTIVE: Deep-learning techniques, particularly the Transformer model, have shown great potential in enhancing the prediction performance of longitudinal health records. Previous methods focused on fixed-time risk prediction, however, time-to-event prediction is often more appropriate for clinical scenarios. Here, we present STRAFE, a generalizable survival analysis Transformer-based architecture for electronic health records. MATERIALS AND METHODS: The input for STRAFE is a sequence of visits with SNOMED-CT codes in OMOP-CDM format. A Transformer-based architecture was developed to calculate probabilities of the occurrence of the event in each of 48 months. Performance was evaluated using a real-world claims dataset of over 130 000 individuals with stage 3 chronic kidney disease (CKD). RESULTS: STRAFE showed improved mean absolute error (MAE) compared to other time-to-event algorithms in predicting the time to deterioration to stage 5 CKD. Additionally, STRAFE showed an improved area under the receiver operating curve compared to binary outcome algorithms. We show that STRAFE predictions can improve the positive predictive value of high-risk patients by 3-fold. Finally, we suggest a novel visualization approach to predictions on a per-patient basis. DISCUSSION: Time-to-event predictions are the most appropriate approach for clinical predictions. Our deep-learning algorithm outperformed not only other time-to-event prediction algorithms but also fixed-time algorithms, possibly due to its ability to train on censored data. We demonstrated possible clinical usage by identifying the highest-risk patients. CONCLUSIONS: The ability to accurately identify patients at high risk and prioritize their needs can result in improved health outcomes, reduced costs, and more efficient use of resources.

International Patient Summary Terminology.

SNOMED CT is a comprehensive medical ontology used in health care sectors across the world covering a wide range of concepts that support diversity at the point of healthcare. However, not all these concepts are needed for every use case; it is better to concentrate on those parts that apply to the particular application while preserving the meaning of relevant concepts. This paper considers the application of a novel subontology extraction method to create a new resource, called the IPS terminology, which functions as a standalone ontology with the same features as SNOMED CT, but is designed for cross-border patient care. The IPS terminology has been released for free use under an open license, with the intention of promoting interoperability of health information worldwide.

Setting the Scene to Link SNOMED CT to Realism-Based Ontologies.

In a proof of concept study, we assessed the feasibility of designing a first-order logic (FOL) framework capable of translating SNOMED CT's terminological view on patient data as referencing concepts, into the realism-based view of the Basic Formal Ontology and the Ontology for General Medical Science according to which patient data represent instances of types. Because within the subject domain of this study, SNOMED CT's terminological coverage was excellent, and its EL++ axioms can be automatically translated into FOL as well as the antecedent part of bridging axioms between SNOMED CT and realism-based ontologies, we conclude that this is an area of R&D that deserves further attention and that may lead to new ways of federating terminologies with ontologies.

Current Status of SNOMED CT National Extensions and Terminology Managements.

We reviewed and surveyed 15 SNOMEDCT national member countries for SNOMED CT national extensions and terminology managements. We found that national extensions were used for adding new contents, developing reference sets, translating, and mapping with other classification system; and terminology management varies in composition and content due to healthcare environment of each member country, eHealth strategy, and infrastructure of national release centers.

Obtaining clinical term embeddings from SNOMED CT ontology.

Clinical term embeddings are traditionally obtained using corpus-based methods, however, these methods cannot incorporate knowledge about clinical terms which is already present in medical ontologies. On the other hand, graph-based methods can obtain embeddings of clinical concepts from ontologies, but they cannot obtain embeddings for clinical terms and words. In this paper, a novel method is presented to obtain embeddings for clinical terms and words from the SNOMED CT ontology. The method first obtains embeddings of clinical concepts from SNOMED CT using a graph-based method. Next, these concept embeddings are used as targets to train a deep learning model to map clinical terms to concepts embeddings. The learned model then provides embeddings for clinical terms and words as well as maps novel clinical terms to their embeddings. The embeddings obtained using the method out-performed corpus-based embeddings on the task of predicting clinical term similarity on five benchmark datasets. On the clinical term normalization task, using these embeddings simply as a means of computing similarity between clinical terms obtained accuracy which was competitive to methods trained specifically for this task. Both corpus-based and ontology-based embeddings have a limitation that they tend to learn similar embeddings for opposite or analogous terms. To counter this, we also introduce a method to automatically learn patterns that indicate when two clinical terms represent the same concept and when they represent different concepts. Supplementing the normalization process with these patterns showed improvement. Although clinical term embeddings obtained from SNOMED CT incorporate ontological knowledge which is missed by corpus-based embeddings, they do not incorporate linguistic knowledge which is needed for sentence-based tasks. Hence combining ontology-based embeddings with corpus-based embeddings is an avenue for future work.

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

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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). RESULTS: 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). CONCLUSION: 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.

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

OBJECTIVE: To evaluate the representation of environmental concepts associated with health impacts in standardized clinical terminologies. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Mapping Hungarian procedure codes to SNOMED CT.

BACKGROUND: Data 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. METHODS: We 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. RESULTS: We 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. CONCLUSIONS: The 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.

The suitability of UMLS and SNOMED-CT for encoding outcome concepts.

OBJECTIVE: Outcomes are important clinical study information. Despite progress in automated extraction of PICO (Population, Intervention, Comparison, and Outcome) entities from PubMed, rarely are these entities encoded by standard terminology to achieve semantic interoperability. This study aims to evaluate the suitability of the Unified Medical Language System (UMLS) and SNOMED-CT in encoding outcome concepts in randomized controlled trial (RCT) abstracts. MATERIALS AND METHODS: We iteratively developed and validated an outcome annotation guideline and manually annotated clinically significant outcome entities in the Results and Conclusions sections of 500 randomly selected RCT abstracts on PubMed. The extracted outcomes were fully, partially, or not mapped to the UMLS via MetaMap based on established heuristics. Manual UMLS browser search was performed for select unmapped outcome entities to further differentiate between UMLS and MetaMap errors. RESULTS: Only 44% of 2617 outcome concepts were fully covered in the UMLS, among which 67% were complex concepts that required the combination of 2 or more UMLS concepts to represent them. SNOMED-CT was present as a source in 61% of the fully mapped outcomes. DISCUSSION: Domains such as Metabolism and Nutrition, and Infections and Infectious Diseases need expanded outcome concept coverage in the UMLS and MetaMap. Future work is warranted to similarly assess the terminology coverage for P, I, C entities. CONCLUSION: Computational representation of clinical outcomes is important for clinical evidence extraction and appraisal and yet faces challenges from the inherent complexity and lack of coverage of these concepts in UMLS and SNOMED-CT, as demonstrated in this study.

A health terminological system for inherited retinal diseases: Content coverage evaluation and a proposed classification.

PURPOSE: To present a classification of inherited retinal diseases (IRDs) and evaluate its content coverage in comparison with common standard terminology systems. METHODS: In this comparative cross-sectional study, a panel of subject matter experts annotated a list of IRDs based on a comprehensive review of the literature. Then, they leveraged clinical terminologies from various reference sets including Unified Medical Language System (UMLS), Online Mendelian Inheritance in Man (OMIM), International Classification of Diseases (ICD-11), Systematized Nomenclature of Medicine (SNOMED-CT) and Orphanet Rare Disease Ontology (ORDO). RESULTS: Initially, we generated a hierarchical classification of 62 IRD diagnosis concepts in six categories. Subsequently, the classification was extended to 164 IRD diagnoses after adding concepts from various standard terminologies. Finally, 158 concepts were selected to be classified into six categories and genetic subtypes of 412 cases were added to the related concepts. UMLS has the greatest content coverage of 90.51% followed respectively by SNOMED-CT (83.54%), ORDO (81.01%), OMIM (60.76%), and ICD-11 (60.13%). There were 53 IRD concepts (33.54%) that were covered by all five investigated systems. However, 2.53% of the IRD concepts in our classification were not covered by any of the standard terminologies. CONCLUSIONS: This comprehensive classification system was established to organize IRD diseases based on phenotypic and genotypic specifications. It could potentially be used for IRD clinical documentation purposes and could also be considered a preliminary step forward to developing a more robust standard ontology for IRDs or updating available standard terminologies. In comparison, the greatest content coverage of our proposed classification was related to the UMLS Metathesaurus.

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

OBJECTIVE: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.

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.

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.

In support of interoperability: A laboratory perspective.

Healthcare in the United States has become increasingly digital since the passage of the HITECH Act in 2009. As a result, there is a growing need to optimize healthcare IT to allow for the interoperable exchange of data. As a result, the Office of the National Coordinator for Health IT has implemented their Final Rule for the 21st Century Cures Act. This requires certified health IT systems to use modernized messaging standards for the safe and secure exchange of data within health information networks and also requires the use of terminology standards including LOINC, SNOMED CT, and UCUM for coding clinical and laboratory data. Given the critical importance of laboratory results in the delivery of healthcare, laboratorians must become familiar with these principles of interoperability. Their clinical laboratory expertise is needed to appropriately structure and code test results to safeguard against improper aggregation or misinterpretation by downstream users and systems.

Logical definition-based identification of potential missing concepts in SNOMED CT.

BACKGROUND: Biomedical ontologies are representations of biomedical knowledge that provide terms with precisely defined meanings. They play a vital role in facilitating biomedical research in a cross-disciplinary manner. Quality issues of biomedical ontologies will hinder their effective usage. One such quality issue is missing concepts. In this study, we introduce a logical definition-based approach to identify potential missing concepts in SNOMED CT. A unique contribution of our approach is that it is capable of obtaining both logical definitions and fully specified names for potential missing concepts. METHOD: The logical definitions of unrelated pairs of fully defined concepts in non-lattice subgraphs that indicate quality issues are intersected to generate the logical definitions of potential missing concepts. A text summarization model (called PEGASUS) is fine-tuned to predict the fully specified names of the potential missing concepts from their generated logical definitions. Furthermore, the identified potential missing concepts are validated using external resources including the Unified Medical Language System (UMLS), biomedical literature in PubMed, and a newer version of SNOMED CT. RESULTS: From the March 2021 US Edition of SNOMED CT, we obtained a total of 30,313 unique logical definitions for potential missing concepts through the intersecting process. We fine-tuned a PEGASUS summarization model with 289,169 training instances and tested it on 36,146 instances. The model achieved 72.83 of ROUGE-1, 51.06 of ROUGE-2, and 71.76 of ROUGE-L on the test dataset. The model correctly predicted 11,549 out of 36,146 fully specified names in the test dataset. Applying the fine-tuned model on the 30,313 unique logical definitions, 23,031 total potential missing concepts were identified. Out of these, a total of 2,312 (10.04%) were automatically validated by either of the three resources. CONCLUSIONS: The results showed that our logical definition-based approach for identification of potential missing concepts in SNOMED CT is encouraging. Nevertheless, there is still room for improving the performance of naming concepts based on logical definitions.

Semi-Automated Approach to Retrieve SNOMED CT Hierarchy of Clinical Terms by Using Terminology Server.

SNOMED CT has an enormous number of clinical concepts and mapping to SNOMED CT is considered as the foundation to achieve semantic interoperability in healthcare. Manual mapping is time-consuming and error-prone thus making this crucial step challenging. In addition, hierarchy retrieval of clinical concepts increases the challenges for the user. Terminology Servers provide an interface, which can be used to automate the process of retrieving data. In this work, it is shown that Snowstorm can significantly improve the efficiency of retrieval process if used with semi-automated workflows.