SNOMED CT Concept Hierarchies for Sharing Definitions of Clinical Conditions Using Electronic Health Record Data

Willett, Duwayne L; Kannan, Vaishnavi; Chu, Ling; Buchanan, Joel R.; Velasco, Ferdinand; Clark, John D.; Fish, Jason; Ortuzar, Adolfo R.; Youngblood, Josh E.; Bhat, Deepa; Basit, Mujeeb A.

doi:10.1055/s-0038-1668090

Cited by 36 publications

(31 citation statements)

References 34 publications

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“…Many networks encompass a variety of EHRs. Since the designated interoperability language between EHRs for diagnoses (conditions) is SNOMED CT, employing SNOMED CT value sets enables EHR-agnostic consistent definition of subpopulations for registries, clinical decision support to promote best practices within the EHR, care gap closure, and quality measurement [9]. This provides analytic interoperability across disparate EHRs even if using clinical terminologies from different vendors.…”

Section: Discussionmentioning

confidence: 99%

“…Those subtype relationships can be a close match to clinicians’ thinking about clinical phenotypes and the subtypes of conditions they wish to be included or excluded. In a report on 125 such hierarchy-based value sets, we’ve shown they also are simple to create in an EHR and employ in an analytic data warehouse [9].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, in most EHRs clinicians enter patient conditions onto their Problem List by using either SNOMED CT directly, or, more commonly, a clinician-friendly clinical terminology premapped to SNOMED CT concepts. Both methods enable preserving a higher level of clinical fidelity and relevant clinical detail than ICD does due to the enhanced clinical specificity of SNOMED CT [9].…”

Section: Introductionmentioning

confidence: 99%

“…SNOMED CT intensional value sets, by contrast, are rule-based and leverage the polyhierarchy structure of SNOMED CT. That is, one can include or exclude an entire “tree” of real-world condition subtypes via a single reference to a SNOMED CT concept and all its descendants. Combining such tree references with simple Boolean logic (or with SNOMED CT Expression Constraint Language) enables efficient definition of a desired clinical phenotype [9]. For instance, osteoporosis and all of its subtypes can be defined by reference to one SNOMED CT concept (SCT ID 64859006 Osteoporosis) and all its descendants.…”

Section: Introductionmentioning

confidence: 99%

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SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets

et al. 2019

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BackgroundDefining clinical phenotypes from electronic health record (EHR)–derived data proves crucial for clinical decision support, population health endeavors, and translational research. EHR diagnoses now commonly draw from a finely grained clinical terminology—either native SNOMED CT or a vendor-supplied terminology mapped to SNOMED CT concepts as the standard for EHR interoperability. Accordingly, electronic clinical quality measures (eCQMs) increasingly define clinical phenotypes with SNOMED CT value sets. The work of creating and maintaining list-based value sets proves daunting, as does insuring that their contents accurately represent the clinically intended condition.ObjectiveThe goal of the research was to compare an intensional (concept hierarchy-based) versus extensional (list-based) value set approach to defining clinical phenotypes using SNOMED CT–encoded data from EHRs by evaluating value set conciseness, time to create, and completeness.MethodsStarting from published Centers for Medicare and Medicaid Services (CMS) high-priority eCQMs, we selected 10 clinical conditions referenced by those eCQMs. For each, the published SNOMED CT list-based (extensional) value set was downloaded from the Value Set Authority Center (VSAC). Ten corresponding SNOMED CT hierarchy-based intensional value sets for the same conditions were identified within our EHR. From each hierarchy-based intensional value set, an exactly equivalent full extensional value set was derived enumerating all included descendant SNOMED CT concepts. Comparisons were then made between (1) VSAC-downloaded list-based (extensional) value sets, (2) corresponding hierarchy-based intensional value sets for the same conditions, and (3) derived list-based (extensional) value sets exactly equivalent to the hierarchy-based intensional value sets. Value set conciseness was assessed by the number of SNOMED CT concepts needed for definition. Time to construct the value sets for local use was measured. Value set completeness was assessed by comparing contents of the downloaded extensional versus intensional value sets. Two measures of content completeness were made: for individual SNOMED CT concepts and for the mapped diagnosis clinical terms available for selection within the EHR by clinicians.ResultsThe 10 hierarchy-based intensional value sets proved far simpler and faster to construct than exactly equivalent derived extensional value set lists, requiring a median 3 versus 78 concepts to define and 5 versus 37 minutes to build. The hierarchy-based intensional value sets also proved more complete: in comparison, the 10 downloaded 2018 extensional value sets contained a median of just 35% of the intensional value sets’ SNOMED CT concepts and 65% of mapped EHR clinical terms.ConclusionsIn the EHR era, defining conditions preferentially should employ SNOMED CT concept hierarchy-based (intensional) value sets rather than extensional lists. By doing so, clinical guideline and eCQM authors can more readily engage specialists in vetting condition subtypes to i...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets

et al. 2019

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“…So, numbers "0," "1," and "1" mean a diagnosis of "Strain of the right Achilles tendon." SNOMED-CT (SNOMED Clinical Terms) (WILLETT et al, 2018) is an inclusive, multilingual clinical healthcare terminology. It is a resource with inclusive and scientifically approved clinical content that allows the consistent and processable description of the clinical content in electronic health records.…”

Section: Icd(international Classification Of Diseases) Is Made Of a Mmentioning

confidence: 99%

Similarity algorithms for Heterogeneous Information Networks

Ribeiro¹

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Most real systems can be represented as a graph of multi-typed components with a large number of interactions. Heterogeneous Information Networks (HIN) are interconnected structures with data of multiple types which support the rich semantic meaning of structural types of nodes and edges. In HIN, different information can be presented using different types and forms of data, but may have the same or complementary information. So there is knowledge to be discovered. Terminology Knowledge Structures (TKS) como terminology products can be sources of linguistic representations and knowledge to be used for enrich the HIN and create a measure of similarity to extract the documents similar to each other, even if these documents are of different types (for example, finding medical articles that are in some way related to medical records). In this sense, this work presents the creation of a Heterogeneous Information Network using classical similarity measures, terminology products and the attributes of documents by an algorithm called NetworkCreator. As a contribution, an algorithm called NetworkCreator was created that from medical records and scientific articles builds an HIN with related documents, was also created. The algorithm HeteSimTKSQuery to calculate similarity measures between documents of different types which are in HIN. Terminology products with meta-paths were also explored. The results were efficient, reaching on average 89% accuracy in some cases. However, it is important to note that all HIN presented in the researched literature were constructed only by one type of data coming from a single source. The results show that the algorithms are feasible to solve the problems of HIN construction and search for similarity. But it still needs improvement. In the future one can work on detection in the detection of node granularity of these networks and try to reduce the network construction runtime.

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Multiple emergency department encounters for acute musculoskeletal presentation with an existing mental health diagnosis

Arora,

Elliott,

Pourkazemi

et al. 2023

Clinical Case Reports

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Key Clinical MessageReconceptualising acute Musculoskeletal (MSK) injuries with both stress‐ and tissue‐ based factors is required to consider prior influences of mental health disorders on acute persistent MSK pain presentations. This report describes repeated emergency presentations of an individual with acute persistent MSK pain in their twenties living with mental health. Their mental health diagnoses included depression, mood disorders, and anorexia nervosa. This person also had mental health related inpatient admissions that were not captured under the retrospective record review for a large district hospital emergency department using the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) classification system. This case report attempts to demonstrate that improving the understanding of preexisting vulnerabilities and mental health diagnoses may assist with informing healthcare design to develop specialised care pathways for acute injury presentations within triage settings.

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SNOMED CT Concept Hierarchies for Sharing Definitions of Clinical Conditions Using Electronic Health Record Data

Cited by 36 publications

References 34 publications

SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets

SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets

Similarity algorithms for Heterogeneous Information Networks

Multiple emergency department encounters for acute musculoskeletal presentation with an existing mental health diagnosis

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