A simple web-based interface for advanced SNOMED CT queries
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SNOMED CT – as the most comprehensive biomedical ontology – has the potential to utilize semantic query methods that operate on the defining attributes of the concepts. This type of semantic querying is widely used, and some of the query languages already extended the attribute constraints with the option for limited lexical and metadata search criteria. Since the introduction of RF2 the expressibility of SNOMED CT can increase, and various national extensions make use of this extensibility by adding specific description logic features that are relevant for their content. An example for this is the Singapore Drug Dictionary that is based on the SNOMED CT concept model, but applies additional attribute types. The standard query languages are not powerful enough for such content. This demonstration introduces a search interface that allows querying both standard SNOMED CT content as well as pharmaceutical extensions that utilize optional description logic extensions. These advanced queries are created by terminologists with an understanding of SNOMED CT. End-users can then use these queries to browse relevant subsets of the terminology appropriate for their use case. For example, clinicians can browse only drugs that are clinically relevant, while regulators can constrain their searches to controlled substances. The tool also allows early validation of intensional reference set content, without having to implement and publish the reference sets. Practical examples using an online browser (Snow Owl Web) will highlight challenges and lessons learnt when working with real-world clinicians and regulators lacking SNOMED CT training. Please see our website http://b2i.sg for further information.
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Abstract:
The availability of semantically rich electronic health records utilizing SNOMED CT as a reference terminology continues to grow, providing new opportunities to improve patient care and reduce costs. However, traditional data warehouses struggle to unleash the full semantic meaning within the health records, as the data is built around a limited number of concepts.
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Abstract:
A critical enabler in the journey towards semantic interoperability in Singapore is the Singapore "˜Logical Information Model' (LIM). The LIM is a model of the healthcare information shared within Singapore, and is defined as a set of reusable "˜archetypes' for each clinical concept (e.g. Problem/Diagnosis, Pharmacy Order). These archetypes are then constrained and composed into "˜templates' to support specific use cases.
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A simple web-based interface for advanced SNOMED CT queries
- 1. A simple web-‐based interface for advanced SNOMED CT queries Orsolya Bali, Brandon Ulrich – B2i Healthcare IHTSDO Showcase 2014.10.30 -‐ Amsterdam
- 2. Why search or query SNOMED CT content? Common use cases include: • Terminology authoring • Reference set authoring – intensional sets • Mappings • Terminology binding • EHR queries • ValidaOon/QA 1
- 3. Challenges • Over 300,000 concepts • Lexical search may be insufficient – Different meanings could sOll carry the same descripOon • SemanOc search requires comprehensive knowledge of the concept model • Various component properOes, extendable by RF2 • Average users have high-‐level or only vague understanding of SNOMED CT 2
- 4. A search interface must be • … powerful enough to cater for all the relevant a[ributes (including lexical, semanOc and metadata searches). • …yet simple and user friendly. 3
- 5. Snow Owl Meaningful Query (MQ) • Online browser for SNOMED CT and extensions – Advanced search – Filtered hierarchy • EHR datastore • SemanOc query engine to extract the semanOcs captured in EHRs • Terminology server integraOon 4
- 6. DEMO 5
- 7. Introduce filters • Concept, DescripOon, Property, Refset and Mapping filters • Some relevant filters: property-‐value, module, descripOon term, reference set, mapping 6
- 8. Features to check out • Create a simple filter • Open a few more complex filters • Browse view • Filtered browse view 7
- 9. The advanced search interface 8
- 10. Lexical search in a specific descripMon type To return all concepts that have an acOve SG English preferred term containing the word tetralogy. 9
- 11. Search by aNribute (relaMonship) To return all concepts, that have a procedure device type of a[ribute, or any of its subtypes (eg. direct device), with the value of arthroscope. 10
- 12. Search by aNribute (concrete domain) To return all concepts with the acOve ingredient of paracetamol, whose strength is between 50 and 500 mg. 11
- 13. Complex query The below query combines hierarchical, a[ribute, lexical and metadata searches, using logical and, logical or operators and exclusion. 12
- 14. Search in reference set 13 To return members of a reference set.
- 15. Search by mapping To search concepts by their maps. The below example returns all concepts, that are mapped to the specified ATC code. 14
- 17. Quick filter hierarchy 16
- 18. Apply a pre-‐authored filter 17
- 20. Dashboard 19
- 22. THANK YOU! 21