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Fundamentals of snomed ct

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Provides an overview of SNOMED CT concentrating on its fundamentals, advantages and disadvantages of use, how its logical model is designed, the relationships and attribute name-value pairing, and pre- & post-coordinated expressions

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Fundamentals of snomed ct

  1. 1. SNOMED CT FUNDAMENTALS Dr SB Bhattacharyya MBBS, MBA, FCGP Member, National EHR Standardisation Committee, MoH&FW, GoI Member, IMA Standing Committee for IT, IMA Headquarters Member, Health Informatics Sectional Committee, MHD 17, BIS President (2010 – 2011), IAMI
  2. 2. SNOMED CT SNOMED CT is a semantically interoperable polyhierarchical subtype multi-lexical clinical terminology system that is able to deliver robust benefits to the health care services. Dr SB Bhattacharyya© 2
  3. 3. SNOMED CT SNOMED CT is a reference terminology for clinical terms and consists of terms used in health and health care that range from abscess to zygote that can code contents belonging to all parts of a clinical record within the Subjective-Objective-Assessment-Plan (SOAP) paradigm Dr SB Bhattacharyya© 3
  4. 4. SNOMED CT SNOMED CT uses description logic (a formalism for ontology aka knowledge representation) to encode clinical concepts for interpretation and further action by computer systems Dr SB Bhattacharyya© 4
  5. 5. SNOMED CT A code system that enables machines to “interpret” clinical record contents Dr SB Bhattacharyya© 5
  6. 6. SNOMED CT Basically a clinical dictionary for machines Dr SB Bhattacharyya© 6
  7. 7. Why SNOMED CT? ■ Natural language for end-users – Doctors – Dentists – Nurses – Paramedics ■ Since clinical documentation systems document the clinical thoughts of the observers, a concept-based system works best, not a term-based or classification-based one ■ For clinical analytics, evidence based medicine, clinical decision support, etc., the correct idea is vital, not a prefixed term Dr SB Bhattacharyya© 7
  8. 8. SNOMED CT In the full version of SNOMED CT 2014 July International Release there are: Components Active Total Concepts 300,751 403,836 Description (terms) 1,037,903 1,206,870 Relationships 908,668 2,412,104 Term : Concept Ratio 3.45+ – Term : Relationships Ratio 3.02+ – Dr SB Bhattacharyya© 8
  9. 9. Advantages Through its structure and support for refset (reference sets) or subsets, extensions and maps it permits: ■ Local additions of concepts and terms that are not present in the International Release ■ Mapping to other health care codes like ICD, LOINC, local codes (aka interface terminology) ■ Clinical pathway automation ■ Running extremely rich clinical analytics even on free text unstructured data Dr SB Bhattacharyya© 9
  10. 10. Advantages ■ Users get to choose that particular term or a set of terms that they are most comfortable with in their own language/dialect ■ Users can express their clinical thoughts very precisely using expressions ■ Many of the record management tasks can be automated leading to extremely user-friendly richly-ergonomic systems ■ Rule-based alerts and warnings can be triggered more accurately Dr SB Bhattacharyya© 10
  11. 11. Disadvantages ■ Learning the clinical terminology systems requires patience and perseverance – although this is applicable only to system designers and architects ■ Composing and querying expressions is a technical challenge –this too is again only applicable to system designers and architects Dr SB Bhattacharyya© 11
  12. 12. SNOMED CT Components – Core ■ Concepts – Concept Ids :: meaningless machine-processable numbers that act as unique identifiers for each ■ Descriptions (Terms) – Human-processable terms :: each concept has a uniquely human-readable Fully Specified Name (FSN) and one preferred term for each language/dialect apart from synonyms ■ Relationships – Between concepts: source to destination :: provides the “intelligence” for machines to process Dr SB Bhattacharyya© 12
  13. 13. SNOMED CT Concepts ■ SNOMED CT is a concept-based code system based on the principle “one code per concept and one concept per code”, where concept is a thought or idea ■ Every clinical term is “expressed” as a concept and represents a “unit of meaning” ■ When a user chooses a clinical term, he is choosing the particular term that best expresses his thought or idea regarding a particular item, which could be a complaint or an item of past history or family history or an observation related to physical examination or investigation or diagnosis or treatment, etc. Dr SB Bhattacharyya© 13
  14. 14. SNOMED CT Terms ■ Fully Specified Name (FSN) – identified by having the term suffixed by its top-level hierarchy enclosed in parenthesis thus - “(disorder)”, “(body structure)”, “(procedure)”, etc. ■ Preferred Term (PT) – one for each language/dialect ■ Acceptable (Terms) – synonyms Dr SB Bhattacharyya© 14
  15. 15. SNOMED CT Relationships ■ Used to link concepts to help define it ■ Other than the root concept, a single concept will have at least one subtype relationship link and none-to-many (0…1) attribute relationship links that provide a unique definition for the concept ■ Provides the “intelligence” that enables IT systems to “comprehend” the meaning of a concept so that the most appropriate action may be initiated using rule-based, machine learning algorithms, etc. Dr SB Bhattacharyya© 15
  16. 16. SNOMED CT Logical Model Dr SB Bhattacharyya© 16
  17. 17. SNOMED CT Relationships Infective pneumonia (disorder) Lung (body structure) Infection (disorder)Respiratory disease (disorder) Virus (organism) Viral pneumonia (disorder) Dr SB Bhattacharyya© 17
  18. 18. Concept Model ■ Shaped in an inverted tree fashion with root concept at top representing the coarsest concept to the finest leaf at the bottom representing the finest concept ■ Each concept has a corresponding definition that is either primitive or fully defined and expressed using ■ Each concept (other than the root concept,) must have at least one | is a | meronomic relationship with another concept that is its supertype parents or ancestors ■ Each concept may have none-to-many (0…n) attribute “has a” relationship with other concepts ■ Each attribute has a name-value pairing in the domain name = range value format Dr SB Bhattacharyya© 18
  19. 19. SNOMED CT Concept Model (schematic) Dr SB Bhattacharyya© 19
  20. 20. SNOMED CT – Attribute Value-Range DOMAIN (hierarchy) ATTRIBUTE RANGE (concept of hierarchy) << This concept or one of its descendants | Clinical finding | | FINDING SITE | << 442083009 | Anatomical or acquired body structure | | Body structure | | LATERALITY | < 182353008 |Side| < Descendants only. Not the concept itself Dr SB Bhattacharyya© 20
  21. 21. SNOMED CT Expressions ■ Clinical thoughts are “expressed” using SNOMED CT ■ Actual “code” of SNOMED CT, ideally hidden from all users and handled exclusively by the system ■ Consists of either of the following two formats – ConceptId – ConceptId | Description (Term) | ■ Needs to adhere to compositional grammar rules and expression constraint syntax Dr SB Bhattacharyya© 21
  22. 22. SNOMED CT Expressions ■ Pre-coordinated – Coordination already done – Representation of a clinical meaning using a single concept identifier is referred to as precoordination ■ Post-coordinated – Coordination done on-the-fly by the users choosing various rules-based alternatives – Representation of a clinical meaning using a combination of two or more concept identifiers is referred to as postcoordination Dr SB Bhattacharyya© 22
  23. 23. Pre-coordinated Expression – Example Clinical thought: Viral pneumonia Expressed as: ConceptId Term 75570004 viral pneumonia The advocated format can be any one of the following two: 1. 75570004 2. 75570004 | viral pneumonia | Dr SB Bhattacharyya© 23
  24. 24. Post-coordinated Expression – Example Clinical thought: Family history of hypertension No single pre-coordinated term exists for this. The clinical thought needs to be expressed as a composite of the following individual pre-coordinated concepts: ConceptId Term 281666001 family history of disorder 246090004 associated finding 64572001 disease 38341003 hypertension Dr SB Bhattacharyya© 24
  25. 25. Post-coordinated Expression – Example [contd.] A postcoordinated expression based on the concept 281666001 | family history of disorder | can be used to record a family history of any disorder. The definition of this concept includes 246090004 | associated finding | = 64572001 | disease | and the value of this attribute can be refined to 38341003 | hypertension | (which is a subtype descendant of 64572001 | disease |). Dr SB Bhattacharyya© 25
  26. 26. Post-coordinated Expression – Example [contd.] Therefore, the following postcoordinated expression can be created as below and used to represent this family history – 281666001 | family history of disorder | : 246090004 | associated finding | = 38341003 | hypertension | This can also be written as – 281666001 : 246090004 = 38341003 Dr SB Bhattacharyya© 26
  27. 27. Greatest Impact Area Clinical data analytics ■ All aspects of a medical record can now be analysed using computers ■ This leads to the discovery of many hidden facts that can be statistically correlated and ■ Using Machine Learning techniques predictive analytics can be used to support personalised medicine Dr SB Bhattacharyya© 27
  28. 28. Introduction to SNOMED CT by Dr SB Bhattacharyya available at ■ www.amazon.com/Introduction-SNOMED-CT-S-B- Bhattacharyya/dp/9812878939/ref=sr_1_1?ie=UTF8&qid=1453269681&sr=8- 1&keywords=introduction+to+snomed+ct – hardcopy only ■ www.amazon.in/Introduction-SNOMED-CT-2016- Bhattacharyya/dp/9812878939/ref=sr_1_1?ie=UTF8&qid=1453269722&sr=8- 1&keywords=introduction+to+snomed+ct – hardcopy only ■ www.springer.com/gp/book/9789812878939 – ebook and hardcopy ■ link.springer.com/book/10.1007/978-981-287-895-3 – chapter-wise online access only Dr SB Bhattacharyya© 28
  29. 29. sbbhattacharyya@gmail.com Dr SB Bhattacharyya© 29
  30. 30. Dr SB Bhattacharyya© 30 Thanks!

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