Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Fundamentals of snomed ct


Published on

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

Published in: Healthcare

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 ■ Bhattacharyya/dp/9812878939/ref=sr_1_1?ie=UTF8&qid=1453269681&sr=8- 1&keywords=introduction+to+snomed+ct – hardcopy only ■ Bhattacharyya/dp/9812878939/ref=sr_1_1?ie=UTF8&qid=1453269722&sr=8- 1&keywords=introduction+to+snomed+ct – hardcopy only ■ – ebook and hardcopy ■ – chapter-wise online access only Dr SB Bhattacharyya© 28
  29. 29. Dr SB Bhattacharyya© 29
  30. 30. Dr SB Bhattacharyya© 30 Thanks!