SNOMED CT is a clinical terminology system that provides robust benefits to healthcare. It is a reference terminology consisting of clinical terms defined using description logic. SNOMED CT allows natural language expression of clinical thoughts and automated clinical processes through its polyhierarchical structure and relationships between concepts. While learning the complex terminology requires effort, when implemented SNOMED CT enables precision in clinical documentation, interoperability, and analytics.

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. 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.
■ SNOMED CT is a reference terminology for clinical terms and
consists of terms used in health and health care.
■ SNOMED CT uses description logic (a formalism for ontology
aka knowledge representation) that are mainly characterised
by a set of construction rules that allow defining complex
concepts and their associated roles using atomic ones thereby
making the codes constructed using it immediately and
automatically available for interpretation and further action
by computer systems
Dr SB Bhattacharyya© 2

3. 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© 3

4. 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© 4

5. 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© 5

6. 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© 6

7. 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© 7

8. 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© 8

9. 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© 9

10. 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© 10

11. 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© 11

12. SNOMED CT Logical Model
Dr SB Bhattacharyya© 12

13. SNOMED CT Relationships
Infective pneumonia (disorder)
Lung (body structure)
Infection (disorder)Respiratory disease (disorder)
Virus (organism)
Viral pneumonia (disorder)
Dr SB Bhattacharyya© 13

14. 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© 14

15. SNOMED CT Concept Model (schematic)
Dr SB Bhattacharyya© 15

16. 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© 16

17. 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© 17

18. 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© 18

19. 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© 19

20. 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© 20

21. 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© 21

22. 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© 22

23. 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© 23

24. 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© 24

25. sbbhattacharyya@gmail.com
Dr SB Bhattacharyya© 25

26. Dr SB Bhattacharyya© 26
Thanks!