Fundamentals of snomed ct

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

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.

Dr SB Bhattacharyya© 2

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


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+ –


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


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


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


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


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.


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


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.


SNOMED CT Logical Model


SNOMED CT Relationships

Infective pneumonia (disorder)

Lung (body structure)

Infection (disorder)Respiratory disease (disorder)

Virus (organism)

Viral pneumonia (disorder)


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 valueformat


SNOMED CT Concept Model (schematic)


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


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


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 |


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


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


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


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


http://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

http://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

http://www.springer.com/gp/book/9789812878939

http://link.springer.com/book/10.1007/978-981-287-895-3

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Fundamentals of snomed ct

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