This presentation discusses the basis of SNOMED CT and how it helps machines to understand the clinical documents that are coded using it

1. Understanding the basis of
SNOMED CT
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. Convergence of Medical Science with
Computer & Information Science – making
electronic clinical documents “smart”
SNOMED CT – Systematised Nomenclature Of Medicine – Clinical Terminology
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3. If you have trouble figuring out what SNOMED CT,
give yourself a break. It is not easy and requires in-
depth understanding of the medical domain, and
basic understanding of some aspects computer,
information and linguistic sciences
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4. What is recorded in clinical documents?
• The entries in a record represent the thoughts of the author had
when about the item
• So, when a doctor records “cough with fever” in chief complaint or
“family history of COPD” in family history or “O/E chest clear” in
physical exam or “influenza” in diagnosis, the entries reflect the
thought the doctor had about chief compliant or family history or
physical exam or diagnosis
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5. How SNOMED CT makes “sense” of what has
been recorded in clinical documents?
• A lexicographer provides the meanings of terms (semasiology)
• A terminologist provides the definitions of concepts using various
terms (onomasiology)
• These definitions are actually the knowledge representation or
ontology (information science)
• Using Description Logics (computer science) these ontologies can be
coded for machine-processing
• Thus machines can be enabled to “understand” concepts
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6. How SNOMED CT makes “sense” of what has
been recorded in clinical documents?
• By matching various synonymous terms to concepts, the same codes
can be made human-readable
• Thus, a human-machine bridge can be created to record and interpret
records
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7. How SNOMED CT makes “sense” of what has
been recorded in clinical documents?
• SNOMED CT provides both the machine-processable and human-
readable codes
• Using it, clinical documents can be coded, making them
unambiguously “understandable” by both machines and humans alike
• Currently, no other clinical code system exists that can accomplish this
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8. How SNOMED CT makes “sense” of what has
been recorded in clinical documents?
• SNOMED CT Expressions are its codes
• These are of two types
• Pre-coordinated
• Post-coordinated
• Pre-coordinated expressions are simple constructs and usually
represent a post-coordinated expression
• Post-coordinated expressions are complex constructs composed
according to the concept model
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9. How SNOMED CT makes “sense” of what has
been recorded in clinical documents?
• The concept model is basically the Description Logic and reflects the
ontology, i.e. knowledge representation, of clinical concepts
• The terms are human-readable “codes” while the concept identifier’s
are the machine-processable codes
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10. How SNOMED CT makes “sense” of what has
been recorded in clinical documents?
• Every concept in SNOMED CT has a definition
• Every concept uses other concepts to help define it, except the root
concept that uses itself
• This definition is available as expressions
• To machines as machine-processable expressions
• To humans as human-readable expressions
• Users can also record their own special concepts by making good use
of this process to define them
• For concepts already defined – pre-coordinated expressions
• For concepts not currently defined – post-coordinated expressions
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11. How SNOMED CT makes “sense” of what has
been recorded in clinical documents?
• Since every data in a clinical document is coded in a machine-
processable and human-readable format (if the latter is missing, it can
be easily generated and presented), the machines can process the
code and “figure out” what it is by reversing the logic of creation of
expression
• Usual logic:
human concepts  create ontology  use DL  expressions
• Reverse logic:
expressions  use DL  “derive” ontology  human concepts
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12. Why use SNOMED CT (instead of, for
example, ICD or LOINC)?
• Querying records can now be made as granular as possible and in a
myriad of ways
• Resultant data sets can reveal very rich information from which
knowledge can be gained and wisdom derived to help deliver ever-
better health care
• Every clinically-relevant part of clinical documents can be coded, the
data from the entire record can be extracted and used for further
action
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13. How SNOMED CT helps in data analytics?
• Since ~70% of analytics involves data preparation for analysis, the
ready-availability of clinical data in machine-processable coded
format makes automated analysis that much closer to reality
• This helps make it possible for making clinically-relevant (right)
information available at the (right) point of care (right) just-in-time
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14. Dr SB Bhattacharyya© 14
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