What is SNOMED CT good for ?

1
What is SNOMED CT good for ?

• Ole Terkelsen
• MD Ph.D.
• Danish National Board of Health

2
Why is there a need for a clinical terminology?

• Electronic Health Records (EHRs) will be
  introduced in the hospitals in this decade
• In the paper records there have always been a
  demand for precise and detailed documentation
• about e.g. the patient's diagnosis and procedures
  performed in relation to the patient
• The same demands exists for EHRs
• The mentioned information can be written in "free
  text" but will in this case not be much easier
  to find than information in paper records

3
Why is there a need for a clinical terminology?

• If possible, it would be rational to structure
  the information i.e. use codes in order to ease
  retrieval
• The primary demands to a coding system that could
  meet the demands would be
• it will have to be highly granulated or detailed
  in order to capture the clinical situations
• it will have to reflect the terms used in the
  clinics
• it will have to contain some kind of definitions
• What coding systems can meet such demands?

4
Why can't we use classifications like ICD-10?

• ICD-10 is a statistical classification that often
  aggregate information at code level e.g.
• C49.0 Malignant neoplasm of connective and soft
  tissue of head, face and neck
• It is therefore not granulated enough
• There are no definitions
• C80 Malignant neoplasm without specification of
  site
• probably means "cancer"
• It is out of date
• C85.0 Lymphosarcoma
• probably means "malignant lymphoma"

5
What terminologies are available?

• Clinical Terms ver. 3 ("Read Codes" v.3)
• SNOMED RT
• SNOMED CT
• Open Galen

• UMLS (Unified Medical Language System) is not a
  terminology but a collection of approximately 130
  classifications and terminologies

6
What is SNOMED CT?

• SNOMED CT is a merge and further development of
  SNOMED RT and Clinical Terms ver. 3
• The largest coherent terminology covering the
  clinical domain

7
A quick journey from the sources of SNOMED
Clinical Terms
1965 SNOP
1983 Read Code Mnemonics (500)
RCGP
1984 Read Code 4-byte (10,000)
1978 SNOMED 45,000
Oxmis
1993 SNOMED International 130,000
1988 Read Code Version 2 (30,000)
2001 SNOMED RT (150,000)
NHS Clinical Terms Version 3 (250,000)
1993 SNOMED International 3.5 156,000
2002 SNOMED Clinical Terms (350,000)
8
What is SNOMED CT?

• Contains
• approximately 300.000 active concepts
• approximately 1 million terms (incl. synonyms)
• 1.5 million relations between the concepts
• Languages English (US and UK), Spanish, German
• In use in USA, soon in England (NHS), trails in
  Denmark and Argentina

9
SNOMED CT's Top-level Hierarchies
10
SNOMED CT database tables
11
Concepts table 350,000 entries

• CONCEPTID CONCEPTSTATUS FULLYSPECIFIEDNAME CTV3ID
  SNOMEDID ISPRIMITIVE
• 74400008 0 Appendicitis (disorder) Xa9C4 D5-46100
  0
• 80146002 0 Appendectomy (procedure) X20Wz P1-57450
  0
• 233604007 0 Pneumonia (disorder) X100E D2-0007F 1
  
• 3716002 0 Goiter (disorder) X76FB DB-80100 1
• 69536005 0 Head structure (body
  structure) Xa1gv T-D1100 1
• 113276009 0 Intestinal structure (body
  structure) Xa1Fr T-50500 1
• 14742008 0 Large intestinal structure (body
  structure) Xa1Fv T-59000 1
• 1236009 0 Duodenal serosa (body
  structure) XU5xL T-58230 1
• 41146007 0 Bacterium (organism) X79pY L-10000 1
• 9861002 0 Streptococcus pneumoniae
  (organism) X73GQ L-25116 1
• 113861009 0 Mycobacterium tuberculosis
  (organism) XU3Q2 L-21907 1
• 373270004 0 Penicillin (substance) XUWFk C-0021D
  1
• 17369002 0 Spontaneous abortion
  (disorder) L04.. D8-04100 1
• 123603008 0 Acute focal hepatitis
  (disorder) XU5xO D5-80300 1

12
Descriptions table ca. 1 mio. synonyms

• DESCRIPTIONID DESC-STATUS CONCEPTID TERM DESCRIPTI
  ONTYPE LANGUAGECODE
• 814894010 0 74400008 Appendicitis (disorder) 3 en
• 123558018 0 74400008 Appendicitis 1 en
• 21274010 0 80146002 Appendectomy (procedure) 3 en
• 132967011 0 80146002 Appendectomy 1 en-US
• 132973012 0 80146002 Appendicectomy 1 en-GB
• 132972019 0 80146002 Excision of appendix 2 en
• 621810017 0 233604007 Pneumonia (disorder) 3 en
• 350049016 0 233604007 Pneumonia 1 en
• 768995016 0 3716002 Goiter (disorder) 3 en
• 7261017 0 3716002 Goiter 1 en-US
• 7267018 0 3716002 Goitre 1 en-GB
• 486646013 0 3716002 Struma - goiter 2 en-US
• 486645012 0 3716002 Struma - goitre 2 en-GB
• 486643017 0 3716002 Swelling of thyroid
  gland 2 en
• 486644011 0 3716002 Thyroid enlargement 2 en

13
Relationships table 1.5 million entries

• RELATIONSHIPID CONCEPTID1 RELATIONSHIPTYPE CONCEPT
  ID2
• 521526024 236209003 363704007 181422007
• 556899029 247994001 363714003 47078008
• 462569022 191910002 123005000 362012001
• 1045543021 190570008 363698007 77637002
• 405306026 147235008 116680003 363662004
• 1800183029 129709009 363714003 278844005
• 1939511022 206126004 246075003 373266007
• 707803022 15410007 363704007 30291003
• 136924025 309574009 116680003 118246004
• 78981022 257819000 116680003 129304002
• 1752936025 315369003 363714003 302147001
• 372287021 172363006 116680003 172359004
• 2038091027 64614001 116680003 39981009
• 152634025 122210004 116680003 104172004
• 1919793025 122279008 260686004 129265001
• 859420029 74319002 123005000 361714009
• 20869021 106424006 116680003 236312003
• 210013026 38169004 116680003 106424006

Is a
14
The architecture of SNOMED CT !
Disorder
A concept based terminology
Tumor
Throat disease
Lung disease
Inflammation
Cancer
Tonsillitis
Pneumonia
Lung cancer
Throat cancer
Benigne tumor in throat
15
SNOMED CT as a multilingual terminology
Fully specified name Appendectomy
(procedure) Appendektomie (Verfahren) Apendicectom
ía (procedimiento) Appendektomi (procedure)
All with the same conceptid 80146002
Modified from David Markvell
16
SNOMED CT as a multilingual terminology
Preferred term Appendectomy Appendicectomy Appende
ktomie Apendicectomía Appendectomi
Synonym Excision of appendix Entfernung des
Wurmfortsatzes Operative Entfernung des
Appendix Escisión del apéndice Operativ fjernelse
af blindtarm
17
SNOMED CT - relations

• Attribute relations

Associated morphology (attribute) Has specimen
(attribute) Specimen source morphology
(attribute) Specimen source topography
(attribute) Specimen source identity
(attribute) Specimen procedure (attribute) Part
of (attribute) Has active ingredient
(attribute) Subject of information
(attribute) Causative agent (attribute) Associated
finding (attribute) Component (attribute) Onset
(attribute) Severity (attribute) Occurrence
(attribute) Episodicity (attribute) Revision
status (attribute) Access (attribute) Approach
(attribute) Method (attribute) Priority
(attribute)
Course (attribute) Using (attribute) Laterality
(attribute) Finding site (attribute) Direct
device (attribute) Direct morphology
(attribute) Direct substance (attribute) Has
focus (attribute) Has intent (attribute) Procedure
site (attribute) Has definitional manifestation
(attribute) Temporally follows (attribute) Indirec
t morphology (attribute) Has interpretation
(attribute) Interprets (attribute) Associated
etiologic finding (attribute) Access instrument
(attribute) Recipient category (attribute) Specime
n substance (attribute) Pathological process
(attribute)
18
SNOMED CT relations
Appendectomy is-a Operation on appendix
is-a Partiel excision of large intestine
procedure-site Appendix structure method
Excision - Action
Bacterial meningitis is-a Infective
meningitis is-a Bacterial infection of central
nervous system finding-site Meninges
structure associated-morphology Inflammation
pathological process Infectious disease
Causative-agent Bacterium (fully defined)
The use of attribute relations follow specific
rules (description logics)
anatomical man
19
Do SNOMED CT meet the demands?

• It is highly granulated and detailed and can
  capture the clinical situations
• It do reflect the terms used in the clinics
• conclusion from clinical trail
• it does contain formal definitions

20
What about statistics and DRG?
21
Handling legacy systems

• Is it possible to map?
• what are the use cases?
• mapping from SNOMED CT to classifications?
• mapping from classifications to SNOMED CT?
• Is it possible to use EHR data directly?
• for statistics?
• for DRG/HRG?
• etc.

22
Is it possible to map?

• what are the use cases?
• mapping from SNOMED CT to classifications?
• mapping from classifications to SNOMED CT?

23
Mapping from SNOMED CT to classifications

• Questions to be asked
• In the following slides ICD-10 is used as an
  example
• How is the structure/architecture of SNOMED CT ?
• How is the structure/architecture of ICD-10 ?
• Can they be aligned ?

24
The architecture of SNOMED CT !
A concept based terminology
25
The architecture of ICD-10

• The basic building blocks are categories
• Groups of up to 10 entries
• The two last mentioned are often
• XNN.8 Other . . .
• XNN.9 . . ., unspecified
• The categories are grouped under headings
• The headings are assembled in chapters

26
The architecture of ICD-10 - Examples

• Apparent rule ICD-10 becomes less specific the
  higher the code number
• The three-character code is never reported to
  registers (at least not in Denmark)
• The XNN.8 and/or XNN.9 therefore appears as
  top-level concepts

27
The architecture of ICD-10 More examples

• Do this rule hold ?

• Again apparently

28
Proposed mechanism for mapping
C80.9
C39.9
C80.9
C80.9

• Create a 11 input mapping table
• Read ICD-10 backwards and assign every ICD-10
  code (map) to the concept and all its decendents

29
The architecture of ICD-10 More examples

• However, for some reason ICD-10 breaks its own
  rule

• Solution Identify the areas and re-run the
  algorithm for these selected areas

30
Mapping from SNOMED CT to ICD-10

• The algoritm was implemented on an Oracle
  database (program written in PL/SQL)
• Temporary result
• Over 70.000 concepts mainly disorders mapped
• This result can be refined
• When new versions of the terminology and/or the
  classification are released the program can be
  reexecuted

31
Mapping from classifications to SNOMED CT

• Why map backwards?
• to get the primary table for mapping from SNOMED
  CT to classifications (the input table for the
  algorithm)
• to demonstrate a terminology's capability as an
  aggregation tool

32
The architecture of a concept based terminology
Disorder
A polyhierarchal terminology
Tumour
Throat disease
Lung disease
Cancer
Inflammatory disorder
Acute tonsillitis
Pneumonia
Lung cancer
Throat cancer
Benigne tumor in throat
33
The architecture of a concept based terminology
Disorder
The "is a" relations always points "upwards"
Tumour
Throat disease
Lung disease
Cancer
Inflammatory disorder
Acute tonsillitis
Pneumonia
Lung cancer
Throat cancer
Benigne tumor in throat
34
The architecture of a concept based terminology
Disorder
If the "is a" relation is used in "reverse" you
can aggregate information (count) from any point
(concept) downwards
Tumor
Throat disease
Lung disease
Cancer
Inflammatory disorder
Acute tonsillitis
Pneumonia
Lung cancer
Throat cancer
Benigne tumor in throat
35
The architecture of a concept based terminology
Disorder
If the "is a" relation is used in "reverse" you
can aggregate information (count) from any point
(concept) downwards
Tumour
Count cancers
Throat disease
Lung disease
Cancer
Inflammatory disorder
Acute tonsillitis
Pneumonia
Lung cancer
Throat cancer
Benigne tumor in throat
36
The architecture of a concept based terminology
Disorder
If the "is a" relation is used in "reverse" you
can aggregate information (count) from any point
(concept) downwards
Count "tumours"
Tumour
Throat disease
Lung disease
Cancer
Inflammatory disorder
Acute tonsillitis
Pneumonia
Lung cancer
Throat cancer
Benigne tumor in throat
37
The architecture of a concept based terminology
Disorder
If the "is a" relation is used in "reverse" you
can aggregate information (count) from any point
(concept) downwards
Count "lung diseases"
Tumour
Throat disease
Lung disease
Cancer
Inflammatory disorder
Acute tonsillitis
Pneumonia
Lung cancer
Throat cancer
Benigne tumor in throat
38
There are several possibilities for selection of
entry ("aggregation") points

• The mentioned terminologies contains many levels
  (they are "deep" not "flat")
• Each concept can be used as an "aggregation
  point"
• You can extract the list of concepts "below" a
  chosen point for review or "control"
• You can add or subtract chosen "subtrees"
• You can select via aggregation points in
  supporting hierarchies (e.g. anatomy or
  microbiology)

39
While we are waiting for data recorded with codes
from clinical terminologies

• The best way of showing the described mechanism
  is by collecting fine granulated coded
  information via an EHR
• Such information is currently not available
• However, disease - and procedure classifications
  have been in use for decades
• The classification codes can be mapped to
  terminologies
• "At the end of the day, a code is a code"
• Margo Imel

40
Mapping of classification codes to a terminology
When the ICD-10 codes are mapped to the
terminology concept codes the terminology
framework can be used as an aggregation tool
Each classification code (in this example ICD-10
codes) is mapped to the corresponding terminology
concept
Disorder
Tumour
Throat disease
Lung disease
Inflammation
Cancer
Acute tonsillitis
Pneumonia
Lung cancer
Throat cancer
Benigne tumor in throat
41
Mapping of classification codes to a terminology
This mechanism also works with concepts that only
exists in the terminology e.g. the concept
"lung disease" that are not found in ICD-10
Each classification code (in this example ICD-10
codes) is mapped to the corresponding terminology
concept
Disorder
Tumour
Throat disease
Lung disease
Inflammation
Cancer
Acute tonsillitis
Pneumonia
Lung cancer
Throat cancer
Benigne tumor in throat
42
Mapping of classification codes to a terminology
If a corresponding concept for a ICD-10 code does
not exist this particular code mapped or linked
to the concept in the terminology that
corresponds to the nearest supertype
Disorder
Tumor
Throat disease
Lung disease
Cancer
Inflammatory disorder
Abscess of pharynx
J18.9
J03.9
Acute Tonsillitis
Throat cancer
Pneumonia
Benigne tumor in throat
Lung cancer
Retropharyngeal and parapharyngeal abscess
J39.0
43
Examples from the National Danish Patient
Registrar

• On the following slides a few examples of
  aggregation of coded information based on the
  described method is shown
• The information is drawn from all outpatients and
  admitted patients in Denmark 2002
• The information is recorded with ICD-10 codes
  partially mapped to SNOMED CT
• The aggregation points are SNOMED CT concepts
  shown in italics

44
Data from NPR aggregated with SNOMED
CTSNOMED CT concept in italics
45
Data from NPR aggregated with SNOMED
CTSNOMED CT concept in italics
46
Data from NPR aggregated with SNOMED
CTSNOMED CT concepts in italics
47
Terminology as an aggregation tool

• Terminologies can be used as statistical
  aggregation tools
• It can be questioned if the mapping from a
  clinical terminology to a classification with the
  purpose of using the classification as the
  aggregation tool is practical in the future
• It is possible to link e.g. ICD codes into the
  terminology and use this as an aggregation tool
  both for analysing present day information and
  in the future for comparison of structured
  information collected from an EHR with present
  day coded registrar information

48
Is it possible to use EHR data directly?

• - for statistics?
• - for DRG/HRG?
• - etc. . .

apperantly!
49
Can DRG/HRG groupings be found in SNOMED CT?

• 134 05 MED HYPERTENSION
• 38341003 hypertensive disorder
• 238 08 MED OSTEOMYELITIS
• 60168000 osteomyelitis
• 271 09 MED SKIN ULCERS
• 46742003 skin ulcer
• 127 05 MED HEART FAILURE SHOCK
• heart failure shock
• 232 08 SURG ARTHROSCOPY
• 13714004 arthroscopy

include subtypes
Again apperantly However, the possibility of
direct mapping from SNOMED CT to DRG/HRH should
be analysed further
50
Does a terminology give all the answers?
Decision support
51
The Danish EHR modelThe steps of the clinical
process
Evaluation
Diagnostic consideration
Diagnosis (Condition)
Goal
Outcome
The model is a modified problem solving or
quality assurance circle with with health care
terms and a "goal" added
Version 2.2 is just released and is documented in
text, use cases and UML
Executing
Planning
Plans
52
Model and Terminology

• The model requires highly structured input
• i.e. data types such as numbers, dates etc.
• and structured (preferably coded) clinical
  information e.g. from a terminology (including
  drugs)

• including information about
• location (hospital, department, etc.)
• user access (logging)

53
Can we use SNOMED CT?
Evaluation
Diagnostic consideration
Outcome (result)
Diagnosis (Condition)
Goal
Clinical finding
Observable entity Substance
A model is needed as a container for the
information
Executing
Planning
Plans
Procedures
54
Comparing HL7 v3 with BEHR
55
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