Transcript National Health Informatics Strategy in Denmark

What is SNOMED CT good for ?
Ole Terkelsen
MD Ph.D.
Danish National Board of Health
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
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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?
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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 It is out of date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"
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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
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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
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A quick journey from the sources of
SNOMED Clinical Terms
RCGP
Oxmis
1984 Read Code
4-byte (10,000)
1983 Read Code
Mnemonics (500)
1965 SNOP
1978 SNOMED
45,000
1988 Read Code
Version 2 (30,000)
2001 SNOMED RT
(150,000)
1993 SNOMED
International
130,000
NHS Clinical Terms
Version 3 (250,000)
1993 SNOMED
International 3.5
156,000
2002 SNOMED Clinical Terms
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(350,000)
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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
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SNOMED CT's Top-level Hierarchies
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SNOMED CT database tables
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Concepts – table – 350,000 entries
CONCEPTID
74400008
80146002
233604007
3716002
CONCEPTSTATUS
FULLYSPECIFIEDNAME
0
Appendicitis (disorder)
0
Appendectomy (procedure)
0
Pneumonia (disorder)
0
Goiter (disorder)
CTV3ID
Xa9C4
X20Wz
X100E
X76FB
SNOMEDID
D5-46100
P1-57450
D2-0007F
DB-80100
ISPRIMITIVE
0
0
1
1
69536005
113276009
14742008
1236009
0
0
0
0
Head structure (body structure)
Intestinal structure (body structure)
Large intestinal structure (body structure)
Duodenal serosa (body structure)
Xa1gv
Xa1Fr
Xa1Fv
XU5xL
T-D1100
T-50500
T-59000
T-58230
1
1
1
1
41146007 0
9861002
0
113861009 0
Bacterium (organism)
Streptococcus pneumoniae (organism)
Mycobacterium tuberculosis (organism)
X79pY
X73GQ
XU3Q2
L-10000
L-25116
L-21907
1
1
1
373270004 0
Penicillin (substance)
XUWFk
C-0021D
1
17369002
123603008
123604002
123609007
0
0
0
0
Spontaneous abortion (disorder)
Acute focal hepatitis (disorder)
Toxic cirrhosis (disorder)
Subacute glomerulonephritis (disorder)
L04..
XU5xO
X307V
XU5xW
D8-04100
D5-80300
D5-80390
D7-12102
1
1
1
1
12361006
0
Osteotomy of radius and ulna (procedure)
XU5xY
P1-16187
1
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Descriptions – table – ca. 1 mio. synonyms
DESCRIPTIONID DESC-STATUS
814894010
0
123558018
0
CONCEPTID
74400008
74400008
TERM
Appendicitis (disorder)
Appendicitis
DESCRIPTIONTYPE
3
1
LANGUAGECODE
en
en
21274010
132967011
132973012
132972019
0
0
0
0
80146002
80146002
80146002
80146002
Appendectomy (procedure)
Appendectomy
Appendicectomy
Excision of appendix
3
1
1
2
en
en-US
en-GB
en
621810017
350049016
0
0
233604007
233604007
Pneumonia (disorder)
Pneumonia
3
1
en
en
3716002
3716002
3716002
3716002
3716002
3716002
3716002
3716002
3716002
3716002
Goiter (disorder)
Goiter
Goitre
Struma - goiter
Struma - goitre
Swelling of thyroid gland
Thyroid enlargement
Enlargement of thyroid
Struma of thyroid
Thyromegaly
3
1
1
2
2
2
2
2
2
2
en
en-US
en-GB
en-US
en-GB
en
en
en
en
en
768995016
0
7261017
0
7267018
0
486646013
0
486645012
0
486643017
0
486644011
0
7263019
0
7264013
0
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7265014
0
12
Relationships – table – 1.5 million entries
RELATIONSHIPID
521526024
556899029
462569022
1045543021
405306026
1800183029
1939511022
707803022
136924025
78981022
1752936025
372287021
2038091027
152634025
1919793025
859420029
20869021
210013026
181749023
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CONCEPTID1
236209003
247994001
191910002
190570008
147235008
129709009
206126004
15410007
309574009
257819000
315369003
172363006
64614001
122210004
122279008
74319002
106424006
38169004
20628002
RELATIONSHIPTYPE
363704007
363714003
123005000
363698007
116680003
363714003
246075003
363704007
116680003
116680003
363714003
116680003
116680003
116680003
260686004
123005000
116680003
116680003
116680003
CONCEPTID2
181422007
47078008
362012001
77637002
363662004
278844005
373266007
30291003
118246004
129304002
302147001
172359004
39981009
104172004
129265001
361714009
236312003
106424006
106424006
Is a
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The architecture of SNOMED CT !
Disorder
A concept
based
terminology
Tumor
"Is a"
relation
Throat
disease
Inflammation
Tonsillitis
Lung disease
Cancer
Pneumonia
Benigne tumor
in throat
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Throat
cancer
Lung cancer
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SNOMED CT as a multilingual terminology
Fully specified name
Appendectomy (procedure)
Appendektomie (Verfahren)
Apendicectomía (procedimiento)
Appendektomi (procedure)
All with the same conceptid: 80146002
Modified from David Markvell
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SNOMED CT as a multilingual terminology
Preferred term
Synonym
Appendectomy
Excision of appendix
Appendicectomy
Entfernung des Wurmfortsatzes
Appendektomie
Operative Entfernung des Appendix
Apendicectomía
Escisión del apéndice
Appendectomi
Operativ fjernelse af blindtarm
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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)
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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)
Indirect morphology (attribute)
Has interpretation (attribute)
Interprets (attribute)
Associated etiologic finding (attribute)
Access instrument (attribute)
Recipient category (attribute)
Specimen substance (attribute)
Pathological process (attribute)
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SNOMED CT – relations
Appendectomy
Bacterial meningitis
is-a Operation on appendix
is-a Infective meningitis
is-a Partiel excision of large intestine
is-a Bacterial infection of central nervous
system
procedure-site Appendix structure
method Excision - Action
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)
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anatomical man
18
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
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What about statistics and DRG?
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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.
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Is it possible to map?
what are the use cases?
 mapping from SNOMED CT to classifications?
 mapping from classifications to SNOMED CT?
New
EHR
EPJ
based
on
EPJ
baseret
på
BEHR
baseret
BERH på
BERH
national
patient
registry
(continuity
care
based)
SNOMED CT codes
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mapning,
converting
and explicitreporting
national
patient registry
(based on contact
registration)
statistics
DRG
quality
research
etc.
(based on
contact
registration)
Classification codes
22
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 ?
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The architecture of SNOMED CT !
Disorder
A concept
based
terminology
Tumor
Throat
disease
Inflammation
Tonsillitis
Lung disease
Cancer
Pneumonia
Benigne tumor
in throat
Sandefjord 2005
Throat
cancer
Lung cancer
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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
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The architecture of ICD-10 - Examples
CODE
A03
A03.0
A03.1
A03.2
A03.3
A03.8
A03.9



TEXT
Shigellosis
Shigellosis due to Shigella dysenteriae
Shigellosis due to Shigella flexneri
Shigellosis due to Shigella boydii
Shigellosis due to Shigella sonnei
Other shigellosis
Shigellosis, unspecified
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”
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The architecture of ICD-10 – More examples
 Do this ”rule” hold ?
A49.8 Other bacterial infections of unspecified site
A49.9 Bacterial infection, unspecified
B26
B26.0
B26.1
B26.2
B26.3
B26.8
B26.9
Mumps
Mumps
Mumps
Mumps
Mumps
Mumps
Mumps
orchitis
meningitis
encephalitis
pancreatitis
with other complications
without complication
B34.8 Other viral infections of unspecified site
B34.9 Viral infection, unspecified
B99.9 Other and unspecified infectious diseases

Again – apparently
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Proposed mechanism for mapping
Disorder
Tumor
Inflammation
Throat
disease
Lung
disease
C80.9
Cancer
C80.9
Tonsillitis
Pneumonia
Benigne
tumor in
throat
Throat
cancer
C80.9
C39.9
C34.9 Malignant neoplasm of bronchus or lung, unspecified
C80.9 Malignant neoplasm without specification of site
Lung
cancer
Create a 1:1 input mapping table
 Read ICD-10 backwards – and assign every ICD-10 code
(map) to the concept and all its decendents

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The architecture of ICD-10 – More examples

However, for some reason ICD-10 breaks its own rule
A49.8 Other bacterial infections of unspecified site
A49.9 Bacterial infection, unspecified

A53
Other and unspecified syphilis
A54
Gonococcal infection
Solution: Identify the areas and re-run the algorithm for
these selected areas
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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
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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
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The architecture of a concept based
terminology
Disorder
A polyhierarchal
terminology
One concept
can have more
than one
supertype
Tumour
Throat
disease
Lung disease
Cancer
Inflammatory disorder
Acute tonsillitis
Pneumonia
Benigne tumor
in throat
Sandefjord 2005
Throat
cancer
Lung cancer
32
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
Benigne tumor
in throat
Sandefjord 2005
Throat
cancer
Lung cancer
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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
Benigne tumor
in throat
Sandefjord 2005
Throat
cancer
Lung cancer
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
Tumour
Throat
disease
Lung disease
Count
cancers
Cancer
Inflammatory disorder
Acute tonsillitis
Pneumonia
Benigne tumor
in throat
Sandefjord 2005
Throat
cancer
Lung cancer
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
Count
"tumours"
Tumour
Throat
disease
Lung disease
Cancer
Inflammatory disorder
Acute tonsillitis
Pneumonia
Benigne tumor
in throat
Sandefjord 2005
Throat
cancer
Lung cancer
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 "lung
diseases"
Tumour
Throat
disease
Lung disease
Cancer
Inflammatory disorder
Acute tonsillitis
Pneumonia
Benigne tumor
in throat
Sandefjord 2005
Throat
cancer
Lung cancer
37
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)
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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
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Mapping of classification codes to a terminology
Each classification
code (in this example
ICD-10 codes) is
mapped to the
corresponding
terminology concept
Disorder
Tumour
Throat
disease
Inflammation
J39.0
Acute tonsillitis
Sandefjord 2005
J18.9
When the ICD-10 codes
are mapped to the
terminology concept
codes the terminology
framework can be used as
an aggregation tool
Lung disease
Cancer
Pneumonia
Benigne tumor
in throat
Throat
cancer
Lung cancer C34.9
40
Mapping of classification codes to a terminology
Each classification
code (in this example
ICD-10 codes) is
mapped to the
corresponding
terminology concept
Disorder
Tumour
Throat
disease
Inflammation
J39.0
Acute tonsillitis
Sandefjord 2005
J18.9
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
Lung disease
Cancer
Pneumonia
Benigne tumor
in throat
Throat
cancer
Lung cancer C34.9
41
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
J03.9
Retropharyngeal and
parapharyngeal
abscess
Acute
Tonsillitis
J18.9
Pneumonia
Benigne tumor
in throat
Throat
cancer
Lung cancer
J39.0
Sandefjord 2005
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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
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Data from NPR – ”aggregated” with SNOMED CT
SNOMED CT concept in italics
All types of pneumonia and viral pneumonia
all admitted patients 2002, distributed by age
1400
1200
number
1000
800
600
400
200
Age
10
4
96
92
88
84
80
76
72
68
64
10
0
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60
56
52
48
44
40
36
32
28
24
20
16
12
8
4
0
0
44
Data from NPR – ”aggregated” with SNOMED CT
SNOMED CT concept in italics
All types of complications to diabetes
Outpatients and admitted patients 2002, distributed by age
1000
900
800
700
number
600
500
400
300
200
100
92
88
84
80
76
72
68
64
60
56
48
44
40
36
32
28
24
20
16
52
age
96
10
0
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12
8
4
0
0
45
Data from NPR – ”aggregated” with SNOMED CT
SNOMED CT concepts in italics
Hereditary and congenital disease
admitted patients 2002, distributed by age
2500
2000
number
1500
1000
500
age
96
10
0
92
88
84
80
76
72
68
64
60
56
52
48
44
40
36
32
28
24
20
12
8
4
0
Sandefjord 2005
16
0
46
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

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Is it possible to use EHR data directly?
 - for statistics? apperantly!
 - for DRG/HRG?
 - etc. . .
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Can DRG/HRG groupings be found in SNOMED CT?

134 05 MED HYPERTENSION


46742003 skin ulcer*
127 05 MED HEART FAILURE & SHOCK


60168000 osteomyelitis*
271 09 MED SKIN ULCERS


* include subtypes
238 08 MED OSTEOMYELITIS


38341003 hypertensive disorder*
heart failure* + shock*
Again apperantly
However, the
possibility of direct
mapping from
SNOMED CT to
DRG/HRH should be
analysed further
232 08 SURG ARTHROSCOPY

13714004 arthroscopy*
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Does a terminology give all the answers?
Sandefjord 2005
Decision support
50
The Danish EHR model
The steps of the clinical process
Process
Diagnostic
Evaluation
consideration
Information
Diagnosis
Version
The
model
2.2isisa
modified
just
released
problem
and
solving
is
documented
or quality
in
assurance
text,
use cases
circle
with UML
and
with health
care terms and a
"goal" added
Sandefjord 2005
(Condition)
Goal
Planning
Outcome
Executing
Plans
51
Model and Terminology
Clinical 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)


BEHR
Sandefjord 2005
including information about
location (hospital,
department, etc.)
 user access (logging)

52
Can we use SNOMED CT?
Process
Diagnostic
Evaluation
consideration
Information
Diagnosis
(Condition)
Clinical finding
A model is
needed as a
container for
the information
Sandefjord 2005
Goal
Outcome
(result)
Observable
entity
Substance
Planning
Executing
Plans
Procedures
53
Comparing HL7 v3 with BEHR
Diagnostic
Evaluation
consideration
Diagnosis
(Condition)
Goal
Planning
Outcome
(result)
Executing
Plans
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