what is snomed ct good for
Download
Skip this Video
Download Presentation
What is SNOMED CT good for ?

Loading in 2 Seconds...

play fullscreen
1 / 55

What is SNOMED CT good for - PowerPoint PPT Presentation


  • 158 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'What is SNOMED CT good for ' - thane


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
what is snomed ct good for

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
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
why is there a need for a clinical terminology3
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?
why can t we use classifications like icd 10
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"
what terminologies are available
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
what is snomed ct
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
a quick journey from the sources of snomed clinical terms
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)

what is snomed ct8
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
concepts table 350 000 entries
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

123604002 0 Toxic cirrhosis (disorder) X307V D5-80390 1

123609007 0 Subacute glomerulonephritis (disorder) XU5xW D7-12102 1

12361006 0 Osteotomy of radius and ulna (procedure) XU5xY P1-16187 1

descriptions table ca 1 mio synonyms
Descriptions – table – ca. 1 mio. synonyms

DESCRIPTIONID DESC-STATUS CONCEPTID TERM DESCRIPTIONTYPE 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

7263019 0 3716002 Enlargement of thyroid 2 en

7264013 0 3716002 Struma of thyroid 2 en

7265014 0 3716002 Thyromegaly 2 en

relationships table 1 5 million entries
Relationships – table – 1.5 million entries

RELATIONSHIPID CONCEPTID1 RELATIONSHIPTYPE CONCEPTID2

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

20869021106424006116680003 236312003

210013026 38169004116680003106424006

18174902320628002116680003106424006

Is a

the architecture of snomed ct

"Is a" relation

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

snomed ct as a multilingual terminology
SNOMED CT as a multilingual terminology

Fully specified name

Appendectomy (procedure)

Appendektomie (Verfahren)

Apendicectomía (procedimiento)

Appendektomi (procedure)

All with the same conceptid: 80146002

Modifiedfrom David Markvell

snomed ct as a multilingual terminology16
SNOMED CT as a multilingual terminology

Preferred term

Appendectomy

Appendicectomy

Appendektomie

Apendicectomía

Appendectomi

Synonym

Excision of appendix

Entfernung des Wurmfortsatzes

Operative Entfernung des Appendix

Escisión del apéndice

Operativ fjernelse af blindtarm

snomed ct relations
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)

Indirect morphology (attribute)

Has interpretation (attribute)

Interprets (attribute)

Associated etiologic finding (attribute)

Access instrument (attribute)

Recipient category (attribute)

Specimen substance (attribute)

Pathological process (attribute)

snomed ct relations18
SNOMED CT – relations

Appendectomy

is-aOperation on appendix

is-aPartiel excision of large intestine

procedure-siteAppendix structure

methodExcision - Action

  • Bacterial meningitis
    • is-a Infective meningitis
    • is-aBacterial infection of central nervous system
    • finding-siteMeninges structure
    • associated-morphologyInflammation
    • pathological processInfectious disease
    • Causative-agentBacterium
    • (fully defined)

The use of attribute relations follow specific rules (description logics)

anatomical man

do snomed ct meet the demands
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
handling legacy systems
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.
is it possible to map

statistics DRG quality research etc. (based on contact registration)

New

mapning,

converting

and explicit-

reporting

EHRbased on BEHR

national patient registry (continuity care based)

national patient registry (based on contact registration)

EPJbaseret på BERH

EPJbaseret på BERH

SNOMED CT codes

Classification codes

Is it possible to map?

what are the use cases?

  • mapping from SNOMED CT to classifications?
  • mapping from classifications to SNOMED CT?
mapping from snomed ct to classifications
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 ?
the architecture of snomed ct24

Disorder

Tumor

Throat disease

Lung disease

Inflammation

Cancer

Tonsillitis

Pneumonia

Lung cancer

Throat cancer

Benigne tumor in throat

The architecture of SNOMED CT !

A concept based terminology

the architecture of icd 10
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
the architecture of icd 10 examples
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”
the architecture of icd 10 more examples
The architecture of ICD-10 – More examples
  • Do this ”rule” hold ?
  • Again – apparently
proposed mechanism for mapping

Disorder

Tumor

Throat disease

Lung disease

Inflammation

Cancer

Tonsillitis

Pneumonia

Lung cancer

Benigne tumor in throat

Throat cancer

Proposed mechanism for mapping
  • 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

C80.9

C39.9

C80.9

C80.9

the architecture of icd 10 more examples29
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
mapping from snomed ct to icd 10
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
mapping from classifications to snomed ct
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
the architecture of a concept based terminology

One concept can have more than one supertype

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

the architecture of a concept based terminology33
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

the architecture of a concept based terminology34
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

the architecture of a concept based terminology35
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

the architecture of a concept based terminology36
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

the architecture of a concept based terminology37
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

there are several possibilities for selection of entry aggregation points
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)
while we are waiting for data recorded with codes from clinical terminologies
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
mapping of classification codes to a terminology

J39.0

J18.9

C34.9

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

mapping of classification codes to a terminology41

J39.0

J18.9

C34.9

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

mapping of classification codes to a terminology42
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

J18.9

Acute Tonsillitis

Pneumonia

Benigne tumor in throat

Throat cancer

Lung cancer

Retropharyngeal and parapharyngeal abscess

J39.0

examples from the national danish patient registrar
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
terminology as an aggregation tool
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
is it possible to use ehr data directly
Is it possible to use EHR data directly?
  • - for statistics?
  • - for DRG/HRG?
  • - etc. . .

apperantly!

can drg hrg groupings be found in snomed ct
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

the danish ehr model the steps of the clinical process

Process

Information

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

Planning

Executing

Plans

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)

Clinical Terminology

BEHR

Model and Terminology
  • including information about
    • location (hospital, department, etc.)
    • user access (logging)
can we use snomed ct

Process

Information

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

Planning

Executing

Plans

Procedures

comparing hl7 v3 with behr

Evaluation

Diagnostic

consideration

Diagnosis

(Condition)

Goal

Outcome

(result)

Planning

Executing

Plans

Comparing HL7 v3 with BEHR
ad