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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

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What is snomed ct good for l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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  • It is out of date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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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


Snomed ct s top level hierarchies l.jpg

SNOMED CT's Top-level Hierarchies


Snomed ct database tables l.jpg

SNOMED CT database tables


Concepts table 350 000 entries l.jpg

Concepts – table – 350,000 entries

CONCEPTIDCONCEPTSTATUSFULLYSPECIFIEDNAMECTV3IDSNOMEDIDISPRIMITIVE

744000080Appendicitis (disorder)Xa9C4D5-461000

801460020Appendectomy (procedure)X20WzP1-574500

2336040070Pneumonia (disorder)X100ED2-0007F1

37160020Goiter (disorder)X76FBDB-801001

695360050Head structure (body structure)Xa1gvT-D11001

1132760090Intestinal structure (body structure)Xa1FrT-505001

147420080Large intestinal structure (body structure)Xa1FvT-590001

12360090Duodenal serosa (body structure)XU5xLT-582301

411460070Bacterium (organism)X79pYL-100001

98610020Streptococcus pneumoniae (organism)X73GQL-251161

1138610090Mycobacterium tuberculosis (organism)XU3Q2L-219071

3732700040Penicillin (substance)XUWFkC-0021D1

173690020Spontaneous abortion (disorder)L04..D8-041001

1236030080Acute focal hepatitis (disorder)XU5xOD5-803001

1236040020Toxic cirrhosis (disorder)X307VD5-803901

1236090070Subacute glomerulonephritis (disorder)XU5xWD7-121021

123610060Osteotomy of radius and ulna (procedure)XU5xYP1-161871


Descriptions table ca 1 mio synonyms l.jpg

Descriptions – table – ca. 1 mio. synonyms

DESCRIPTIONIDDESC-STATUSCONCEPTIDTERMDESCRIPTIONTYPELANGUAGECODE

814894010074400008Appendicitis (disorder)3en

123558018074400008Appendicitis1en

21274010080146002Appendectomy (procedure)3en

132967011080146002Appendectomy1en-US

132973012080146002Appendicectomy1en-GB

132972019080146002Excision of appendix2en

6218100170233604007Pneumonia (disorder)3en

3500490160233604007Pneumonia1en

76899501603716002Goiter (disorder)3en

726101703716002Goiter1en-US

726701803716002Goitre1en-GB

48664601303716002Struma - goiter2en-US

48664501203716002Struma - goitre2en-GB

48664301703716002Swelling of thyroid gland2en

48664401103716002Thyroid enlargement2en

726301903716002Enlargement of thyroid2en

726401303716002Struma of thyroid2en

726501403716002Thyromegaly2en


Relationships table 1 5 million entries l.jpg

Relationships – table – 1.5 million entries

RELATIONSHIPIDCONCEPTID1RELATIONSHIPTYPECONCEPTID2

521526024236209003363704007181422007

55689902924799400136371400347078008

462569022191910002123005000362012001

104554302119057000836369800777637002

405306026147235008116680003363662004

1800183029129709009363714003278844005

1939511022206126004246075003373266007

70780302215410007 36370400730291003

136924025309574009116680003118246004

78981022 257819000116680003129304002

1752936025315369003363714003302147001

372287021172363006116680003172359004

203809102764614001 11668000339981009

152634025122210004116680003104172004

1919793025122279008260686004129265001

85942002974319002 123005000361714009

20869021106424006116680003236312003

21001302638169004116680003106424006

18174902320628002116680003106424006

Is a


The architecture of snomed ct l.jpg

"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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

SNOMED CT – relations

Appendectomy

is-aOperation on appendix

is-aPartiel excision of large intestine

procedure-siteAppendix structure

methodExcision - Action

  • Bacterial meningitis

    • is-aInfective 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 l.jpg

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


What about statistics and drg l.jpg

What about statistics and DRG?


Handling legacy systems l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

The architecture of ICD-10 – More examples

  • Do this ”rule” hold ?

  • Again – apparently


Proposed mechanism for mapping l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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


Data from npr aggregated with snomed ct snomed ct concept in italics l.jpg

Data from NPR – ”aggregated” with SNOMED CTSNOMED CT concept in italics


Data from npr aggregated with snomed ct snomed ct concept in italics45 l.jpg

Data from NPR – ”aggregated” with SNOMED CTSNOMED CT concept in italics


Data from npr aggregated with snomed ct snomed ct concepts in italics l.jpg

Data from NPR – ”aggregated” with SNOMED CTSNOMED CT concepts in italics


Terminology as an aggregation tool l.jpg

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 l.jpg

Is it possible to use EHR data directly?

  • - for statistics?

  • - for DRG/HRG?

  • - etc. . .

apperantly!


Can drg hrg groupings be found in snomed ct l.jpg

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


Does a terminology give all the answers l.jpg

Does a terminologygive all the answers?

Decision support


The danish ehr model the steps of the clinical process l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

Evaluation

Diagnostic

consideration

Diagnosis

(Condition)

Goal

Outcome

(result)

Planning

Executing

Plans

Comparing HL7 v3 with BEHR


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