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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?. Electronic Health Records (EHRs) will be introduced in the hospitals in this decade

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What is SNOMED CT good for ?

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  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• There are no definitions • C80 Malignant neoplasm without specification of site • probably means "cancer" • It is out of date • C85.0 Lymphosarcoma • probably means "malignant lymphoma"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 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

  12. 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

  13. 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

  14. "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

  15. 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

  16. 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

  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) Indirect morphology (attribute) Has interpretation (attribute) Interprets (attribute) Associated etiologic finding (attribute) Access instrument (attribute) Recipient category (attribute) Specimen substance (attribute) Pathological process (attribute)

  18. 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

  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. 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?

  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. 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

  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. 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

  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. 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

  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. 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

  41. 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

  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 J03.9 J18.9 Acute Tonsillitis Pneumonia Benigne tumor in throat Throat cancer 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 terminologygive all the answers? Decision support

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