SNOMED CT is a clinical terminology used for coding, retrieving, and analyzing health care data. It consists of codes, terms, and relationships that can precisely record and represent clinical information across health care. SNOMED CT concepts are organized into hierarchies and linked through relationships. It aims to enable automated clinical decision support and research by structuring information in a semantically meaningful way.

1. SNOMED CT & HL7 Terminology Binding Dr. Abbas Shojaee – BUMS, June 2010 This presentation uses works of: Tim Benson Ian Horrocks Kent A. Spackman

2. Vocabulary Syntactic: شکلي Semantic:معنايي Lexical: واژه شناسي، وابسته به واژه Homonym: اشاره يک واژه به چند معنا Synonym:هم معنايي، اشاره چند واژه به يک معني

3. Presentation What is interoperability? What is SNOMED CT? Uses of SNOMED CT SNOMED Standard Development Organisation (SSDO) - Why the change/why join? - What is the current situation? The role of WHO Conclusions

4. Definition

5. SNOMED CT 5 The Systematized Nomenclature of Medicine – Clinical Terms

6. 6 SNOMED CT “SNOMED Clinical Terms (SNOMED CT) is a dynamic, scientifically validated clinical health care terminology and infrastructure that makes health care knowledge more usable and accessible. The SNOMED CT Core terminology provides a common language that enables a consistent way of capturing, sharing and aggregating health data across specialties and sites of care. Among the applications for SNOMED CT are electronic medical records, ICU monitoring, clinical decision support, medical research studies, clinical trials, computerized physician order entry, disease surveillance, image indexing and consumer health information services.” http://www.snomed.org/snomedct/index.html

7. Motivation

8. Why SNOMED CT might be of my interest? Archimedes: “Give me a place to stand and a lever long enough and I will move the world” This lever to mathematicians is Numbers, they try to percept and move the world through numbers. This lever to medical knowledge and information workers, is going to appear in SNOMED CT or its successors.

9. ORIGIN and HISTORY SNOMED CT

10. SNOMED SNOMED® International: The division of the College of American Pathologists responsible for maintenance and release of SNOMED CT SNOMED CT Releases twice yearly (January & July) of the terminology commonly called SNOMED

11. Development of terminologies SNOMED CT 2000 SNOMEDRT READ 3 (CTV3) 1990 READ 2 SNOMED 3 Read 4 byte 1980 SNOMED 2 1970 SNOP

12. SNOMED Clinical Terms SNOMED – CTV3 Timelines SNOMED Read Codes SNOMED 21979 - 1982 1983 Read Codes (v1) 1984 1985 1986 1987 1988 Professional Endorsement 1989 1990 Purchased by NHS 1991 1992 Clinical Terms Projects SNOMED 31993 , “ 1994 “ 1995 CTV3 (Clinical Terms version 3) 1996 UK Gov’t Inquiries into Read Codes CAP business plan 1997 “ 1998 “ NHS Agreement1999 CAP Agreement SNOMED RT 2000 2001 2002 Formation of the SNOMED International Division of the C.A.P.

13. SNOMED History SNOP – 1965 – basis for ICD-O SNOMED – 1974 SNOMED II – 1979 SNOMED Version 3.0 – 1993 SNOMED Version 3.5 – 1998 SNOMED RT – 2000 (Merge with UK NHS) SNOMED CT (SNOMED RT + CTV3) – 2002 SNOMED CT Spanish Edition – April 2002 SNOMED CT German Edition - April 2003 Free in USA - Agreement with NLM – June 2003 SNOMED SDO Proposal 2006

14. User distribution - SNOMED CT Users

15. What is SNOMED CT Essence

16. What is it? A reference terminology A clinical terminology with reference and interface properties A CD containing a set of tables A set of codes with names A set of definitions “per genus et differentiam” A clinical terminology standard A knowledge base? A dictionary? An ontology? An application ontology?

17. Formal Ontology? SNOMED is not a formal ontology (but some parts of it are migrating in that direction) It is a reference terminology that is progressively more well-supported by formal ontological principles Includes terms and non-ontological assertions / ideas I dislike the term “application ontology” – fish or fowl? Many of SNOMED’s design decisions are supported by formal ontological principles. But… Many of SNOMED’s hierarchies are still “unprincipled” and incomplete. Requires continued evolution and maturation

18. What is an Ontology?

19. What is an Ontology? A model of (some aspect of) the world

20. What is an Ontology? A model of (some aspect of) the world Introduces vocabularyrelevant to domain, e.g.: Anatomy

21. What is an Ontology? A model of (some aspect of) the world Introduces vocabularyrelevant to domain, e.g.: Anatomy Cellular biology

22. What is an Ontology? A model of (some aspect of) the world Introduces vocabularyrelevant to domain, e.g.: Anatomy Cellular biology Aerospace

23. What is an Ontology? A model of (some aspect of) the world Introduces vocabularyrelevant to domain, e.g.: Anatomy Cellular biology Aerospace Dogs

24. What is an Ontology? A model of (some aspect of) the world Introduces vocabularyrelevant to domain, e.g.: Anatomy Cellular biology Aerospace Dogs Hotdogs …

25. What is a Clinical Terminology? Ordinarily: A finite enumerated set of terms intended to convey information unambiguously SNOMED is more than this Terms plus codes plus the ability to put them together in meaningful ways

26. What is SNOMED CT? A work of clinical terminology for coding, retrieving and analyzing data about health and health care Comprised of codes, terms and relationships, for use in precisely recording and representing clinical information across the scope of health care Concept-based: Each code represents a single meaning and can have multiple descriptions (terms)

27. What is not SNOMED is not the language police Clinicians determine what words mean by how they use them. SNOMED reflects those meanings. SNOMED is not an independent source of scientific/professional practice standards Scientists and professional groups define their standards. We try to follow those standards. SNOMED is not a comprehensive knowledge base for healthcare This is out of scope. SNOMED’s goal is terminological knowledge: that which is always necessarily true of a term.

28. What does it do? SNOMED CT is a terminological resource that can be implemented in software applications to represent clinically relevant information In a “semantically structured” form that can be used by automated applications

29. What is it for? It is for building applications capable of: Recording statements about the health and health care of individuals In a way that permits retrieval according to the meaning of the statements, rather than just the words used Retrieving individual cases and groups of cases To enable more automated and sophisticated decision support, epidemiology, and research

30. Pictures can also be presented more dramatically in widescreen. Desiderata for a global terminology

31. Desiderata for a global terminology Comprehensiveness: Coverage for all aspects of health care Adequacy: Is it fit or purpose – multiple purposes? Does it have a good information model and ontological basis? Multilingual applicability language independent formal concept representation Representation in multiple languages – more than translation Utility: Is it beneficial for: Care providers : decision making, outcome evaluation Consumers : participation – ownership – evaluation – risk reduction Policy/Decision Makers : informed decision making on costs, benefits, efficiency Reliability: does it give the same results in different users Source: T. Ustun, WHO, SNOMED Semantic Mining Conference, Copenhagen, Oct. 2006

32. Desiderata for a global terminology Validity: Does it indicate the right things – and does the indication make sense Comparability Does the data in different context have same properties to be compared Interoperability Technical: Can information systems exchange information and use it? Semantic: Can information systems interpret the data with the same meaning? Quality Assurance Product: Annotation and Content Process: Use and Usability Sustainability Secured maintenance: commitment to stability with earlier versions Openness to address emerging technical issues Source: T. Ustun, WHO, SNOMED Semantic Mining Conference, Copenhagen, Oct. 2006

33. SNOMED CT Structure Hierarchies Parent child relationships Vertical structure Concepts may have multiple parents Relationships between concepts Using attributes, concepts may be linked to each other Horizontal relationships

34. Healthcare systems included ICD9-CM ICD-V2-Oncology LOINC Ophthalmology-related terms Systematized Nomenclature of Dentistry SM of Vet Med NANDA Taxonomy II Nursing Interventions Classification Nursing Outcomes Classification Peri-operative Nursing Data Set The Omaha System The Georgetown Home Health Care Classification

35. SNOMED CT Structural components SNOMED CT is composed of components, which include concepts, relationships, descriptions, subsets, and cross maps, Each of which is identified by a SNOMED CT Identifier (SCTID) and has a validity status.

36. Components of SNOMED CT Concepts The basic units of SNOMED CT Descriptions These relate terms that name the concepts to the concepts themselves. Each concept has at least two Descriptions. Hierarchies Concepts are organized into twenty SNOMED CT hierarchies (in UK extension). Each hierarchy has sub-hierarchies within it. Relationships Relationships are the connections between concepts in SNOMED CT. + mappings Many-to-many mappings to terms in ICD and OPCS + Inclusion of Dictionary of Medicines and Devices

37. SNOMED Clinical Terms Identifier (SCTID) The SCTID is a 64 bit integer- between 6 and 18 digits long. All components are identified using a special SCTID.

38. Validity Status An important principle of SNOMED CT is that of permanence. Once a component such as a concept or description has been created it is never deleted Status codes: Active: Current (0), Limited (6), Pending move (11) Inactive: Retired (1), Duplicate (2), Obsolete (3), Ambiguous (4), Erroneous (5),Inappropriate (7), Inactive concept (8), Implied (9), Moved elsewhere (10)

39. Concepts SNOMED CT is concept-oriented A concept is just a clinical idea to which a unique ConceptID that is a SCTID Concepts are formally defined in terms of their relationships with other concepts: Subtype relationship: Concept Z IS_A concept Y Attribute relationship

40. Content of SNOMED CT: Organized in 19 different hierarchiesThese are indeed high level concepts SNOMED CT : Root concept Clinical Finding Procedure Observable entity Body structure Organism Substance Pharmaceutical/biology product Specimen Physical object Physical force Events Environments/Geographical locations Social Context Context-dependent categories Staging and scales Attribute Qualifier value Special concept

41. Concept ExampleGastric ulcer(SCTID397825006) Terms: Gastric ulcer (disorder) Gastric ulcer Stomach ulcer GU – Gastric ulcer Gastric ulceration Relationships: Is_a Disease of stomach Is_aGastrointestinal ulcer Associated morphology  Ulcer Finding site  Stomach

42. Descriptions Each description has a DescriptionIDwhich is a SCTID Each description links a human-readable term with a concept. Every concept has at least two descriptions: Fully specified name (FSN) : is a phrase that names a concept in a way that is both unique and unambiguous Preferred term:the common phrase or word used by clinicians to name a concept Each concept may have several other descriptions: e.g. synonyms, translations

44. SNOMED Clinical Terms Identifier (SCTID) A 64 bit integer - between 6 and 18 digits long.

45. SNOMED CT Hierarchies

46. Lets bypass the rest of SNOMED CT structures

47. SNOMED CT Expressions Clinical records are created for the purpose of providing information about events or states of affairs. A SNOMED CT expression is a collection of references to one or more concepts used to express an instance of a clinical idea. pre-coordinateda single concept identifier is used to represent a clinical idea Post-coordination representation of a clinical meaning using a combination of two or more codes

48. SNOMED CT expression

49. SNOMED CT Expressions: Sample |50043002 | disorder of respiratory system |+ 87628006 | bacterial infectious disease |: 246075003 | causative agent | = 9861002 | streptococcus pneumoniae|, 363698007 | finding site | = (45653009 | structure of upper lobe of lung | : 272741003 | laterality | = 7771000 | left |)

50. Documentation

51. SNOMED CT Documentation: User Guide Explains the content and the principles used to model the terminology. Intend: To explain SNOMED CT’s capabilities and uses from a content perspective. Audience: clinical personnel, business directors, software product managers, and project leaders; information technology experience, though not necessary, can be helpful.

52. SNOMED CT Documentation: Technical Reference Guide (TRG) Contains reference material related to the current release of SNOMED CT and includes file layouts, field sizes, required values and their meanings, and high-level data diagrams. Audience: for SNOMED CT implementers, such as software developers. assumes an information technology background , clinical knowledge is not a prerequisite.

53. SNOMED CT Documentation: Technical Implementation Guide (TIG) Contains guidelines and advice about the design of applications, terminology services, entering and storing information, and migration of legacy information. Audience: for SNOMED CT implementers, such as software designers. assumes information technology and software development experience. Clinical knowledge is not required, although some background is helpful to understand the application context and needs

54. CLINI Clue BROWSER

55. Pulmonary Tuberculosis kind of pneumonitis kind of tuberculosis caused by Mycobacterium tuberculosis complex kind of Pulmonary disease due to Mycobacteria found in lung structure

56. Employing SNOMED CT

57. What about clinical decision support? IF Two blood cultures, drawn through an antibiotic removal device, more than 30 minutes apart, grow no organism, THEN discontinue antibiotic.

58. procedures IFTwoblood cultures, drawn through an antibiotic removal device, more than 30 minutes apart, growno organism, THENdiscontinueantibiotic. finding device

59. Clinical Decision Support Model + Inference Rules Interface Interface Interface Information Model + Patient Data Structures Terminology Model + Coded Data Diagram based on Figure 1 in Rector AL et al. “Interface of Inference Models with Concept and Medical Record Models” AIME 2001: 314-323

60. Clinical Decision Support Model + Inference Rules IFTwoblood cultures, drawn through Antibiotic removal device, more than 30 minutes apart, grows no organism, THENdiscontinueantibiotic. Interface Interface Interface HL7 RIM SNOMED CT Information Model + Patient Data Structures Terminology Model + Coded Data What test was performed? How many were done? At what time? What device was used? What was the result of the test? 30088009 blood culture 55512120 antibiotic removal device 264868006 No growth 281789004 antibiotic therapy 223438000 advice to discontinue a procedure Diagram based on Figure 1 in Rector AL et al. “Interface of Inference Models with Concept and Medical Record Models” AIME 2001: 314-323

61. Using SNOMED CT and HL7 together we cannot separate the issues of information structure from those of terminology. We cannot slot any terminology into any data structure and expect it to work. SNOMED CT was designed to be syntax-neutral, so it could work with any syntax. HL7 RIM and set out to be terminology-neutral.

62. Benefits of

63. Interoperability In the context of e-health, interoperability is the way in which reliable data is provided and communicated in a secure, accurate and efficient way. It has to surmount the barriers of national policies, culture, language and systems of medical knowledge representation and use of ICT’s. Towards Interoperable eHealth for Europe. Telemedicine Alliance. BR255, November 2005

64. Classifying and Coding, which is more important? Coding means understanding? Assigning codes to concepts. Identifying the concepts. Clarifying them. Dealing with homonyms, synonyms and overlaps Enables us to identify and document interrelationships This is indeed

65. SNOMED CT is the most comprehensive, multilingual clinical healthcare terminology in the world. The value of SNOMED CT can only be realized when it is built into software and systems that are designed around it Kent Spackman: The first rule of data quality is that the quality of data collected is directly proportional to the care with which options are presented to the user. The first rule of coding is that yesterday’s data should be usable today. Heterogeneity of healthcare Healtcare data need to be permanent

66. SNOMED CT at 2009 SNOMED Reference Terminology ® Clinical Terms Version 3® 310,000 health care concepts990,000 synonyms and English descriptions1.38 million semantic relationships ICD 10 contains 10760 concepts coming in three large volumes

67. Content, Content, Content

68. Standardized Healthcare Languages Need for a normalized healthcare vocabulary. Across settings, applications, datasets. SNOMED CT, UMLS.

69. Problems of SNOMED CT

70. Percentage of SNOMED CT concept codes that are “fully defined” Eventually should reach ~70% or more of disorders, findings & procedures

72. smoker (an agent) is subsumed by tobacco smoking behaviour – finding (a role)

73. severe asthma is not a kind of asthma, but a kind of asthma finding.

74. Mixing the subsumption relation with other roles (typically part of)

75. haemoglobin subsumes haemin (instead of constitutional part)

77. Disease, observation and finding are subsumed by clinical finding

78. acute on chronic, which is both subsumed by acute and chronic

80. Smoker (an agent) and smoker (finding) (a description of a situation)

81. additional pathologic finding in tumor specimen (observable entity) and additional pathologic finding

83. 76 02.10.2006 Gergely Héja - SMCS2006 Additional problems - 2 Underspecification: roles are not quantified (existential / universal) criteria are not specified (necessary / sufficient) conversion to DL: do we have to decide in each particular case, or can it be done universally? Multiple hierarchy alcoholic beverage (through its parent ingestible alcohol) is subsumed by central depressant, ethyl alcohol and psychoactive substance of abuse – non-pharmaceutical. Alcoholic drinks contain ethyl alcohol, which plays a role of depressant and substance of abuse (with respect to human beings) Is this a general phenomenon in SNOMED? Which relations are asserted and which are inferred?

84. 77 02.10.2006 Gergely Héja - SMCS2006 Discussion - 1 The intended meaning of the categories is not always clear: possible translation errors Is it reasonable to import categories from medical classifications? Size Artificial concepts Consistency errors

85. 78 02.10.2006 Gergely Héja - SMCS2006 Discussion - 2 Real world entities listed heterogeneously Mars bar and Kit Kat (chocolate candy would suffice) UFO is subsumed by transport vehicle tendon pulley reconstruction is represented, but tendon pulley not

87. Restructure SNOMED into a high-quality ontology.

89. Future Researches

90. Some Research Challenges Extend saturation-based techniques to non-Horn fragments SNOMED users want negation and/or disjunction Non infectious Pneumonia Infectious or Malignant disorder of lung Burn injury of face neck or scalp Extend reasoning support Modularity Explanation ...

91. Some (more) Research Challenges Open questions w.r.t. query rewriting FO rewritability (AC0) only for very weak ontology languages Even for AC0 languages, queries can get very large (order ), and existing RDBMSs may behave poorly Larger fragments require (at least) Datalog engines and/or extension to technique (e.g., partial materialisation) Integrating DL/DB research Ontologies -v- dependencies Open world -v- closed world

92. Widescreen Test Pattern (16:9) Aspect Ratio Test (Should appear circular) 4x3 16x9

93. 85 SNOMED CT hierarchy

94. Why Care About Semantics? Herasy! Herasy! Herasy!

95. Why Care About Semantics? Why should I care about semantics?

96. Why Care About Semantics? Why should I care about semantics?

97. Why Care About Semantics? Why should I care about semantics? Well, from a philosophical POV, we need to specify the relationship between statements in the logic and the existential phenomena they describe.

98. Why Care About Semantics? Why should I care about semantics? Well, from a philosophical POV, we need to specify the relationship between statements in the logic and the existential phenomena they describe. That’s OK, but I don’t get paid for philosophy.

99. Why Care About Semantics? Why should I care about semantics? Well, from a philosophical POV, we need to specify the relationship between statements in the logic and the existential phenomena they describe. That’s OK, but I don’t get paid for philosophy. From a practical POV, in order to specify and test ontology-based information systems we need to precisely define their intended behaviour

100. In FOL we define the semantics in terms of models (a model theory). A model is supposed to be an analogue of (part of) the world being modeled. FOL uses a very simple kind of model, in which “objects” in the world (not necessarily physical objects) are modeled as elements of a set, and relationships between objects are modeled as sets of tuples. Why Care About Semantics?

101. Why Care About Semantics? In FOL we define the semantics in terms of models (a model theory). A model is supposed to be an analogue of (part of) the world being modeled. FOL uses a very simple kind of model, in which “objects” in the world (not necessarily physical objects) are modeled as elements of a set, and relationships between objects are modeled as sets of tuples. This is exactly the same kind of model as used in a database: objects in the world are modeled as values (elements) and relationships as tables (sets of tuples).