An Approach to Using Controlled-Vocabularies in Clinical Information Systems

1. An Approach to Using Controlled-Vocabularies in Clinical Information Systems Jeff Wilcke, DVM, MSc, DACVCP and Art Smith, M.S.

2. Lists of words… Nomenclature The system or set of names for things, etc., commonly employed by a person or community (Petchamp, SNVDO, SNOMED) Vocabulary A collection or list of words with explanations of their meanings (SNOMED) Classification The result of classifying; a systematic distribution, allocation, or arrangement, in a class or classes; esp. of things which form the subject-matter of a science or of a methodic inquiry. (SNOMED)

3. What do we need? Nomenclature ONLY Provides a simple list for data entry Vocabulary / Classification We can be CERTAIN that the “term” (description in SNOMED) means what we think it means. We can develop rules that allow us to combine concepts to express ideas more complicated than those contained in the nomenclature. We can use the knowledge base supported by the vocabulary/classification to search, retrieve and analyze our data.

4. Vocabulary Suitability Adequate Content Multiple granularities Functional Subsetting Rich Semantic Structure

5. Adequate Content Lower boundary? Values for all patient-care context(s) in the medical record system. Values to allow for patient and patient-care specific specializations. left, severe, chronic, etc. Upper boundary? Adequate content IS NOT the same thing as “any conceivable medical utterance”. Some content belongs to specialized vocabularies Pharmacy (e.g., specific brand name items)

6. Multiple granularities Granularities appropriate for various patient care settings. Problem list Fractured femur Surgery report summary Closed spiral fracture of the midshaft of the femur

7. Functional Sub-setting We only need PORTIONS of SNOMED for any one part of a Clinical Information System (CIS) We need DIFFERENT portions of SNOMED for different parts of CIS. We must be able to use ALL of SNOMED to search, retrieve, analyze data produced using sub-sets.

8. Medical Record Semantics CIS Data Structure Meaning of fields carried in the data dictionary for the CIS Meta-semantics Internal Vocabulary Semantics Instance-semantics Structurally identical to Meta-semantics Some attributes from the Vocabulary Some attributes used ONLY in instance semantics

9. CIS Data structure

10. Find a Balance

11. Middle Ground CIS “Content” Problem list Rule out Final diagnosis Treatments Surgical Procedures Diagnostic Procedures Vocabulary “Content” Body system Morphology Etiology Approach Instrument Generic drug name

12. Finding Middle Ground Option A – Single “findings” field “Problem diabetes mellitus” “Final diagnosis diabetes mellitus” “Tentative diagnosis diabetes mellitus” “Rule-out diabetes mellitus”

13. Finding Middle Ground Option B – Multiple “findings” fields Problem list field – value = “diabetes mellitus” Final diagnosis field – value = “diabetes mellitus” Rule-out field – value = “diabetes mellitus”

14. SNOMED RT Definition

15. Meta-semantics The built-in semantic structure of the nomenclature system itself. Object – attribute – value triples Defined and sanctioned attributes Associated Morphology Associated Topography Associated Etiology Allowed value sets

16. Defined Attribute(s) “ASSOC-ETIOLOGY names the direct causative agent (organism, toxin, force) of a disease or disorder. It does not include vectors (such as the mosquito that transmits malaria). It also does not include method or mechanism by which the etiology is introduced to the body”.

17. Instance semantics Instance-semantics are used to express a particular occurrence of a concept by allowing the addition of details. Object – attribute – value triples Instance attributes Has severity Has laterality Has duration

18. Defined Attribute(s) “HAS LATERALITY* names the specific organ when that organ exists as left and right pairs (such as left and right femur)”

19. Medical Record “Instance”

20. Choosing the correct object

21. Choosing the correct object Refineability (SNOMED) It is NOT a left fracture Fracture of shaft of femur is not refineable by laterality, but has associated topography shaft of femur. It is NOT a left shaft Shaft of femur is not refineable by laterality, but “is a” femur structure. It IS a left femur Femur is refineable.

22. Refineability(SNOMED) The instance semantics need not include the femur itself to establish laterality, but must processed against the meta-semantics. Rule (sic) – “When laterality is processed against a finding, assume that it is assigned to the topography. If the first occurrence of topography is not refineable by laterality, find a parent that is refineable.”

23. SNOMED Structure Concepts are linked to other concepts by specific named Relationships (which are also concepts in SNOMED). The full linkage of associated concepts can be staggering -- not just a tree, but rather a complex network of relationships. Concepts and Relationships are stored in separate relational tables.

24. Complicated!

25. SNOMED Tables(two of them, anyway) Concepts Concept ID SNOMED ID Status Fully Specified Name Relationships Concept ID 1 Relationship ID Concept ID 2 Refineability (SNOMED CT only)

26. SNOMED Tables

27. Instance Semantics Structure Must follow the same pattern as the SNOMED structure to allow seamless searching. The table structure used to represent the nomenclature should also be used to represent controlled vocabulary entries. There are some differences, though….

28. Differences in Semantics Meta Semantics Each concept appears just once (abstract concepts). Linkage can be and usually is a complex network Abstract (defining) relationships (e.g., IS-A, Part-of, Associated Topography) Instance Semantics Concepts may occur more than once (concrete instances). Linkage is a simple tree structure (modeling a noun phrase) Concrete (qualifying) relationships (e.g., Has laterality, Has severity, Associated-topography)

29. Consolidation of caudal and middle lobes of the right lung.(Tree Structure) Note: More than one identical relationship (Assoc. Topog.) Note: Identical targets (Right) – each is a separate instance of the concept. Laterality rule: “When laterality is processed against a finding, assume that it is assigned to the topography. If the first occurrence of topography is not refineable by laterality, find its “part-of parent” that IS.” (Meta-Semantics)Note: More than one identical relationship (Assoc. Topog.) Note: Identical targets (Right) – each is a separate instance of the concept. Laterality rule: “When laterality is processed against a finding, assume that it is assigned to the topography. If the first occurrence of topography is not refineable by laterality, find its “part-of parent” that IS.” (Meta-Semantics)

30. Three Sources of Information The field in the system: Data entered under the “Discharge diagnosis” field is semantically different than the same data in the “Rule-out list” field. The data entered in that field: Either a single SNOMED concept or a phrase constructed using explicit instance semantics. The nomenclature system: The related SNOMED concepts determined by the implicit meta-semantics.

31. Searching the TableUsing Semantics When searching the table, automatically expand all concepts to include their IS-A descendants (children, grandchildren, etc.). A match on any of those is considered a match on the parent. Consider an example: “Find all diagnoses of lung disease occurring in the caudal lobe”

32. “Find all diagnoses of lung diseases occurring in the caudal lobe.” Search for a diagnosis field entry with both D2-50000 (Disease of Lung) AND T-28A20 (Caudal Lobe of Lung) in the Value column. Expand with IS-A descendants D2-50000 has 41 IS-A children including D2-61010 (Abscess of Lung). Many of these children have IS-A children which are also included. T-28A20 has no IS-A children. Search for: (D2-50000 or D2-61010 or…) and T-28A20

33. “Tight” vs. “Loose” Searches Tight Search: Target known to match search criteria. Find all diagnoses of known lung disease that are known to occur in the caudal lobe of the lung. Lose Search: Target might match search criteria. Find all diagnoses that may be lung disease that may be located in the caudal lobe of the lung. Generally we want a “tight” search. Looking at our example search…

34. “Tight” vs. “Loose” Searches Consider a diagnosis of simply D2-61010 Abscess of Lung A “Tight” search would not find this diagnosis. It does not match the criteria or the IS-A descendants of the criteria (i.e., not known to be caudal lobe). A “Loose” search would find this diagnosis. It could match the criteria or the IS-A descendants of the criteria (i.e., it might be in the caudal lobe). “Tight” searches match criteria and their IS-A descendants. “Loose” searches match criteria, their IS-A descendants AND their IS-A ancestors.

35. Pre-coordinated vs. Post-coordinated Concepts Pre-coordinated concept: DD-13152 = Closed fracture of shaft of femur Post-coordinated concept phrase: DD-13100 = Fracture of femur Associated Morphology Fracture, Closed Associated topography Shaft of Femur

36. Pre-coordinated vs. Post-coordinated Concepts Missing M-44000 Granulomatous Inflammation in the precoordinated term is probably an oversight to be corrected in SNOMED Searching using D0-C0000 Subcutaneous fat disorder would miss the post-coordinated term, but search on disorders that have an Associated Topography of T-03020 Subcutatneous fatty tissue would find both the pre- and post-coordinated terms.Missing M-44000 Granulomatous Inflammation in the precoordinated term is probably an oversight to be corrected in SNOMED Searching using D0-C0000 Subcutaneous fat disorder would miss the post-coordinated term, but search on disorders that have an Associated Topography of T-03020 Subcutatneous fatty tissue would find both the pre- and post-coordinated terms.

37. Instance Template

38. Coding an Instance(why?) We need a compact yet unambiguous format for representing an instance structure. Transmission of records Storage of record data for presentation NOT for searches or statistical reports. Two forms – verbose and terse Terse: concepts, attributes & values are just codes. T-12710 Verbose: concepts, attributes & values contain English T-12710[Femur]

39. Coding an Instance(how?) Concept Concept ( attribute : value ) Concept ( attribute1 : value1; attribute2 : value2 ) Concept (attribute1 : value1 ( attribute 1.1 : value1.1 ) ; attribute2 : value2 ) Concept ( attribute1 : value1 ( attribute1.1 : value1.1 ; attribute1.2 : value1.2 ) )

40. Storing an instance(why?) We need a way to store the data in a relational database that will facilitate searches and statistics Must allow for representation of structure. Must fit the relational model (tables). Must allow easy searching for concepts. Must be efficient Simple concepts take little space. More complicated instances take more space.

41. Storing an instanceFive columns in Table Key –unique identifier for the row. Entry –unique identifier for the instance. Single concept instance is one row. Complicated instances take multiple rows. Parent – for attribute/value modifiers. Key for concept that they modify. Empty for main concept. Attribute Shows attribute (relationship type) for attribute/value modifiers. Empty for main concept Value Shows main concept or value of a attribute/value modifiers. Searchable field (indexed). Match pulls all rows with same Entry.

42. Example 1

45. Closed spiral fracture of shaft of the left femur(Suggested Storage Form)

47. Example 2

50. Abscess of caudal lobe of right lung due to Mannheimia heamolytica (Relational Table)

52. Example 3

53. Consolidation of caudal lobe of right lungand middle lobe of left lung.(Tree Structure)

54. Consolidation of caudal lobe of right lungand middle lobe of left lung. (Coding)

55. Consolidation of caudal lobe of right lungand middle lobe of left lung.(Relational Table)

57. Requirements for Incorporation in a Clinical Information System System must provide appropriate sub-vocabularies for each C.V. field. Controlled Vocabulary is a data type, like date/time. User interface must allow selection from sub-vocabulary. User interface must provide a mechanism for constructing phrases. System must support the semantic structure (both meta-semantics and instance-semantics) to allow for searches and statistical reports.

58. Cost to the Vendors(dollars, time, and space) SNOMED-RT license (probably passed-on). Construction of sub-vocabularies for each field. This includes main concepts, relationships (attributes) and modifiers (values). Concept and Relationship tables for every term in the sub-vocabularies and all their parents (by any relationship). Phrase tables for each field (not just a single entry). Automatic expansion of searches to include all IS-A descendants.

59. Cost to the Users (YOU!) Attention to precise definitions of SNOMED concepts. (G.I.G.O.) Construction of meaningful phrases for an appropriate granularity (whatever that is). If it’s not entered, you can’t find it. Constructing phrases will never be as easy as typing a sentence. How close it comes depends on the vendor’s interface. Passed-on development and licensing costs. Disk space for vocabulary tables.

60. Benefits to the Users (YOU!) Accurate and complete searches. Can search on concepts not directly entered, but implied by the meta-semantics. Use to locate specific cases, do retrospective studies, or generate outcome-based reviews. Accurate and complete statistics. Know what cases you are really seeing. Know what referrals you are really making. Pooled data for research (e.g., VMDB) How does your practice compare with others? Distribution of diagnoses, procedures, etc.

61. Will the Vendors Do It? Only if YOU demand it! Are the benefits meaningful to you? Are you willing to take the time to enter data with a controlled vocabulary? Are you willing to pay the passed-on costs from the vendors? That cost is dependent on how many of you demand it.