1. Research Issues Related to the Construction and Use of Advanced Controlled Medical Terminologies James J. Cimino, M.D. Department of Medical Informatics September 12, 2000

2. The Challenge Build a central, multipurpose clinical data repository with coded data Contributing systems have different coding systems These coding systems change over time There are no satisfactory standards

3. Solution: a Central Terminology Repository

4. K#1 = 4.2 K#1 = 3.3 K#2 = 3.2 K#1 = 3.0 K#3 = 2.6 Additional Challenge: Communication of Changes K#1 K#2 K#3

5. K#1 = 4.2 K#1 = 3.3 K#2 = 3.2 K#1 = 3.0 K#3 = 2.6 Solution: Hierarchical Integration K#1 K#2 K#3 K

6. Seeking an Elegant Solution The DXplain experience The UMLS experience

7. The Theory: "A knowledge-based approach to vocabulary representation will improve maintenance and utility."

8. The Medical Entities Dictionary (MED) Multiple hierarchy Synonyms Translations Semantic links Attributes Frame-based 65,000 concepts

9. MED Structure Medical Entity Laboratory Procedure CHEM-7 Plasma Glucose Laboratory Specimen Plasma Specimen Substance Sampled Part of Has Specimen Substance Measured Event Laboratory Test Diagnostic Procedure Plasma Anatomic Substance Bioactive Substance Glucose Chemical Carbo- hydrate

10. "A knowledge-based approach to vocabulary representation will improve maintenance and utility." The Theory: "A knowledge-based approach to vocabulary representation will improve maintenance and utility."

11. Maintenance Tasks New Vocabularies (Laboratory) Changing Vocabularies (Pharmacy)

12. New Vocabulary: Laboratory Original lab: 2533 terms New lab: 5291 terms Vocabulary delivered: June 15, 1994 “Go live” date: July 24, 1994

13. Changing Vocabulary: Pharmacy Started with 2091 drugs In two years, added 1827 drugs Classification by: – Ingredients – AHFS Class – Allergy – DEA – Form

14. Adding New Terms Identify redundant terms Put new terms into existing classes Create new classes where appropriate

15. Put Terms into Existing Classes Theory: The attributes of new terms can be used to identify classes Practice: "Pushing" Terms

16. “Pushing” a Term Medical Entity Laboratory Test Plasma Glucose Test Bioactive Substance Glucose Carbo- hydrate Chemistry Test Chem-7 Glucose Test Chem-20 Glucose Test Stat Glucose Test Chemical

17. “Pushing” a Term Medical Entity Laboratory Test Plasma Glucose Test Bioactive Substance Glucose Carbo- hydrate Chemistry Test Chem-7 Glucose Test Chem-20 Glucose Test Stat Glucose Test Stat Glucose Test Chemical

18. “Pushing” a Term Medical Entity Laboratory Test Plasma Glucose Test Bioactive Substance Glucose Carbo- hydrate Chemistry Test Chem-7 Glucose Test Stat Glucose Test Chem-20 Glucose Test Stat Glucose Test Stat Glucose Test Chemical

19. Create New Classes Theory: Attribute patterns can be detected which identify potential classes Practice: Recursive partitioning of existing classes

20. Finding a New Class Medical Entity Laboratory Test Hepatitis B Core Antigen Chemical Chemistry Test Core Antigen HBC Antigen

21. Finding a New Class Medical Entity Laboratory Test Hepatitis B Core Antigen Chemical Chemistry Test Core Antigen Hepatitis B Core Antigen Test HBC Antigen Medical Entity Laboratory Test Hepatitis B Core Antigen Chemical Chemistry Test Core Antigen HBC Antigen

22. Semi-Automated Maintenance Read formulary file Identify new drugs Link new drug to ingredient(s) Suggest classifying in “preparation” class Add new drug as per human reviewer

23. Interactive Classification Adding "LASIX 20MG TAB" Generic Ingredient "FUROSEMIDE" AHFS Class "DIURETICS" Add to "FUROSEMIDE PREPARATION"? y Adding "ZAROXOLYN 5MG CAP" Generic Ingredient "METOLAZONE" AHFS Class "DIURETICS" Add to "DIURETICS"? n Create METOLAZONE PREPARATION" Class? y

24. Automated Classification Medical Entity Drug Pharmacologic Substance Sulfameth- oxizole Chemical Antibiotic Bactrim "S1", "65" Trimethoprim/ Sulfamethoxizole Preparations Trimeth- oprim Septra "S1" Sulfa Allergy "S1" Allergy Class Trimethoprim Allergy "65"

25. Formulary Correction Statistics Among original 2091 drugs: – 334 unclassified drugs assigned classes – 289 drugs assigned multiple classes – 173 drugs discovered to be missing allergy codes Among additional 1827 drugs added: – 25 unclassified drugs assigned classes – 121 drugs assigned multiple classes – 38 drugs discovered to be missing allergy codes

26. Impact of "Theory into Practice": Better management Easier to merge new vocabularies Easier to automate change management Higher quality through better modeling

27. The Theory: "A knowledge-based approach to vocabulary representation will improve maintenance and utility."

28. Advanced Uses of Coded Data Primary use Other patient care reuse Financial Management Information transfer (messaging) Clinical research Expert systems Information retrieval Vocabulary discovery

29. Case Studies Summary reporting

30. Case Study: Summary Reporting Plasma Glucose Test Serum Glucose TestFingerstick Glucose Test Lab Test Intravascular Glucose Test Chem20 Display Lab Display

31. DOP Summary

32. WebCIS Summary

33. Case Studies Summary reporting HCFA requirements

34. Case Study: HCFA Requirements HCFA won’t pay for lab batteries Individual tests now treated as orderable procedures Need to appear in database as procedures and as tests

35. Lab Procedure Chem 7 Lab Test Intravascular Glucose Test Plasma Glucose Test Serum Glucose TestFingerstick Glucose Test Case Study: HCFA Requirements

36. Lab Procedure Chem 7 Lab Test Intravascular Glucose Test Plasma Glucose Test Serum Glucose TestFingerstick Glucose Test Orderable Test Case Study: HCFA Requirements

37. Case Studies Summary reporting HCFA requirements Clinical research

38. Clinical Research Epidemiology - symptoms, incidence, history of disease Outcomes - effectiveness of therapy, ideal length of stay Recruitment - identifying eligible participants

39. Case Studies Summary reporting HCFA requirements Clinical research Expert systems

44. Case Studies Summary reporting HCFA requirements Clinical research Expert systems Automated decision support

45. Terminology and Automated Decision Support Data monitor checks for triggering conditions Medical Logic Modules decide if warning conditions are present Message sent to appropriate channel Example: Tuberculosis culture result

46. Decision Support Example: TB Monitors for delayed culture results Sends message if result not equal to the code “No growth” One day, dozens of alerts about positive results but no organism was reported What happened?

47. How the Lab Fooled the Alert Alert looked for results = “No Growth” Lab started reporting “No Growth to Date” “No Growth to Date”  “No Growth” Solution: Use the controlled terminology to map all No-Growth-like lab terms into a single class, and have the alert logic refer to the class.

48. How We Outsmarted the Lab (Before) No Growth Medical Logic Module No Growth to Date

49. No Growth after... How We Outsmarted the Lab (After) No Growth No Growth after 48 Hours No Growth after 72 Hours “No Growth” Results No Growth after 24 Hours No Growth to Date Medical Logic Module

50. Case Studies Summary reporting HCFA requirements Clinical research Expert systems Automated decision support Linking to on-line information sources

51. Linking to On-line Resources with Terminology Clinician reviewing reports will have information needs On-line information sources can satisfy that need Data from report can be used to automate the query

52. Translations with the MED Gentamicin Etiology Measures Sensitivity Substance Measured Injectable Gentamicin Gentamicn Sensitivity Test Serum Gentamicin Level is-a Intravascular Gentamicin Tests Gentamicin Toxicity Has ingredient Summary Reports Decision Rule Expert System Drug Information

61. Linking Text Reports to On-line Information Sources Natural Language Processing Data representation to support reuse Codification of information needs

74. Impact of Better Management: More Useful Vocabulary MED is up-to-date for ancillary systems Easier to find terms in the MED Support for multiple conceptual levels More accurate database queries Support for reuse of clinical data

75. The MED Today Concept-based (65,000) Multiple hierarchy (85,000) Synonyms (149,000) Translations (103,000) Semantic links (114,000) Attributes (136,000)

78. MED Editor/Server Architecture MED MUMPS Globals Datatree MUMPS MED Editor Functions MED EditorUpdate Programs medlog MED Server Query Functions Command Line Interface MED Browser Translation Tables Unix IBM Unix

79. Unix Shared Memory Server ShMMED MS Acces MED db MedLEE NLP MEDlib MLM compiler Dxplain button Medline button MLM composition tool Integrated results design tool Web MED browser accessMED rpc MEDlib qrymed MED browser MEDviewer Lab upload extract Radiology bupload extract Bloodban upload Integrated results review ICU results display dop Data engine pse WebCIS

80. Qrymed Functions -allslts:lists all the slots, with their names. -scd:returns the slotcode with string x as its name. -snm:returns the name of slotcode x. -srecip:returns the reciprocal of slotcode x. -stype:returns the type for slotcode x. -cd:returns the medcode with string x as its name (exact match). -find:lists medcodes that have string x in their names (pattern match). -nm:returns the name for medcode x. -pnm:returns the print name for medcode x. -par:lists the parents of medcode x. -child:lists the children of medcode x. -anc:lists the ancestors of medcode x. -desc:lists the descendants of medcode x. -ianc:lists the ancestors of medcode x (including x). -idesc:lists the descendants of medcode x (including x). -slts:lists the slots of medcode x. -sltsval:lists the slots, with their values, for medcode x. -isval:lists the medcodes which have value y in slotcode x. -val:returns the value(s) of slotcode x for medcode y.

86. MED in DB2 on Mainframe 12341234 Entities 10 Name 20 UMLS 30 Part-of 40 Specimen Slots 1 10 2 10 2 20 2 30 Entity-Slots 1 10 Entity 2 10 C0001 2 40 1234 2 50 mg/dl Entity/Slot/Values 1 1 2 1 3 2 3 Ancestry

88. Future Directions Knowledge management system User interface Automated maintenance Expansion of breadth and depth