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Research Issues Related to the Construction and Use of Advanced Controlled Medical Terminologies

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. The Challenge. Build a central, multipurpose clinical data repository with coded data

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Research Issues Related to the Construction and Use of Advanced Controlled Medical Terminologies

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

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

  10. The Theory: "A knowledge-based approach to vocabulary representation will improve maintenance and utility." "A knowledge-based approach to vocabulary representation will improve maintenance and utility." "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 Chemical Laboratory Test Carbo- hydrate Stat Glucose Test Bioactive Substance Chemistry Test Plasma Glucose Test Glucose Chem-7 Glucose Test Chem-20 Glucose Test

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

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

  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 Chemical Chemistry Test Antigen Core Antigen HBC Hepatitis B Core Antigen

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

  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 Allergy Class Chemical Drug Sulfa Allergy "S1" Trimethoprim Allergy "65" Antibiotic Pharmacologic Substance Trimethoprim/ Sulfamethoxizole Preparations Sulfameth- oxizole Trimeth- oprim Septra "S1" Bactrim "S1", "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." "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. Lab Display Lab Test Intravascular Glucose Test Case Study:Summary Reporting Chem20 Display Fingerstick Glucose Test Serum Glucose Test Plasma Glucose Test

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

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

  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

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

  41. 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

  42. 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?

  43. 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.

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

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

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

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