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Managing Vocabulary for a Centralized Clinical System

Learn how to effectively manage centralized clinical systems with automated knowledge-based techniques. Explore vocabulary construction, maintenance tasks, and the impact of theory into practice for a more efficient medical informatics environment.

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Managing Vocabulary for a Centralized Clinical System

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  1. Managing Vocabulary for a Centralized Clinical System James J. Cimino, MD; Stephen B. Johnson, PhD; George Hripcsak, MD; Claire L Hill, MA; Paul D. Clayton, PhD Department of Medical Informatics - Columbia University New York, New York, USA

  2. Managing Vocabulary for a Centralized Clinical System: Automated Knowledge-Based Techniques Putting Theory into Practice

  3. Distributed Systems

  4. Central Clinical Repository(Fantasy)

  5. Central Clinical Repository(Reality)

  6. Central Controlled Vocabulary

  7. Vocabulary Construction Issues • Understanding • Modeling • Creation • Maintenance

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

  9. Medical Entities Dictionary (MED) • Semantic network of concepts • Multiple hierarchies • Frame-based concept representation • 46,000 concepts

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

  11. K+1 = 4.2 K+1 = 3.3 K+2 = 3.2 K+1 = 3.0 K+3 = 2.6 Retrieving Results Individually K+1 K+2 K+3

  12. K+1 = 4.2 K+1 = 3.3 K+2 = 3.2 K+1 = 3.0 K+3 = 2.6 Retrieving Results by Class K K#1 K#2 K#3

  13. Maintenance Tasks • New Vocabularies (Laboratory) • Changing Vocabularies (Pharmacy)

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

  15. Changing Vocabulary: Pharmacy • Started with 2091 drugs • In two years, added 2327 drugs • Classification by: • Ingredients • AHFS Class • Allergy • DEA • Form

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

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

  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 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

  21. screen shot: MED Editor

  22. screen shot: MED Editor proposing a new class

  23. 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

  24. 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

  25. 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"

  26. 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 2327 drugs added: • 28 unclassified drugs assigned classes • 141 drugs assigned multiple classes • 57 drugs discovered to be missing allergy codes

  27. Future Directions:Web-Browser • Platform independence • Available everywhere

  28. screen shot: Web

  29. Future Directions:X-Based Browser/Editor • Runs directly off vocabulary server • Multi-user environment • Ready for use in real world

  30. screen shot: Accessmed

  31. Future Directions:K-Rep • IBM product • Knowledge-based approch built in • Automated term subsumption • Moving from research to real world

  32. screen shot: K-Rep

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

  34. 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

  35. See for Yourself http://www.cpmc.columbia.edu/homepages/ciminoj

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