1. Integrating Clinical Systems by Integrating Controlled Vocabularies James J. Cimino, MD Center for Medical Informatics Columbia University New York, New York, USA

2. ...collecting patient information ...links to financial systems ...automated decision support ...library at the bedside Integrated Information Means...

3.  Stand-alone clinical systems  Several billing systems  Clinical alert system  Local- and wide-area networks  On-line educational resources The Columbia Computing Environment

4. Federation of Systems

5. Central Patient Database

6. Central Controlled Vocabulary

7. Advantages of Central Vocabulary  Resource for system developers  Single update point  Brings vocabularies together

8. Issues  Understand the vocabulary  Integrate the terms  Maintenance

9. Central Vocabulary Design  Semantic network of concepts  Content from: –Unified Medical Language System (UMLS) –Ancillary systems –Standard controlled vocabularies  Browsing and editing tools

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

11. Vocabulary Sources  Clinical laboratories  Pharmacy  Cardiology  Discharge coding  Radiology  Primary Care

12. Current Size  41,000 concepts  146 slots  51,500 parent/child links  40,000 other semantic links  240,000 strings

13. Vocabulary Browser/Editors  Knowledge Engineering Environment (KEE) - 386 and 370  PC-MUMPS  RS/6000 and X-Windows

14. Editor Functions  Lexical and graphic browsing  Addition and deletion functions  Inheritance  Automatic classification

16. mumps editor - take a photo!!!

17. X-browser - get a stock photo!!!

18. Success Stories  Repairing vocabularies  Database extension  Confidential data display  Summary reporting  Medline Button  Medical Logic Modules

19. Repairing Vocabularies  Radiology  Laboratory  Pharmacy

20. Autoclassifying the Laboratory Stat Serum Glucose Laboratory Test Plasma Glucose Fasting Glucose Chem-7 Glucose SMAC20 Glucose Stat Serum Glucose Blood Glucose Plasma Glucose Fasting Glucose Chem-7 Glucose SMAC20 Glucose Laboratory Test Glucose Test Serum Glucose Serum Glucose

21. Autoclassify the Pharmacy: Before Drug Empirin #3 Bayer Aspirin Salicylate Allergy Analgesic Opiate Allergy Nonsteroidal Anti-inflamm. Allergy

22. Autoclassify the Pharmacy: After Drug Empirin #3 Bayer Aspirin Aspirin Drugs Codeine Drugs Salicylate Allergy Analgesic Opiate Allergy Nonsteroidal Anti-inflamm. Allergy

23. Results of Autoclassification  971 new classes  334 drugs classified by type  289 drugs into two or more types  173 drugs into allergy classes

24. Database Extension  Information model for new applications  Merging the new with the old  Adding organization to the model

25. Summary reporting - stock photo!!!!

26. Confidential Data Display  Need to display sensitive data (HIV)  Need an extra level of security  Step 1: create a new class in the MED  Step 2: have the application check for class membership

27. Medline Button - stock photo 1 !!!

28. Medline Button - stock photo 2 !!!

29. Medline Button - stock photo 3 !!!

30. Medline Button - stock photo 4 !!!

31. Medline Button - stock photo 5 !!!

32. MLM Alert - stock photo 1 !!!

33. MLM Alert - stock photo 2 !!!

34. Medical Logic Modules  Clinical alerts  Data interpretation  Finding research patients  Monitoring hospital resources

35. Current Challenges  Editing environment  Expansion of automated updates  Vocabulary content

36. Conclusions  Integrating vocabularies helps  Complex vocabulary model pays off  Automated maintenance works