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Overview

Intelligent Terminologies to Support System Interfaces: The Medical Entities Dictionary Presentation to Guidant Corporation April 15, 2002. Overview. What is the MED? Support for Clinical Information Systems Support for Data Reuse MED Tools Knowledge-based Maintenance.

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Overview

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  1. Intelligent Terminologies to Support System Interfaces:The Medical Entities DictionaryPresentation to Guidant CorporationApril 15, 2002

  2. Overview • What is the MED? • Support for Clinical Information Systems • Support for Data Reuse • MED Tools • Knowledge-based Maintenance

  3. Medical Logic Modules Clinical Database Alerts & Reminders Database Monitor Results Review Database Interface Administrative Medical Entities Dictionary Research Reformatter Reformatter Reformatter . . . . . . Radiology Discharge Summaries Laboratory New York Presbyterian HospitalClinical Information Systems Architecture

  4. Medical Entities Dictionary: A Central Terminology Repository

  5. 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 Communicating Terminology Changes

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

  7. 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

  8. The MED Today • Concept-based (70,000) • Multiple hierarchy (90,000) • Synonyms (170,000) • Translations (120,000) • Semantic links (130,000) • Attributes (160,000)

  9. Support for Data Reuse • Generated by one system, used by another • Different granularities (lumpers and splitters) • Different semantics (impedance mismatch)

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

  11. Lab Display Lab Test Intravascular Glucose Test Summary Reporting Chem20 Display Fingerstick Glucose Test Serum Glucose Test Plasma Glucose Test

  12. DOP Summary

  13. WebCIS Summary

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

  15. Patient Recruitment • Study of bisphosphonates in hypercalcemia • Potential subjects treated before enrollment • Alert checked for elevated calcium and sent message to research fellow • Enrollment was complete in two months

  16. Linking to Expert Systems

  17. Linking to Expert Systems

  18. Linking to Expert Systems

  19. 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

  20. 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?

  21. 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.

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

  23. “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

  24. Smarter Retrievals from the Record • Repository stores events and results • Clinical problems at a different level of granularity • Re-use knowledge to map from problems to clinical data • Produce problem-specific views of the medical record

  25. Cardiac Enzyme Heart Disease Chest Creatine Kinase Chest X ray Congestive Heart Failure Angina Intravascular CK Test Chest X ray 2 View Admission :2/14/98 Angina Discharge :1/15/99 CHF Radiology :2/23/99 Chest X Ray Lab :1/1/99 Cardiac Enzyme Test Concept-oriented (Heart) Admission :3/14/96 Stroke Lab :12/28/96 Sickle Cell Test Radiology :2/28/96 Head CT Lab :1/1/99 Blood Type Test Radiology :2/1/97 Knee X Ray Admission :2/14/98 Angina Discharge :1/15/99 CHF Radiology :2/23/99 Chest X Ray Lab :1/1/99 Cardiac Enzyme Test

  26. Linking to On-line Resources with Terminology • Reviewing reports will generate information needs • On-line information sources can satisfy that need • Data from report can be used to automate the query

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

  28. MED Tools • MUMPS MED Editor • qrymed • accessmed • Web Browsers

  29. MED Browsers

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

  31. Knowledge-based Maintenance Theory: "A knowledge-based approach to vocabulary representation will improve maintenance and utility."

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

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

  34. “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

  35. “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

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