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

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Presentation transcript:

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

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

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

Federation of Systems

Central Patient Database

Central Controlled Vocabulary

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

Issues  Understand the vocabulary  Integrate the terms  Maintenance

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

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

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

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

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

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

mumps editor - take a photo!!!

X-browser - get a stock photo!!!

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

Repairing Vocabularies  Radiology  Laboratory  Pharmacy

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

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

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

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

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

Summary reporting - stock photo!!!!

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

Medline Button - stock photo 1 !!!

Medline Button - stock photo 2 !!!

Medline Button - stock photo 3 !!!

Medline Button - stock photo 4 !!!

Medline Button - stock photo 5 !!!

MLM Alert - stock photo 1 !!!

MLM Alert - stock photo 2 !!!

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

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

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