The Medical Entities Dictionary - © 2002 - James J. Cimino Intelligent Terminologies to Support System Interfaces: The Medical Entities Dictionary Presentation

The Medical Entities Dictionary - © 2002 - James J. Cimino

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


New York Presbyterian HospitalClinical Information Systems Architecture

Clinical Database

Medical Entities Dictionary

Database Monitor

Medical Logic Modules

DatabaseInterface

Research

Administrative

Alerts & Reminders

Results Review

. . .. . .Radiology LaboratoryDischarge

Summaries

Reformatter Reformatter Reformatter


Medical Entities Dictionary: A Central Terminology Repository


K#1 = 4.2K#1 = 3.3

K#2 = 3.2

K#1 = 3.0

Communicating Terminology Changes

K#1

K#2

K#3 = 2.6

K#3


K#1 = 4.2K#1 = 3.3

K#2 = 3.2

K#1 = 3.0

Solution: Hierarchical Integration

K#1

K#2

K

K#3

K#3 = 2.6


MED Structure

MedicalEntity

LaboratoryProcedure

CHEM-7PlasmaGlucose

LaboratorySpecimen

PlasmaSpecimen

Substance

Sampled

Part of

Has S

pecimen

Event

LaboratoryTest

DiagnosticProcedure

Substance MeasuredGlucose

Plasma

AnatomicSubstance

Substance

BioactiveSubstance

Chemical

Carbo-hydrate


The MED Today

• Concept-based (70,000)

• Multiple hierarchy (90,000)

• Synonyms (170,000)

• Translations (120,000)

• Semantic links (130,000)

• Attributes (160,000)


Support for Data Reuse

• Generated by one system, used by another

• Different granularities (lumpers and splitters)

• Different semantics (impedance mismatch)


Translations with the MED

Gentamicin

InjectableGentamicin

Gentamicn Sensitivity

Test

SerumGentamicin

Level

GentamicinToxicity

EtiologyMeasures

Sensitivity

Substance Measured Has ingredient

is-a

IntravascularGentamicin

TestsSummaryReports

DecisionRule

ExpertSystem

DrugInformation


Summary Reporting

Plasma Glucose Test

Serum Glucose TestFingerstick Glucose Test

Lab Test

Intravascular Glucose Test Chem20 Display

Lab Display


DOP Summary


WebCIS Summary


Supporting Clinical Research

• Epidemiology - symptoms, incidence, natural history of disease

• Outcomes - effectiveness of therapy, ideal length of stay

• Recruitment - identifying eligible participants


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

Linking to Expert Systems




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


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?


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.


How We Outsmarted the Lab(Before)

No Growth

Medical Logic Module

No Growth to Date


No Growth after ...

How We Outsmarted the Lab(After)

No Growth

No Growth after 48 Hours


“No Growth” Results


No Growth to Date

Medical Logic Module


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

Chest X rayCongestive

Heart Failure

Intravascular CK Test

CreatineKinase

Chest X ray 2 View

Cardiac Enzyme

Angina

Lab :1/1/99 Lab :1/1/99 Cardiac Enzyme TestCardiac Enzyme Test

Radiology :2/23/99 Radiology :2/23/99 Chest X RayChest X Ray

Radiology :2/28/96 Radiology :2/28/96 Head CTHead CT

Lab :12/28/96 Lab :12/28/96 Sickle Cell TestSickle Cell Test

Admission :3/14/96 Admission :3/14/96 StrokeStroke

Admission :2/14/98Admission :2/14/98AnginaAngina

Lab :1/1/99 Lab :1/1/99 Blood Type TestBlood Type Test

Radiology :2/1/97 Radiology :2/1/97 Knee X RayKnee X Ray

Concept-oriented(Heart)

Heart Disease

Chest

Discharge :1/15/99Discharge :1/15/99 CHFCHF

Discharge :1/15/99Discharge :1/15/99 CHFCHF

Admission :2/14/98Admission :2/14/98AnginaAngina

Lab :1/1/99 Lab :1/1/99 Cardiac Enzyme TestCardiac Enzyme Test

Radiology :2/23/99 Radiology :2/23/99 Chest X RayChest X Ray





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





Linking Text Reports to On-line Information Sources

• Natural Language Processing

• Data representation to support reuse

• Codification of information needs


MED Tools

• MUMPS MED Editor

• qrymed

• accessmed

• Web Browsers


MED Browsers



Vocabulary Construction Issues

• Understanding

• Modeling

• Creation

• Maintenance


Knowledge-based Maintenance

Theory:

"A knowledge-based approach to

vocabulary representation will improve

maintenance and utility."


Adding New Terms

• Identify redundant terms

• Put new terms into existing classes

• Create new classes where appropriate


Put Terms into Existing Classes

• Theory: The attributes of new terms can be used to identify classes

• Practice: "Pushing" Terms


“Pushing” a Term

MedicalEntity

LaboratoryTest

PlasmaGlucose

Test

BioactiveSubstance

Glucose

Carbo-hydrate

ChemistryTest

Chem-7Glucose

Test

Chem-20Glucose

Test

StatGlucose

Test

Chemical



MedicalEntity

LaboratoryTest

PlasmaGlucose

Test

BioactiveSubstance

Glucose

Carbo-hydrate

ChemistryTest

Chem-7Glucose

Test

Chem-20Glucose

Test

StatGlucose

Test

StatGlucose

Test

Chemical



MedicalEntity

LaboratoryTest

PlasmaGlucose

Test

BioactiveSubstance

Glucose

Carbo-hydrate

ChemistryTest

Chem-7Glucose

TestStat

GlucoseTest

Chem-20Glucose

Test

StatGlucose

Test

StatGlucose

Test

Chemical


Create New Classes

• Theory: Attribute patterns can be detected which identify potential classes

• Practice: Recursive partitioning of existing classes


Finding a New Class

MedicalEntity

LaboratoryTest

Hepatitis BCore Antigen

Chemical

ChemistryTest

CoreAntigenHBC

Antigen


Finding a New Class

MedicalEntity

LaboratoryTest


Chemical

ChemistryTest

CoreAntigen


Test

HBC

Antigen

MedicalEntity

LaboratoryTest


Chemical

ChemistryTest

CoreAntigenHBC

Antigen


Maintenance Tasks

• New Vocabularies (Laboratory)

• Changing Vocabularies (Pharmacy)


New Vocabulary: Laboratory

• Original lab: 2533 terms

• New lab: 5291 terms

• Vocabulary delivered: June 15, 1994

• “Go live” date: July 24, 1994


Changing Vocabulary: Pharmacy

• Started with 2091 drugs

• In two years, added 1827 drugs

• Classification by:– Ingredients– AHFS Class– Allergy– DEA– Form


Automated ClassificationMedicalEntity

Drug

PharmacologicSubstance

Sulfameth-oxizole

Chemical

Antibiotic

Bactrim"S1", "65"

Trimethoprim/Sulfamethoxizole

PreparationsTrimeth-

oprim

Septra"S1"

Sulfa Allergy"S1"

AllergyClass

TrimethoprimAllergy

"65"


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


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


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"? nCreate METOLAZONE PREPARATION" Class? y


Impact of "Theory into Practice":Better management

• Easier to merge new vocabularies

• Easier to automate change management

• Higher quality through better modeling


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


Knowledge-Based Terminology Maintenance

• Forcing explicit definitions elicits meaning

• Explicit definitions force consistency

• Inferencing possible to:– automate classification– identify new classes

• But:– Modeling is hard– Agreement on definitions is hard


The Columbia Experience

• Capturing data from ancillary systems• Natural language processing• Reusable data summaries• Merging data across systems• Support for clinical research• Linking to expert systems• Automated decision support• Smarter retrievals from the record• Linking to on-line resources• Terminology management

Documents

The Medical Entities Dictionary - © 2002 - James J. Cimino Intelligent Terminologies to Support System Interfaces: The Medical Entities Dictionary Presentation