The Challenge of Reuse of Information

James J. Cimino

Columbia UniversityMIE ‘02

Budapest, Hungary

August 27, 2002

The Challenge of Reuse of Information

Overview

• Data types

• Information reuse

• Information mismatch

• Terminology solutions

• Experience

• Conclusions

Data Types

Text

Numeric

Signal

Structured

CodedStandard

CodedNLP

Interpretation

Image

Blobs

Symbols

Overview

• Data types




• Experience

• Conclusions

Information Reuse

InformationResearch

OtherClinicians

Summary

HospitalAdministration

Government

DecisionSupport

Overview

• Data types




• Experience

• Conclusions

Information Mismatch

• Form

• Meaning

• Language

• Granularity

• Semantics

• Version

Information Mismatch: Form

Information Mismatch: Form

21 22 23 24 25 26 27 28 29

7

6

5

4

3

2

1

Information Mismatch: Meaning

“Paget’sDisease”

“of the bone”

Paget’s Disease of the Breast?!?!

Information Mismatch: Language

“Tüdőgyulladás”Pneumonia?

Information Mismatch: Granularity

“Goodpasture’sSyndrome”

Does the patient have lung disease?

Information Mismatch: Semantics

AMP Sens. Test = 1:2

Should I prescribe “Ampicillin 250mg

Caps”?

Information Mismatch: Version

Patient has hantavirus infection

“Virus, NEC”

2001 ICD: - Smallpox - Cowpox - Virus, NEC

2002 ICD: - Smallpox - Cowpox - Hantavirus - Virus, NEC

Overview

• Data types




• Experience

• Conclusions

Terminology Solutions

• Standards

• Distribution

• Semantic Representation

Terminology Solutions: Standards

• Advantages– Less duplication of work– “Plug and play” compatibility

• Disadvantages– Cost of adoption– Unresponsive to change– Developers Users

Terminology Solutions: Distribution

• Media– 9-track tape– Floppy disks– CD-ROM– Web

• Models– ICD: annual– UMLS: change files– HL7: server

Terminology Solutions: Semantic Representation

• Concept oriented

• Concept permanence

• True is-a hierarchies

• Multiple hierarchies (heterarchy)

• Semantic relationships

• Inheritance

Terminology Solutions: Semantic Representation

Goodpasture’sSyndrome

Kidney Disease

Hemoptysis Hematuria

Finding

Lung Kidney

Organ

has-site

Lung Disease

is-a

has-finding

Semantic Representation: Galen

• Structured Meta Knowledge from Pen&Pad

• Common Reference Terminology

• Requires terminology server

• Automated classification

• Open source terminology

Semantic Representation: GalenFracture which <

hasLocation BonehasCause Condition>

Fracture which <hasLocation (AnatomicalNeck which isDivisionOf Femur)hasCause (Osteoporosis which hasCause

PostMenopausalChange)>

Can be classified as:FractureFracture which hasLocation LongBone.Fracture which hasLocation (AnatomicalNeck which

isDivisionOf LongBone).Fracture which hasLocation Thigh.Fracture which hasLocation Hip.Lesion which isCausedBy Osteoporosis.Lesion which isCausedBy PostmenopausalChange.

Semantic Representation:SNOMED-CT

• Merger of SNOMED and Read Clinical Terms

• Reference terminology

• Many domains

• Heterarchy

• Semantic relations (roles)

• Postcoordination

• >300,000 concepts

Semantic Representation:SNOMED-CT

is-a

BacterialPneumoniaTularemia

PulmonaryTularemia

has-causative-agent

Francisellatularensis

LungStructure

has-finding-site

Inflammation

associated-morphology

Semantic Representation: LOINC

• Logical Observations, Identifiers, Names and Codes

• Codes for observations in HL7 messages

• Fully-specified names

• Codes for orderable observations

• Codes for results

Semantic Representation: LOINC

24356-8 | URINALYSIS PANEL

5778-6 | COLOR | COLOR | PT | UR | NOM

22705-8 | GLUCOSE | SCNC | PT | UR | QN | TEST STRIP

YellowRedColorless…

Semantic Representation: Drugs

• Food and Drug Administration

• Veterans Administration

• National Library of Medicine

• Drug knowledge base vendors

• Common model for Clinical Drug

• RxNorm

Semantic Representation: Drugs

Clinical Drug

Ingredient Class

Ingredient

is-a

ChemicalsDrug Class

Not-Fully-Specified Drug

is-a

is-a

Medications

International Package Identifiers

Country-Specific Packaged Product

is-a

Packages

Trademark Drug

Manufactured Components

is-a

is-a

Composite Clinical Drug

is-a

Composite Trademark Drug

is-a

Semantic Representation: MED

• Medical Entities Dictionary

• Data dictionary and controlled terminology

• Columbia-Presbyterian Medical Center

• Heterarchy

• Semantic network

• Multiple domains

• >70,000 concepts

Semantic Representation: MED

LaboratoryProcedure

CHEM-7PlasmaGlucose

Test

Substance

Sampled

Part of

Has S

pecimen

Substance Measured

MedicalEntity

Event

LaboratoryTest

DiagnosticProcedure

Substance

BioactiveSubstance

Glucose

Chemical

Carbo-hydrate

LaboratorySpecimen

PlasmaSpecimen

Plasma

AnatomicSubstance

Overview

• Data types




• Experience

• Conclusions

Matching Granularity and Semantics

Gentamicin

InjectableGentamicin

Gentamicin Sensitivity

Test

SerumGentamicin

Level

is-a

IntravascularGentamicin

Tests

GentamicinToxicity

EtiologyMeasures

Sensitivity

Substance Measured Has ingredient

SummaryReports

DecisionRule

ExpertSystem

DrugInformation

Example of Reuse:Summary Reporting

• Spreadsheets for trends in lab data

• Defined as concepts in the MED

• Linked to test classes


Plasma Glucose Test

Serum Glucose TestFingerstick Glucose Test

Lab Test

Intravascular Glucose Test Chem20 Display

Lab Display

Example of Reuse: Merging Data

• Merger between Presbyterian Hospital and New York Hospital

• Separate departmental systems

• Common repository

• Merger of terms in MED allows cross-institution data aggregation


45748 - Diazepam 5 mg Tablet

28727 - CPMC Drug: Diazepam 5 mg Tab29952 - CPMC Drug: UD Diazepam 5 mg Tab34734 - CPMC Drug: UD Diazepam 5mg Tab35346 - CPMC Drug: UD Diazepam 5 mg Tab.62523 - Cerner Drug: Diazepam Tab 5 mg

46888 - Diazepam Tablets

31136 - Diazepam Preparations

24015 - Benzodiazepine Preparations28107 - Drug Enforcement Administration (DEA)

Class IV - Drug with Low Abuse Potential 28129 - Drug Allergy Class: Benzodiazepines

28203 - Tablet


2478 - Plasma Glucose Measurement

32308 - Intravascular Glucose Test 32101 - Plasma Chemistry Test

35836 - CPMC Laboratory Test: Glucose Tolerance, 6hr35836 - CPMC Laboratory Test: Glucose Tolerance, 6hr35837 - CPMC Laboratory Test: Glucose Tolerance, Fasting35838 - CPMC Laboratory Test: Glucose, 1/2 Hour36337 - CPMC Laboratory Test: Glucose, Fasting 250005 - NYH Lab Procedure: Glucose, Plasma50078 - NYH Lab Procedure: Glucose, 0 H50079 - NYH Lab Procedure: Glucose, 2 PP50080 - NYH Lab Procedure: Glucose, 0.5 H50081 - NYH Lab Procedure: Glucose, 1 H50082 - NYH Lab Procedure: Glucose, 2 H50084 - NYH Lab Procedure: Glucose, 3 H50107 - NYH Lab Procedure: Glucose, 1.5 H50108 - NYH Lab Procedure: Glucose, 4 H50109 - NYH Lab Procedure: Glucose, 5 H50110 - NYH Lab Procedure: Glucose, 6 H50111 - NYH Lab Procedure: Ogtt,Gest Screen,(50g)

1523 - Presbyterian Plasma Glucose Test 1601 - Presbyterian Plasma Glucose Measurement 1652 - Allen Plasma Glucose Measurement33807 - New CHEM-7 Plasma Glucose Measurement35454 - CPMC Laboratory Test: Old Plasma Glucose

Measurement35815 - CPMC Laboratory Test: Glucose, Challenge35816 - CPMC Laboratory Test: Glucose, Fasting35817 - CPMC Laboratory Test: Glucose, 1hr Post Prandial35818 - CPMC Laboratory Test: Glucose, 2hr Post Prandial35819 - CPMC Laboratory Test: Glucose, Random35821 - CPMC Laboratory Test: Glucose35831 - CPMC Laboratory Test: Glucose Tolerance, 1hr35832 - CPMC Laboratory Test: Glucose Tolerance, 2hr35833 - CPMC Laboratory Test: Glucose Tolerance, 3hr35834 - CPMC Laboratory Test: Glucose Tolerance, 4hr35835 - CPMC Laboratory Test: Glucose Tolerance, 5hr

Example of Reuse:Automated Decision Support

• Data stored in repository reviewed in real time

• Arden Syntax rules triggered by data

• Generation of alerts and reminders

• High-level concepts in rules map to low-level concepts in database

Automated Decision Support: Tuberculosis

• 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.

Automated Decision Support: Tuberculosis

No Growth

Medical Logic Module

No Growth to Date

No Growth after ...

How We Outsmarted the Lab

No Growth

No Growth after 48 Hours


“No Growth” Results


No Growth to Date

Medical Logic Module

Example of Reuse:Information Retrieval

UnderstandInformation

Needs

1

Get InformationFrom EMR

2

AutomatedTranslation

5

ResourceTerminology

4

Presentation

7

Querying

6

ResourceSelection

3

Example of Reuse: Expert Systems

• Expert system has high-level concepts

• Database has quantitative results

• Semantic mismatch

• Translation through semantic net traversal

Example of Reuse: Expert Systems

1600 44 L

1600 Gluc

32703 SerumGlucose Tests

32308 IntravascularGlucose Tests

42485 ElevatedAbnormal Findingin Body Substance

42486 DecreasedAbnormal Findingin Body Substance

42541 Hyperglycemia 3286 Hypoglycemia

32412 IntravascularSpecimen

31987 Glucose

42527 AbnormalLevel of Blood

Glucose

3286 Hypoglycemia

Expert System: DXplain

Expert System: Lipid Guideline

Lab :1/1/99 Cardiac Enzyme Test

Radiology :2/23/99 Chest X Ray

Radiology :2/28/96 Head CT

Lab :12/28/96 Sickle Cell Test

Admission :3/14/96 Stroke

Admission :2/14/98Angina

Lab :1/1/99 Blood Type Test

Radiology :2/1/97 Knee X Ray

Discharge :1/15/99 CHF

MedicalRecord

Example of Reuse:Problem-Oriented Views

Chest X ray

IntravascularCK Test

CreatineKinase

Chest X ray 2 View

Cardiac Enzyme

CongestiveHeart Failure

Angina

Heart Disease

Chest

Admission :2/14/98Angina

Lab :1/1/99 Cardiac Enzyme Test

Radiology :2/23/99 Chest X Ray

Discharge :1/15/99 CHF

Heart MED

Experience with Information Reuse

• Summary reporting

• Merging data

• Automated decision support

• Information retrieval

• Expert systems

• Problem-oriented views

Overview

• Data types




• Experience

• Conclusions

Information, Then and Now

- Cimino JJ, Int J Biomed Comput. 1994; 34:185-194

DischargeDiagnoses

Radiology Reports

PhysicalExams

DischargeSummaries

PatientHistories

Medication Lists

Uncoded,Unstructured

Uncoded,Structured

LocallyCoded

UniversallyCoded

Then (1993)



DischargeDiagnoses

Radiology Reports

PhysicalExams

DischargeSummaries

PatientHistories

Medication Lists


Uncoded,Structured

LocallyCoded

UniversallyCoded

Near Future



DischargeDiagnoses

Radiology Reports

PhysicalExams

DischargeSummaries

PatientHistories

Medication Lists


Uncoded,Structured

LocallyCoded

UniversallyCoded

Far Future

Current Status

DischargeDiagnoses

Radiology Reports

PhysicalExams

DischargeSummaries

PatientHistories

Medication Lists


Uncoded,Structured

LocallyCoded

UniversallyCoded

Current Status

Uncoded,Structured

Standard Semantic

Terminology

Standard Code Set

DischargeDiagnoses

Radiology Reports

PhysicalExams

DischargeSummaries

PatientHistories

Medication Lists


Current Status

DischargeDiagnoses

LaboratoryReports

ProblemLists

TextReports

Text Reports

Medication Lists


Uncoded,Structured

Standard Semantic

Terminology

Standard Code Set

Conclusions

• Advanced health care means information reuse

• Semantic-based terminologies support reuse

• Terminologies are moving in the right direction

The Challenge of Reuse of Information

Documents

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