Controlled Terminologies in Patient Care and Research: An Informatics Perspective James J. Cimino, M.D. Department of Biomedical Informatics Columbia University

Controlled Terminologies in Patient Care and Research: An Informatics Perspective

James J. Cimino, M.D.Department of Biomedical Informatics

Columbia University

Overview

• Motivation for data encoding: reuse

• Challenges to encoding with controlled terminologies

• Approach at Columbia/NY Presbyterian Hospital

• Desiderata for controlled terminologies

• Successful data reuse at Columbia/NYPH

Problems We Are Trying to Solve

• Collecting data from disparate sources

• Aggregating like data

• Sharing data

• Reusing data– Patient care– Administrative functions– Research– Automated decision support

Information Form and Reuse

21 22 23 24 25 26 27 28 29

7

6

5

4

3

2

1

Information Form and Reuse

Patient Care Data

Research Data

?

Finds what is mentioned but not what is discussed (ambiguity, redundancy,

false positives, false negatives)

Text Processing

Text Images

Patient Care Data

Research Data

Text Images

Natural Language Processing

Feature Extraction

Controlled terminology; distinguishes what is

discussed from what is mentioned (concept

oriented)

Patient Care Data

Research Data

Text Images

Encoded Data

Controlled Terminologies

Gender Causes of Death

ReuseSymbolic

Manipulation

Knowledge Networks

Data Mining

Knowledge

Patient Care

Case PresentationThe patient is a 50 year old female who presents to the emergency room with the chief complaint of cough and chest pain. The patient reports that she has had a productive cough for three days but that chest pain developed one hour ago.

She reports that she was treated in the past for tuberculosis while she was pregnant, and that she is allergic to Bufferin.

Physical examination reveals a well-developed, well-nourished female in moderate respiratory distress. Vital signs showed a pulse of 90, a respiratory rate of 22, an oral temperature of 101.3, and a blood pressure of 150/100. Examination reveals rales and rhonchi in the left upper chest.

Labs: Chem7 (serum): Glucose 100Chem7 (plasma): Glucose 150CBC: Hgb 15, Hct 45, WBC 11,000A fingerstick blood sugar was 80Urinalysis showed protein of 1+ and glucose of 0

Chest X-ray: Left upper lobe infiltrate, left ventricular hypertrophy

The patient is started on antibiotics and aspirin and is admitted to the hospital.

A medical student reviewing the case is concerned about patients with pneumonia and myocardial infarction. She decides to do a literature search.

The ER physician is wondering if this patient could be heralding an epidemic.

Reuse of Clinical Dataa) To what bed should the patient be admitted?

b) What were all the results of the patient's blood glucose tests (including serum, plasma and fingerstick)?

c) Does the patient have a history of tuberculosis?

d) Is the patient allergic to any ordered medications?

e) How often are patient with the diagnosis of myocardial infarction started on beta blockers?

f) Can the patient’s data be used by an expert system?

g) Can the patient’s data be used to search health literature?

h) Does the patient represent an index case in an epidemic?

i) Does the patient meet the criteria for a clinical trial of patients over the age of 50 with elevated blood pressure?

To what bed should the patient be admitted?

“Patient is an 50 year old female…”

Admission Discharge Transfer System

“Put the patient in Room 5, Bed B…”

Electronic Medical Record

To what bed should the patient be admitted?

But: how does the computer know the patient is female?

The record could say:

“female”

“Female”

“FEMALE”

“F”

“Woman”

“Girl”

Coding the Data: Gender

• Data element - gender

• Controlled terminology: Male, Female, Unknown

• Representation: M,F,U; 0,1,2

• What about other values?

What’s the Gender?

What are the blood glucose test results?

420 ICD9-CM Tuberculosis Codes (plus 69 hierarchical codes)

010. PRIMARY TB INFECTION*

010.0 PRIMARY TB COMPLEX*

010.00 PRIM TB COMPLEX-UNSPEC

010.01 PRIM TB COMPLEX-NO EXAM

010.02 PRIM TB COMPLEX-EXM UNKN

010.03 PRIM TB COMPLEX-MICRO DX

010.04 PRIM TB COMPLEX-CULT DX

010.05 PRIM TB COMPLEX-HISTO DX

010.06 PRIM TB COMPLEX-OTH TEST

010.1 PRIMARY TB PLEURISY*

010.8 PRIM PROGRESSIVE TB NEC*

010.9 PRIMARY TB INFECTION NOS*

011. PULMONARY TUBERCULOSIS*

012. OTHER RESPIRATORY TB*013. CNS TUBERCULOSIS*014. INTESTINAL TB*015. TB OF BONE AND JOINT*016. GENITOURINARY TB*017. TUBERCULOSIS NEC*018. MILIARY TUBERCULOSIS*

Does the patient have a history of tuberculosis?

Thirteen TB codes not under 01x.137. LATE EFFECT TUBERCULOSIS*137.0 LATE EFFECT TB, RESP/NOS137.1 LATE EFFECT CNS TB137.2 LATE EFFECT GU TB137.3 LATE EFF BONE & JOINT TB137.4 LATE EFFECT TB NEC647. INFECTIVE DIS IN PREG*647.3 TUBERCULOSIS IN PREG*647.30 TB IN PREG-UNSPECIFIED647.31 TUBERCULOSIS-DELIVERED647.32 TUBERCULOSIS-DELIV W P/P647.33 TUBERCULOSIS-ANTEPARTUM647.34 TUBERCULOSIS-POSTPARTUM


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

Patient Care Data

Research Data

Text Images

Encoded Data



ReuseSymbolic

Manipulation

Quality Control

Desiderata

Knowledge Networks

Data Mining

Knowledge

Patient Care

Terminology Desiderata

• Concept orientation• Concept permanence• Nonsemantic identifiers• Polyhierarchy• Reject “Not Elsewhere Classified”• Formal definitions

Cimino JJ. Desiderata for controlled medical vocabularies in the Twenty-First Century. Methods of Information in Medicine; 1998;37(4-5):394-403.

Polyhierarchy

disease

cholera meningitis

infectious disease lung disease

tuberculosis

tuberculosis in pregnancy

infectious diseasein pregnancy

K#1 = 4.2K#1 = 3.3

K#2 = 3.2

K#1 = 3.0

Communication with Hierarchies

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

Communication with Hierarchies

K#1

K#2

K

K#3

K#3 = 2.6

Reject “Not Elsewhere Classified”

The “Will Rogers Phenomenon”: During the Great Dust Bowl Era, when Oakies moved to California, the IQ in both states increased.

1995

Viral Hepatitis Mortality

1994 1995 1996

070.1

070.3

070.5

Diagnosis ICD9-CM Code

ICD9-CM Name

Hepatitis A 070.1 Hepatitis A

Hepatitis B 070.3 Hepatitis B

Hepatitis C 070.5 Hepatitis NEC

Hepatitis E 070.5 Hepatitis NEC

1996

Diagnosis ICD9-CM Code

ICD9-CM Name

Hepatitis A 070.1 Hepatitis A

Hepatitis B 070.3 Hepatitis B

Hepatitis C 070.4 Hepatitis C

Hepatitis E 070.5 Hepatitis NEC

Formal Definitions in the MED

MedicalEntity

LaboratoryProcedure

CHEM-7PlasmaGlucose

Test

LaboratorySpecimen

PlasmaSpecimen

Substance

Sampled

Part of

Has S

pecimen

Event

LaboratoryTest

DiagnosticProcedure

Substance MeasuredGlucose

Plasma

AnatomicSubstance

Substance

BioactiveSubstance

Chemical

Carbo-hydrate

MED Data ModelMED Code Slot Code Value 1600 4 32703, 50000 1600 6 "Serum Glucose Measurement" 1600 8 1724 1600 16 31987 1600 18 "mg/dl" 1600 39 "50" 1600 40 "110" 1600 212 "2345-7" 1724 6 "SMAC" 31987 6 "Glucose" 32703 6 "Serum Glucose Tests“ 50000 6 "CPMC Lab Test "

Slot Slot Name 4 SUBCLASS-OF 6 PRINT-NAME 8 PART-OF 16 SUBSTANCE-

MEASURED 18 UNITS 39 LOW-NORMAL-VALUE 40 HIGH-NORMAL-VALUE 212 LOINC-CODE

Concept OrientedConcept Permanence

NonsemanticIdentifier

Polyhier- archy

FormalDefinitions

Using the MED

MED QueryMEDTranslation

TableInterfaceEngine

WebCIS

DecisionSupport

The MED and Messaging

AncillarySystem

LocalCodes

MEDCodes

ClinicalData

Repository

OtherSubscribers

Interface EngineTranslation

Table

Using the MED• Translation

– What is the display name for …?– What is the ICD9 Code for …?– What is the aggregation class for …?

• Translation Tables

• Class-based questions– Is Piroxicam a nonsteroidal antiinflammatory drug?– What are all the antibiotics?

• Knowledge queries– What are the pharmaceutic ingredients of…?

What’s in the MED?• Sunquest lab terms

• Cerner lab terms

• Digimedix drugs

• Cerner Drugs

• Sunquest Radiology

• ICD9-based problem list terms

• Eclipsys order catalogue

• Other applications

• Knowledge terms

The MED Today

• “Concept”-based (102,071)

• Multiple hierarchy (152,508)

• Synonyms (883,095)

• Translations (436,005)

• Semantic links (395,854)

• Attributes (2,030,184)


Using the MED for Summary Reporting

Plasma Glucose Test

Serum Glucose TestFingerstick Glucose Test

Lab Test

Intravascular Glucose Test Chem20 Display

Lab Display


DOP Summary


WebCIS Summary


Eclipsys Summary


Adapting to Changing Requirements

• Labs ordered as panels of tests

• HCFA will only reimburse for tests

• Clinicians have to order tests separately

• But: they want to review them as panels

• Changing the architecture:– Order tests separately– Group them for display– 2 FTEs– 4 months of work

• Solution: 5 minute change in the MED

Lab Tests and Procedures in the MED

Chem7 SMAC

Lab Procedures

CBC

Lab Tests

GlucoseSodium Hematocrit

Lab Tests and Procedures in the MED

Lab Tests

GlucoseSodium

Chem7 SMAC

Lab Procedures

CBC

Hematocrit

OrderableTests

1) Check the drugs’ allergy codes, or…

2) Infer the allergy codes from the MED, or…

3) Use formal definitions in the MED to check ingredients

Bufferin Enteric-Coated Aspirin

Aspirin PreparationsAspirin

has-ingredient

Allergy: Bufferin

Ordered Medications: Enteric-Coated Aspirin

If ingredient of allergic drug equals ingredient of ordered drug, then send alert

Is the patient allergic to any ordered medications?

TuberculosisInfection

Primary TB Pleurisy 010.1

Primary TBComplex 010.0

PrimaryTB (010)

PulmonaryTB (011)

Other RespTB (012)

Primary TBPleurisyNo Exam 010.11

Primary TBPleurisyUspec010.10

Late EffectTB (137)

TB inPreg (647.3)

Infective Diseasein Pregnancy (647)

Primary TBComplex No Exam 010.01

Primary TBComplex

Uspec010.00


How often are patient with the diagnosis of myocardial infarction started on beta blockers?

2000 2001 2002 2003 2004

MI

MI+Beta

select patient_id , time = primary_time

from visit2004_diagnosis

where diagnosis_code = 2618

and b.primary_time between '01/01/2000' and '01/01/2005'

and b.comp_code = 28144

How often are patient with the diagnosis of myocardial infarction started on beta blockers?

Potassium

Hypokalemia

Serum Potassium Test

Serum Specimen

Serum

Abnormalities ofSerum Potassium

Can the patient’s data be used by an expert system?




Gentamicin

InjectableGentamicin

Gentamicn Sensitivity

Test

SerumGentamicin

Level

GentamicinToxicity

EtiologyMeasures

Sensitivity

Substance Measured Has ingredient

DrugInformation

ExpertSystem

PubMed

Can the patient’s data be used to search health literature?

LabManual

Patient Care Data

Research Data

Text Images

Encoded Data



ReuseSymbolic

Manipulation

Quality Control

Desiderata

Knowledge Networks

Data Mining

Knowledge

Patient Care

Reuse of Clinical Data

Reuse of Clinical Dataa) To what bed should the patient be admitted?

b) What were all the results of the patient's blood glucose tests (including serum, plasma and fingerstick)?

c) Does the patient have a history of tuberculosis?

d) Is the patient allergic to any ordered medications?

e) How often are patient with the diagnosis of myocardial infarction started on beta blockers?

f) Can the patient’s data be used by an expert system?

g) Can the patient’s data be used to search health literature?

h) Does the patient represent an index case in an epidemic?

i) Does the patient meet the criteria for a clinical trial of patients over the age of 50 with elevated blood pressure?

Terminology is key to data integration and reuse

High-quality terminology supports high-quality data integration and reuse

“Desiderata” facilitate high quality

Columbia/NYPH Medical Entities DictionaryServes as a repository for institutional and standard

terminologiesUses multihierarchy semantic networkSupports sophisticated data integrationSupports sophisticated data reuse

Conclusions

Questions?

Documents

Controlled Terminologies in Patient Care and Research: An Informatics Perspective James J. Cimino, M.D. Department of Biomedical Informatics Columbia University