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Controlled Terminologies in Patient Care and Research: An Informatics Perspective

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Title: Representation and Coding of Medical Data G4020 Author: Jim Cimino Last modified by: James Cimino Created Date: 2/1/1999 8:43:06 PM Document presentation format – PowerPoint PPT presentation

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Title: Controlled Terminologies in Patient Care and Research: An Informatics Perspective


1
Controlled Terminologies in Patient Care and
Research An Informatics Perspective
James J. Cimino, M.D.Department of Biomedical
InformaticsColumbia University
2
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

3
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

4
Information Form and Reuse
5
Information Form and Reuse
6
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7
Research Data
Patient Care Data
Text
Images
Natural Language Processing
Feature Extraction
8
Research Data
Patient Care Data
Text
Images
9
Case Presentation
  • The 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 100
  • Chem7 (plasma) Glucose 150
  • CBC Hgb 15, Hct 45, WBC 11,000
  • A fingerstick blood sugar was 80
  • Urinalysis 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.

10
Reuse of Clinical Data
  1. To what bed should the patient be admitted?
  2. What were all the results of the patient's blood
    glucose tests (including serum, plasma and
    fingerstick)?
  3. Does the patient have a history of tuberculosis?
  4. Is the patient allergic to any ordered
    medications?
  5. How often are patient with the diagnosis of
    myocardial infarction started on beta blockers?
  6. Can the patients data be used by an expert
    system?
  7. Can the patients data be used to search health
    literature?
  8. Does the patient represent an index case in an
    epidemic?
  9. Does the patient meet the criteria for a clinical
    trial of patients over the age of 50 with
    elevated blood pressure?

11
To what bed should the patient be admitted?
  • Patient is an 50 year old female

12
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

13
Coding the Data Gender
  • Data element - gender
  • Controlled terminology Male, Female, Unknown
  • Representation M,F,U 0,1,2
  • What about other values?

14
Whats the Gender?
15
What are the blood glucose test results?
16
420 ICD9-CM Tuberculosis Codes (plus 69
hierarchical codes)
Does the patient have a history of tuberculosis?
011. PULMONARY TUBERCULOSIS 012. OTHER
RESPIRATORY TB 013. CNS TUBERCULOSIS 014. INTEST
INAL TB 015. TB OF BONE AND JOINT 016. GENITOURI
NARY TB 017. TUBERCULOSIS NEC 018. MILIARY
TUBERCULOSIS
  • 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

17
Thirteen TB codes not under 01x.
Does the patient have a history of tuberculosis?
  • 137. LATE EFFECT TUBERCULOSIS
  • 137.0 LATE EFFECT TB, RESP/NOS
  • 137.1 LATE EFFECT CNS TB
  • 137.2 LATE EFFECT GU TB
  • 137.3 LATE EFF BONE JOINT TB
  • 137.4 LATE EFFECT TB NEC
  • 647. INFECTIVE DIS IN PREG
  • 647.3 TUBERCULOSIS IN PREG
  • 647.30 TB IN PREG-UNSPECIFIED
  • 647.31 TUBERCULOSIS-DELIVERED
  • 647.32 TUBERCULOSIS-DELIV W P/P
  • 647.33 TUBERCULOSIS-ANTEPARTUM
  • 647.34 TUBERCULOSIS-POSTPARTUM

18
New York Presbyterian HospitalClinical
Information Systems Architecture
19
Medical Entities Dictionary A Central
Terminology Repository
20
Communicating Terminology Changes
21
Research Data
Patient Care Data
Text
Images
22
Terminology Desiderata
Cimino JJ. Desiderata for controlled medical
vocabularies in the Twenty-First Century.
Methods of Information in Medicine
199837(4-5)394-403.
  • Concept orientation
  • Concept permanence
  • Nonsemantic identifiers
  • Polyhierarchy
  • Reject Not Elsewhere Classified
  • Formal definitions

23
Polyhierarchy
disease
infectious disease
lung disease
24
Communication with Hierarchies
25
Communication with Hierarchies
K1
K2
26
Reject Not Elsewhere Classified
1995
1996
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
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
  • The Will Rogers Phenomenon During the Great
    Dust Bowl Era, when Oakies moved to California,
    the IQ in both states increased.

27
Formal Definitions in the MED
Medical Entity
CHEM-7
Plasma Glucose Test
28
MED Data Model
MED 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
29
Using the MED
MED
30
The MED and Messaging
Ancillary System
31
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?

32
Whats 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

33
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)

34
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37
What are the blood glucose test results?
38
Using the MED for Summary Reporting
What are the blood glucose test results?
Chem20 Display
Serum Glucose Test
Fingerstick Glucose Test
Plasma Glucose Test
39
DOP Summary
What are the blood glucose test results?
40
WebCIS Summary
What are the blood glucose test results?
41
Eclipsys Summary
What are the blood glucose test results?
42
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

43
Lab Tests and Procedures in the MED
44
Lab Tests and Procedures in the MED
Lab Procedures
Lab Tests
Chem7
SMAC
CBC
Orderable Tests
Hematocrit
Glucose
Sodium
45
Is the patient allergic to any ordered
medications?
  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

Allergy Bufferin Ordered Medications
Enteric-Coated Aspirin If ingredient of allergic
drug equals ingredient of ordered drug, then send
alert
Bufferin
Enteric-Coated Aspirin
46
Does the patient have a history of tuberculosis?
Tuberculosis Infection
47
How often are patient with the diagnosis of
myocardial infarction started on beta blockers?
48
How often are patient with the diagnosis of
myocardial infarction started on beta blockers?
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
49
Can the patients data be used by an expert
system?
Serum Potassium Test
50
Can the patients data be used by an expert
system?
51
Can the patients data be used by an expert
system?
52
Can the patients data be used by an expert
system?
53
Can the patients data be used to search health
literature?
Injectable Gentamicin
Serum Gentamicin Level
Gentamicin
Gentamicn Sensitivity Test
Gentamicin Toxicity
54
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57
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58
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61
Reuse of Clinical Data
Research Data
Patient Care Data
Text
Images
62
Reuse of Clinical Data
  1. To what bed should the patient be admitted?
  2. What were all the results of the patient's blood
    glucose tests (including serum, plasma and
    fingerstick)?
  3. Does the patient have a history of tuberculosis?
  4. Is the patient allergic to any ordered
    medications?
  5. How often are patient with the diagnosis of
    myocardial infarction started on beta blockers?
  6. Can the patients data be used by an expert
    system?
  7. Can the patients data be used to search health
    literature?
  8. Does the patient represent an index case in an
    epidemic?
  9. Does the patient meet the criteria for a clinical
    trial of patients over the age of 50 with
    elevated blood pressure?

63
Conclusions
  • 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 Dictionary
  • Serves as a repository for institutional and
    standard terminologies
  • Uses multihierarchy semantic network
  • Supports sophisticated data integration
  • Supports sophisticated data reuse

64
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