HL7 Drug Name Coordination Efforts James J. Cimino, M.D. Columbia University - PowerPoint PPT Presentation

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HL7 Drug Name Coordination Efforts James J. Cimino, M.D. Columbia University

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Title: Modeling Clinical Drug Terms Author: Jim Cimino Last modified by: Jim Cimino Created Date: 4/18/1999 11:51:56 PM Document presentation format – PowerPoint PPT presentation

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Title: HL7 Drug Name Coordination Efforts James J. Cimino, M.D. Columbia University


1
HL7 Drug Name Coordination EffortsJames J.
Cimino, M.D.Columbia University
2
Drug Terminology Efforts in HL7
  • Need for medication terms
  • Much source data generated by pharmacy systems
  • Pharmacy knowledge base vendors are exploring
    ways to place their terminologies in the public
    domain
  • Vocabulary TC convened meetings to explore ways
    of collaborating

3
Disclaimer
  • Nothing is a balloted standard yet
  • TC has not yet proposed a formal model
  • TC has not yet proposed a plan of action
  • TC is still defining scope and goals

4
Drug Terminology Modeling
  • Some agreement on the concept of a "clinical
    drug"
  • More-specific terms
  • Less-specific terms
  • Dosage forms
  • Routes

5
Clinical Drugs
  • Active ingredients
  • Dosage form

6
Trademark Drugs
  • Active ingredients
  • Dosage form
  • Trademark

7
Manufactured Components
  • Active ingredients
  • Dosage form
  • Trademark
  • Inactive ingredients
  • Dispensing and Inventory
  • Manufacturer (may not be the same as supplier)

8
Drug Model Hierarchy
Packages
Medications
Drug Class
International Package Identifiers
Chemicals
is-a
Not-Fully-Specified Drug
is-a
Ingredient Class
is-a
Country-Specific Packaged Product
Clinical Drug
is-a
is-a
is-a
Ingredient
Composite Clinical Drug
Trademark Drug
is-a
is-a
Manufactured Components
Composite Trademark Drug
9
Drug Model Hierarchy
Packages
Medications
Drug Class
International Package Identifiers
Chemicals
is-a
Not-Fully-Specified Drug
is-a
Ingredient Class
is-a
Country-Specific Packaged Product
Clinical Drug
is-a
is-a
is-a
Ingredient
Composite Clinical Drug
Trademark Drug
is-a
is-a
Manufactured Components
Composite Trademark Drug
10
Clinical Drugs
  • Dosage form
  • Active ingredients
  • Chemical
  • Form Strength
  • Strength amount
  • Strength units
  • Volume
  • Volume units

11
Experiment
  • Can model allow for interoperability?
  • Single terminology vs.
  • Mapping between terminologies
  • Select random sample of drug terms
  • Obtain descriptions from terminology developers
  • Compare description components
  • Examine overall match rate

12
Sample Selection
  • 71,000 NDC Codes
  • 1000 selected at random (1.4)
  • Many are obsolete

13
Descriptions from Vendors
  • Name Form Ingredient 1 Ingredient 2
  • Valium 5mg Tablet Tablet Diazepam
  • 5mg
  • Tylenol 3 Tablet Acetaminophen Codeine
  • 325mg 30mg
  • Chloral Hydrate Syrup Syrup Chloral Hydrate
  • 100.000000mg1.000000ml

14
Descriptions from Vendors
  • Set Mapped
  • A 358
  • B 459
  • C 605
  • D 405
  • E 566

A B C D E None
- 340 358 326 357 642 340 -
452 361 449 541 358 452 -
393 554 395 326 361 393 -
392 395 357 449 554 392 - 434 Overall,
367 terms were not represented in any set, 71
appeared in only one set, 77 appeared in exactly
two sets, 83 appeared in three sets, 91 appeared
in four, and 311 terms appeared in all five
terminologies.
15
Pairwise Comparisons
  • Set B C D E
  • A 340 358 326 357
  • B 452 361 449
  • C 393 554
  • D 392
  • E

16
Pairwise Comparisons
  • Set B C D E
  • A 340 358 326 357
  • B 452 361 449
  • C 393 554
  • D 392
  • E


3982
17
Comparisons
  • Dosage Form 3982
  • Ingredient (number and match) 5507
  • Dose Strength (dose, units, volume, volume
    units) 4337
  • Overall 3982

18
Dosage Form Matching
  • TAB TABLET
  • LIQUID ? ORAL LIQUID
  • 111 Dosage form synonyms

19
Ingredient Matching
  • HCl vs. Hydrochloride
  • Salt vs. Base
  • Inclusion of form or route
  • Mention of animal source

20
Matching Other Components
  • Dose Strength Matching
  • Dose Units Matching
  • Dose Volume Matching
  • Dose Volume Units Matching

21
Overall Matching
  • Form matches
  • Same number of ingredients
  • Each ingredient matches on chemical and all four
    other parameters

22
Overall Matching
23
Pairwise Comparisons
  • Set A B C D E
  • A - 340 358 326 357
  • B 340 - 452 361 449
  • C 358 452 - 393 554
  • D 326 361 393 - 392
  • E 357 449 554 392 -

24
Pairwise Complete Matches
  • Set A B C D E
  • A - 59 45 48 52
  • B 59 - 54 63 62
  • C 45 54 - 46 48
  • D 48 63 46 - 58
  • E 52 62 48 58 -

25
Example of Mismatch
  • GUAIFENESIN AC LIQUID10100MG/5MLMG/LIQUID
  • SYRUP
  • GUAIFENESIN00000000.0000000.000
  • CODEINE PHOSPHATE..
  • LIQUID
  • CODEINE PHOSPHATE2.000000MG
  • GUAIFENESIN20.000000MG1.000000ML
  • LIQUID, ORAL (SYSTEMIC)
  • CODEINE PHOSPHATE10MG5ML
  • GUAIFENESIN100MG5ML
  • LIQUID
  • CODEINE PHOSPHATE10.0000MG/5ML
  • GUAIFENESIN100.0000MG/5ML
  • SYRUP
  • CODEINE PHOSPHATE10MILLIGRAM(S)5MILLILITER(S)
  • GUAIFENESIN100MILLIGRAM(S)5MILLILITER(S)

26
Example of Mismatch
  • RECOMBIVAX HB ADULT FORMULATION
    INJECTION10MCGINJ-SUS
  • HEPATITIS B VIRUS VACCINE
  • HEPATITIS B SURFACE ANTIGEN
  • HEPATITIS B VACCINE-RECOMBINANT
  • HEPATITIS B VIRUS VACCINE RECOMBINANT
  • HEPATITIS B VACCINE RECOMBINANT

27
Discussion
  • Matching is still far from perfect
  • Not surprising, given lack of standards for
    attribute values
  • Next steps

28
Discussion Next Steps
  • Define some rules for each field
  • Select new random sample
  • Find subset with good overlap across
    terminologies
  • Submit descriptions of new subset

29
Discussion New Rules
  • Dose forms separate translation step
  • Ingredients
  • Right number
  • Specific chemical entity
  • Identifiers (UMLS?)
  • Dont mix in route or concentration
  • Strengths
  • Conversion algorithms
  • Rules for defaults
  • Dont mix route or concentration with strength

30
Conclusions
  • Glass half empty
  • How can we do automated translation of patient
    data?
  • Can drug order transfers and decision support be
    safe?
  • Glass half full
  • No attempt yet to standardize attribute
    terminology
  • Most translation was much better than 50
  • Just getting started
  • Better than what we do now

31
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