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Drug Terminology Modeling in HL7: Coordination Efforts and Challenges

Explore the ongoing efforts and challenges in drug terminology coordination within HL7, discussing the need for precise medication terms and different drug classification models. Discover the complexities in matching drug descriptions across multiple terminologies and the quest for interoperability.

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Drug Terminology Modeling in HL7: Coordination Efforts and Challenges

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  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 • Trademark • Active ingredients • Dosage form

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

  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 NameFormIngredient 1 Ingredient 2 Valium 5mg Tablet Tablet Diazepam ^5^mg Tylenol #3 Tablet Acetaminophen Codeine ^325^mg ^30^mg Chloral Hydrate Syrup Syrup Chloral Hydrate 100.000000^mg^1.000000^ml

  14. Descriptions from Vendors ABCDENone - 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. SetMapped A 358 B 459 C 605 D 405 E 566

  15. Pairwise Comparisons SetBCDE A 340 358 326 357 B 452 361 449 C 393 554 D 392 E

  16. Pairwise Comparisons { SetBCDE 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. 53% Overall Matching

  23. Pairwise Comparisons SetABCDE 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 SetABCDE 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 LIQUID|10;100|MG/5ML;MG/|LIQUID SYRUP| GUAIFENESIN^00000000.000^^0000.000^ CODEINE PHOSPHATE^.^^.^ LIQUID| CODEINE PHOSPHATE^2.000000^MG^^ GUAIFENESIN^20.000000^MG^1.000000^ML LIQUID, ORAL (SYSTEMIC)| CODEINE PHOSPHATE^10^MG^5^ML GUAIFENESIN^100^MG^5^ML LIQUID| CODEINE PHOSPHATE^10.0000^MG/5ML^^ GUAIFENESIN^100.0000^MG/5ML^^ SYRUP| CODEINE PHOSPHATE^10^MILLIGRAM(S)^5^MILLILITER(S) GUAIFENESIN^100^MILLIGRAM(S)^5^MILLILITER(S)

  26. Example of Mismatch RECOMBIVAX HB ADULT FORMULATION INJECTION|10|MCG|INJ-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?) • Don’t mix in route or concentration • Strengths: • Conversion algorithms • Rules for defaults • Don’t 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

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