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Evaluation of a Proposed Method for Representing Drug Terminology James J. Cimino, Timothy J. McNamara, Terri Meredith, Carol A. Broverman, Karen C. Eckert.

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Presentation on theme: "Evaluation of a Proposed Method for Representing Drug Terminology James J. Cimino, Timothy J. McNamara, Terri Meredith, Carol A. Broverman, Karen C. Eckert."— Presentation transcript:

1 Evaluation of a Proposed Method for Representing Drug Terminology James J. Cimino, Timothy J. McNamara, Terri Meredith, Carol A. Broverman, Karen C. Eckert

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 Model Hierarchy Drug Class Not-Fully-Specified Drug Clinical Drug Trademark Drug Manufactured Components Ingredient Class International Package Identifiers Country-Specific Packaged Product Ingredient is-a Chemicals Medications Packages Composite Clinical Drug is-a Composite Trademark Drug is-a

5 Drug Model Hierarchy Drug Class Not-Fully-Specified Drug Trademark Drug Manufactured Components Ingredient Class International Package Identifiers Country-Specific Packaged Product Ingredient is-a Chemicals Medications Packages Composite Clinical Drug is-a Composite Trademark Drug is-a Clinical Drug

6 Clinical Drugs Dosage form Active ingredients –Chemical –Form Strength Strength amount Strength units Volume Volume units

7 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

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

9 Descriptions from Vendors NameForm Ingredient 1 Ingredient 2 Valium 5mg TabletTablet Diazepam ^5^mg Tylenol #3Tablet AcetaminophenCodeine ^325^mg^30^mg Chloral Hydrate SyrupSyrup Chloral Hydrate ^mg^ ^ml

10 Descriptions from Vendors SetMapped A 358 B 459 C 605 D 405 E 566 A B C D E None 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.

11 Pairwise Comparisons Set B C D E A B C D 392 E

12 Pairwise Comparisons Set B C D E A B C D 392 E { 3982

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

14 Dosage Form Matching TAB = TABLET LIQUID  ORAL LIQUID 111 Dosage form synonyms

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

16 Dose Strength Matching Standard format ( vs ) Normalization of units (GM vs. MG)

17 Dose Units Matching Standard abbreviations Normalization of units (GM vs. MG) Missing values Inclusion of concentration information MG vs. %

18 Dose Volume Matching Not given Different numeric formats Defaults (0 and 1) Different volumes ( per 1ml vs per 5ml)

19 Dose Volume Units Matching Not given “Each” Different abbreviations

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

21 Overall Matching 53%

22 Pairwise Comparisons Set A B C D E A B C D E

23 Set A B C D E A - 59%45%48%52% B59% - 54%63%62% C45%54% - 46%48% D48%63%46% - 58% E52%62%48%58% - Pairwise Complete Matches

24 Example of Mismatch GUAIFENESIN AC LIQUID|10;100|MG/5ML;MG/|LIQUID SYRUP| GUAIFENESIN^ ^^ ^ CODEINE PHOSPHATE^.^^.^ LIQUID| CODEINE PHOSPHATE^ ^MG^^ GUAIFENESIN^ ^MG^ ^ML LIQUID, ORAL (SYSTEMIC)| CODEINE PHOSPHATE^10^MG^5^ML GUAIFENESIN^100^MG^5^ML LIQUID| CODEINE PHOSPHATE^ ^MG/5ML^^ GUAIFENESIN^ ^MG/5ML^^ SYRUP| CODEINE PHOSPHATE^10^MILLIGRAM(S)^5^MILLILITER(S) GUAIFENESIN^100^MILLIGRAM(S)^5^MILLILITER(S)

25 Example of Mismatch BUMEX INJECTION|0.25|MG|INJ-SOL AMPUL BUMETANIDE^ ^MG^ ^ML INJECTION SODIUM CHLORIDE, IV USE^ ^%^^ BUMETANIDE, INJECTABLE^ ^MG^ ^ML INJECTION BUMETANIDE^0.25^MG^1^ML SOLUTION BUMETANIDE^0.2500^MG/ML^^ SOLUTION BUMETANIDE^0.25^MILLIGRAM(S)^1^MILLILITER(S)

26 Example of Mismatch RECOMBIVAX HB ADULT FORMULATION INJECTION|10|MCG|INJ-SUS VIAL HEPATITIS B VIRUS VACCINE^ ^MCG^ ^ML INJECTION HEPATITIS B SURFACE ANTIGEN^ ^MCG^ ^ML INJECTION HEPATITIS B VACCINE-RECOMBINANT^10^MCG^1^ML INJECTION HEPATITIS B VIRUS VACCINE RECOMBINANT^ ^MCG/ML ^^ SOLUTION HEPATITIS B VACCINE RECOMBINANT^10^MICROGRAM(S)^ 1^MILLILITER(S)

27 Focus on Dosage Form 3982 comparisons 111 synonyms 72% match rate What went wrong?

28 Examination of Dosage Forms Compare use of each form, in each source, with use in other sources for corresponding medications Look for irregularities in source Look for 100% agreement When not 100% agreement, look at hierarchical subsumption

29 Irregularities in Sources NDC: |FLUORITAB TABLETS CHEWABLE CHERRY|1.1|MG| TAB CHEW |100|BOT: A:: |FLUORITAB 0.5MG TABLET CHEW^SODIUM FLUORIDE| CHEWABLE TABLET |SODIUM FLUORIDE^ ^MG ^ ^ B: |FLUORITAB CHERRY TABS CHEW, 0.5MG| CHEWABLE TABLET | SODIUM FLUORIDE^ ^MG^^ C: |SODIUM FLUORIDE CHEW TAB 1.1 MG (0.5MG F)| CHEWABLE TABLET |SODIUM FLUORIDE^1.1000^MG^^ D: || TABLET |SODIUM FLUORIDE^0.5^MG^^

30 Near Misses and the Hierarchy A Sodium Fluoride 1.1 MG Chewable Tablet B C D Tablet Chewable Tablet is-a Sodium Fluoride 1.1 MG Tablet

31 Irregularities in Sources NDC: |PROPRANOLOL HYDROCHLORIDE EXTENDED RELEASE CAPSULES|80|MG| CAP ER |100|BOT A: || CAPSULE CR |PROPRANOLOL HYDROCHLORIDE^80^MG^^ B: |PROPRANOLOL 80MG CAPSULE SA^PROPRANOLOL HCL| CAPSULE CR |PROPRANOLOL HCL^ ^MG^ ^ C:: |PROPRANOLOL HCL CAP CR 80 MG| CAPSULE CR | PROPRANOLOL HYDROCHLORIDE^ ^MG^^ D:: |PROPRANOLOL HYDROCHLORIDE^80 MG| CAPSULE CR | PROPRANOLOL HYDROCHLORIDE^80^MILLIGRAM(S)^1^EACH E:: |PROPRANOLOL HCL TABS CR, 80MG| TABLET CR |PROPRANOLOL HYDROCHLORIDE^ ^MG^^

32 Example of 100% Agreement A: LOTION: 1: LOTION| B: LOTION: 1: LOTION| C: LOTION: 1: LOTION| D: LOTION: 1: LOTION| E: LOTION: 1: LOTION|

33 Example of 100% Agreement A: TABLET CR: 14: TAB E REL|TAB FC ER B: TABLET CR: 6: TAB FC ER|TAB SUG CO| C: TABLET CR: 9: TAB FC ER|TAB E REL| D: TABLET CR: 14: TAB E REL|TAB FC ER|TAB FC| E: TABLET CR: 7: TAB FC ER|TAB E REL|

34 Example of <100% Agreement A: AEROSOL: 2: INHALANT|AEROSL SPR| B: AEROSOL: 1: INHALANT| C: AEROSOL: 1: INHALANT| SPRAY, TOPICAL, DERMAL: 1: AEROSL SPR| D: AEROSOL: 2: INHALANT|AEROSL SPR| E: AEROSOL: 1: INHALANT|

35 Handling Ambiguity Aerosol Inhaled Aerosol is-a Dermal Aerosol A Triamcinolone Acetonide 0.147mg/GM Aerosol A Terbutaline Sulfate 0.2mg/ACT Aerosol D Terbutaline Sulfate 0.2mg/ACT Inhaled Aerosol C E B C Triamcinolone Acetonide 0.147mg/GM Dermal Aerosol is-a

36 Example of Subsumption Aerosol (): A(AEROSOL) D(AEROSOL) Inhaled Aerosol (INHALENT): B(AEROSOL) C(AEROSOL) E(AEROSOL) Dermal Aerosol (AEROSL SPR): C(SPRAY TOPICAL, DERMAL)

37 No Solution for Solution AMPUL DROPS INFUS. BTL INJECTION IRRIGATION SOLUTION IV SOLN. KIT PIGGYBACK PLAST. BAG SKIN TEST SOLUTION SYRUP VIAL ELIXIR INJ REPOSITORY INJECTION INJECTION TYPE INJECTION IVPB KIT LIQUID SOLUTION SUSPENSION DROPS, OPHTHALMIC (NON-SYSTEMIC) INJECTION IRRIGATION SOLUTION KIT, INJECTION (SYSTEMIC) SOLUTION FOR DROPS, OTIC SOLUTION FOR INHALATION... SOLUTION OPHTMALMIC… SOLUTION, ORAL (SYSTEMIC) SOLUTION, OTIC SOLUTION, TOPICAL, DERMAL SYRUP CONCENTRATE INJECTION KIT NEBU OIL SOLUTION SYRUP

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

39 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

40 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

41 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

42


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