# Presentation: 904681

## Computer Assisted Decision Analysis in Drug Naming

**Bruce L. Lambert, Ph.D.**
**Department of**
** ****Pharmacy Administration**
**University of Illinois at Chicago**
**lambertb@uic.edu**

## Overview

- How can computer resources be used to objectively measure differences between name pairs?
- How can computer resources be used to calculate weights for various elements in name similarity?
- Can computer assisted pattern recognition support the decision process to determine name/name similarities?

## Preface: Need to Change Focus!

- Names are not enough: Must focus on drug *products *not drug *names*
- Similarity is not enough: Must focus on similarity and *frequency* (of prescribing)
- Error reduction is not enough: Must focus on harm reduction
- How to balance public risk against private/corporate benefit?

## Objective Measures of Similarity

- N-gram: based on proportion of n-letter subsequences that two names have in common
- Bigram: two-letter subsequences (e.g., Premarin=Pr, re, em, ma, ar, ri, in)
- Trigram: three-letter subsequences (Pre, rem, etc.)
- Edit distance: the number of insertions deletions or substitutions needed to transform one name into another

## Objective Measures of Similarity

- N-gram and edit distance measures can be used on any formal representation of the name (spelling or phonological)
- Use phonetic alphabet (e.g., International Phonetic Alphabet or ARPAbet)
- ARPAbet: *Zyprexa*: z ay p r eh k s ax
- Phoneme bigrams: z ay, ay p, p r, r eh, eh k, k s, s ax

## Objective Measures of Similarity

- There are many variations on these basic measures
- Add spaces before or after to emphasize beginning or ending of name
- Use different weights depending on position of letters
- Use different equations to compute numerical similarity (Dice, Hamming, etc.)
- Allow approximate matches between letters (e.g., m=n, a=e=i=o=u)

## Objective Measures of Similarity

- Power of simple descriptive analyses
- Ten most common three-letter prefixes in US brand names:
- Pro-, Bio-, Car-, Tri-, Vit-, Pre-, Nut-, Ult-, Con-, Per-
- Lambert BL, Chang KY, Lin SJ. Descriptive analysis of the drug name lexicon. *Drug Inf J. *2001;35:163-172.

## Objective Measures Do Predict Probability of Human Error

- Similarity accurately distinguishes between known error pairs and non-error pairs
- Greater objective similarity correlated with higher rates of recognition memory errors by laypeople and pharmacists
- Greater similarity correlated with lower rates of free recall errors
- Objective similarity correlated with subjective similarity (for experts and laypeople)
- Similarity neighborhoods predict visual perception errors

## Figure 1. Effect of spelling similarity on pharmacists recognition memory errors

- Lambert BL, Chang KY, Lin SJ. Effect of orthographic and phonological similarity on false recognition of drug names. *Soc Sci Med. *2001;52:1843-1857.

## Figure 2. Effect of spelling similarity on pharmacists free recall errors.

- Lambert BL, Chang K-Y, Lin S-J. Immediate free recall of drug names: effects of similarity and availability. *Am J Health-Syst Pharm. *2003;60:156-168.

## Figure 3. Effect of phonological similarity on pharmacists’ 

- Figure 3. Effect of phonological similarity on pharmacists’
- free recall errors.
- Lambert BL, Chang K-Y, Lin S-J. Immediate free recall of drug names: effects of similarity and availability. *Am J Health-Syst Pharm. *2003;60:156-168.

## Figure 4. Relationship between objective similarity and lay people’s subjective dissimilarity.

- Lambert BL, Donderi D, Senders J. Similarity of drug names: Objective and subjective measures. *Psychology and Marketing. *2002;19(7-8):641-661.

## Neighborhoods Matter 

- Concept of similarity “neighborhood” is key part of modern theories of visual and auditory perception
- Neighborhood characteristics
- Frequency (of prescribing)
- Density
- Radius (i.e., how close does a name have to be to be in another name’s neighborhood?)

## Neighborhood Illustration

## Dense Neighborhoods: High and Low Frequency

## Slide 16

## Examples

- High log SF names (log SF > 7): Ventolin®, Dyazide®, Provera®
- Low log SF names (log SF < 3): Vistazine®, Antispas®, Protaphane®
- Name from a sparse neighborhood: Flexeril® (no neighbors in NAMCS/NHAMCS)
- Name from a dense neighborhood: Dynabac®, Synalar®, Rynatan®, Dynapen®, Dynacirc®, Dynacin®, Cynobac®

## Figure 5. Effect of similarity neighborhood on RPh visual perception of drug names.

- Lambert BL, Chang K-Y, Gupta P. Effects of frequency and similarity neighborhoods on pharmacists' visual perception of drug names. *Soc Sci Med. *in press.

## Objective Measures: Conclusions

- They work.
- They are not perfect.
- Better on population basis than on individual basis
- Better for public health than for legal wrangling
- We should be using them.

## Software Demonstration (time permitting)

- Name searching
- Spelling
- N-gram, edit distance
- Pronunciation
- N-gram, edit distance
- Product Searching
- Name, dosage form, strength, route
- Each weighted for importance
- Lambert BL, Yu C, Thirumalai M. A system for multi-attribute drug product comparison. *Journal of Medical Systems. *in press.
- Lambert BL, inventor. Apparatus, method, and product for multi-attribute drug comparison. US patent 6,529,892. March 4, 2003.

## How can computer resources be used to calculate weights for various elements in name similarity?

- One way is to calculate a composite similarity score using multiple distinct similarity measures
- Use multiple measures to predict error probability or some other outcome
- Expert = 0.69 - 0.01*Editex - 0.30*NED + 0.22*Trigram2b - 0.02*EditSoundex

## Figure 6. Using multi-measure regression model to predict expert similarity judgments.

- Lambert BL, Yu C, Thirumalai M. A system for multi-attribute drug product comparison. *Journal of Medical Systems. *in press.

## How can computer resources be used to calculate weights for various elements in name similarity?

- Compute distinct similarity score for each product attribute
- Name, Dosage Form, Strength, Route of Administration, Schedule
- Indication, Shape, Color, etc.
- Use equivalence classes for approximate matching of attribute values (e.g., tablet and capsule)
- Use regression or other modeling techniques to assign weights to various attributes
- Use ISMP/MERP error data to estimate the importance of various attributes

## Can computer assisted pattern recognition support the decision process to determine name/name similarities?

- Yes.
- General problem can be framed as a prediction problem
- Given some predictors, predict some outcome
- Predictors include product attributes and attribute similarity scores, as well as prescribing frequency
- Outcome is probability of error or expert judgment of similarity or probability of FDA approval, etc.
- Many techniques exist for tackling these sorts of problems

## Can computer assisted pattern recognition support the decision process to determine name/name similarities?

- Statistical modeling techniques
- Regression
- Discriminant analysis
- Machine Learning
- Decision trees
- Neural networks
- Instance-based methods
- Support vector machines, etc.

## Can computer assisted pattern recognition support the decision process to determine name/name similarities?

- Problems
- False positives
- False negatives
- Reliability of data for modeling (too often based on voluntary reports)
- Determination of a threshold beyond which a name is “too confusing to approve”
- Validation of predictive models

## Summary

- How can computer resources be used to objectively measure differences between name pairs?
- How can computer resources be used to calculate weights for various elements in name similarity?
- Can computer assisted pattern recognition support the decision process to determine name/name similarities?
- Please contact me to discuss further. Thanks.