A system for multiattribute drug product comparison

Bruce L. Lambert; Clement Yu; Mohanraj Thirumalai

doi:10.1023/b:joms.0000021519.75230.e5

A system for multiattribute drug product comparison

Bruce L. Lambert^*, Clement Yu, Mohanraj Thirumalai

^*Corresponding author for this work

Communication Studies

Research output: Contribution to journal › Article › peer-review

17 Scopus citations

Abstract

We describe a system for multiattribute drug product searching. We then demonstrate the system's performance on sample queries, and evaluate the name-based similarity searching component. Ten drug names were used to query a database of existing drug names using five different retrieval methods. Retrieved names were merged into master lists and presented to 15 pharmacists. Pharmacists rated the similarity between the query name and each retrieved names on a scale of 1-5. We report the precision of our five different retrieval methods at 11 levels of recall. The best single measure was editex, with a precision of 17.4% averaged across 11 levels of recall. A regression model using four objective measures of similarity as predictors accounted for 40.6% of the variance in observed mean similarity ratings. Automated, multiattribute drug product searching may improve the effectiveness and efficiency of preapproval screening processes and thereby prevent medication errors.

Original language	English (US)
Pages (from-to)	31-56
Number of pages	26
Journal	Journal of Medical Systems
Volume	28
Issue number	2
DOIs	https://doi.org/10.1023/b:joms.0000021519.75230.e5
State	Published - Apr 1 2004

Funding

The evaluation of this system was funded in part by a grant to Dr Lambert from the National Patient Safety Foundation. We thank several groups of computer science students who helped to implement earlier versions of the system. We also acknowledge the assistance of Dan Boring, Ken-Yu Chang, Michael Cohen, and the Institute for Safe Medication Practices, Linda Kawano, David Lambert, Eric Lambert, Bob Lee, Swu-Jane Lin, Alan Sherr, Jill Tarzian-Sorensen, Michael Verga, and Justin Zobel. Send reprint requests to Dr Lambert.

Keywords

Confusion
Dosage form
Dosing interval
Drug names
Information retrieval
Medication error
Precision
Recall
Route of administration
Similarity
Strength

ASJC Scopus subject areas

Medicine (miscellaneous)
Information Systems
Health Informatics
Health Information Management

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10.1023/b:joms.0000021519.75230.e5

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abstract = "We describe a system for multiattribute drug product searching. We then demonstrate the system's performance on sample queries, and evaluate the name-based similarity searching component. Ten drug names were used to query a database of existing drug names using five different retrieval methods. Retrieved names were merged into master lists and presented to 15 pharmacists. Pharmacists rated the similarity between the query name and each retrieved names on a scale of 1-5. We report the precision of our five different retrieval methods at 11 levels of recall. The best single measure was editex, with a precision of 17.4% averaged across 11 levels of recall. A regression model using four objective measures of similarity as predictors accounted for 40.6% of the variance in observed mean similarity ratings. Automated, multiattribute drug product searching may improve the effectiveness and efficiency of preapproval screening processes and thereby prevent medication errors.",

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note = "Funding Information: The evaluation of this system was funded in part by a grant to Dr Lambert from the National Patient Safety Foundation. We thank several groups of computer science students who helped to implement earlier versions of the system. We also acknowledge the assistance of Dan Boring, Ken-Yu Chang, Michael Cohen, and the Institute for Safe Medication Practices, Linda Kawano, David Lambert, Eric Lambert, Bob Lee, Swu-Jane Lin, Alan Sherr, Jill Tarzian-Sorensen, Michael Verga, and Justin Zobel. Send reprint requests to Dr Lambert.",

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PY - 2004/4/1

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N2 - We describe a system for multiattribute drug product searching. We then demonstrate the system's performance on sample queries, and evaluate the name-based similarity searching component. Ten drug names were used to query a database of existing drug names using five different retrieval methods. Retrieved names were merged into master lists and presented to 15 pharmacists. Pharmacists rated the similarity between the query name and each retrieved names on a scale of 1-5. We report the precision of our five different retrieval methods at 11 levels of recall. The best single measure was editex, with a precision of 17.4% averaged across 11 levels of recall. A regression model using four objective measures of similarity as predictors accounted for 40.6% of the variance in observed mean similarity ratings. Automated, multiattribute drug product searching may improve the effectiveness and efficiency of preapproval screening processes and thereby prevent medication errors.

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KW - Route of administration

KW - Similarity

KW - Strength

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A system for multiattribute drug product comparison

Abstract

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Keywords

ASJC Scopus subject areas

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