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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
REFERENCES
U.S. Pharmacopeia. USP Quality Review. U. S. Pharmacopeia, March. Available at http://www.usp.org/reporting/review/qr76.pdf. Accessed July 19, 2001.
Avantiq. Avantiq Home Page. Available at http://www.avantiq.com/visitors/main.htm. Accessed March 3, 2003.
Kim, H.-J., and Phillips, J., Medication errors associated with Serzone and Seroquel. Drug Topics 7:38–40, 2002.
Cohen, M. R., Drug product characteristics that foster drug-use-system errors. Am. J. Health-Syst. Pharm. 52:395–399, 1995.
Lambert, B. L., (inventor), Apparatus, method, and product for multiattribute drug comparison. US patent 6, 529, 892, March 4, 2003.
Lambert, B. L., Chang, K. Y., and Lin, S. J., Descriptive analysis of the drug name lexicon. Drug Inf. J. 35:163–172, 2001.
Multum Information Services, Inc. The Multum Lexicon. Multum Information Services Inc. Available at http://www.multum.com/Lexicon.htm. Accessed January 28, 2003.
U.S. Food and Drug Administration. Orange Book data files. U. S. Food and Drug Administration [Web page], January 29, 2003. Available at: http://www.fda.gov/cder/orange/obreadme.htm. Accessed February 28, 2003.
Lambert, B. L., Predicting look-and sound-alike medication errors. Am. J. Health-Syst. Pharm. 54:1161–1171, 1997.
Lambert, B. L., Lin, S.-J., Gandhi, S. K., and Chang, K.-Y., Similarity as a risk factor in drug name confusion errors: The look-alike (orthographic) and sound-alike (phonological) model. Med. Care. 37(12):1214–1225, 1999.
Lambert, B. L., Chang, K. Y., and Lin, S. J., Effect of orthographic and phonological similarity on false recognition of drug names. Soc. Sci. Med. 52:1843–1857, 2001.
Lambert, B. L., Donderi, D., and Senders, J., Similarity of drug names: Objective and subjective measures. Psychol. Marketing 19(7–8):641–661, 2002.
Lambert, B. L., Chang, K.-Y., and Lin, S.-J., Immediate free recall of drug names: Effects of similarity and availability. Am. J. Health-Syst. Pharm. 60:156–168, 2003.
Stephen, G. A., String Searching Algorithms, World Scientific, River Edge, NJ, 1994.
Ukkonen, E., Approximate string-matching with q-grams and maximal matches. Theor. Comp. Sci. 92:191–211, 1992.
Zobel, J., and Dart, P. Phonetic string matching: Lessons from information retrieval. Paper presented at 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, August 18–22, 1996, Zurich, Switzerland.
Zobel, J., and Dart, P., Finding approximate matches in large lexicons. Software-Pract. Experience 25(3):331–345, 1995.
Pfeifer, U., Poersch, T., and Fuhr, N., Searching for proper names in databases. Paper presented at Hypertext-Information Retrieval-Multimedia: Synergistic Effects of Electronic Information Systems, 5–7 April 1995, Konstanz, Germany.
Gernsbacher, M. A., ed. Handbook of Psycholinguistics, Academic Press, San Diego, CA, 1994.
Fisher, W. M., A statistical text-to-phone function using ngrams and rules. Paper presented at IEEE International Conference on Acoustics, Speech and Signal Processing, March 15–19, 1999, Phoenix, AZ.
Fisher, W. M., Fiscus, J. G., Martin, A., Pallett, D. S., and Pryzbocki, M. A., Further studies in phonological scoring. Proc. Spoken Language Syst. Technol. Workshop 1995:181–186.
Jurafsky, D., and Martin, J. H., Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition Prentice-Hall, Upper Saddle River, NJ, 2000.
U. S. Food and Drug Administration. Electronic Orange Book data files. U. S. Food and Drug Administration [Web page]. Available at http://www.fda.gov/cder/orange/obreadme.htm. Accessed January 28, 2003.
Lambert, B. L., Chang, K.-Y., and Gupta, P., Effects of frequency and similarity neighborhoods on pharmacists' visual perception of drug names. Soc. Sci. Med. in press.
Luce, P. A., and Pisoni, D. B., Recognizing spoken words: The neighborhood activation model. Ear Hearing 19(1):1–36, 1998.
Luce, P. A., Pisoni, D. B., and Goldinger, S. D., Similarity neighborhoods of spoken words. In Altmann, G. T. M. (ed.), Cognitive Models of Speech Processing: Psycholinguistic and Computational Perspectives, MIT Press, Cambridge, MA, 1990, pp. 122–147.
Grainger, J., and Jacobs, A. M., Orthographic processing in visual word recognition: A multiple read-out model. Psychol. Rev. 103(3):518–565, 1996.
Salton, G., and McGill, M., Introduction to Modern Information Retrieval. McGraw-Hill, New York, 1983.
U.S. Pharmacopeia. USP DI. Vol. I: Drug Information for the Health Care Professional. U.S. Pharmacopeia, Rockville, MD, 1998.
U.S. Pharmacopeia. USP Dictionary of USAN and International Drug Names, U.S. Pharmacopeia, Rockville, MD, 1998.
U.S. Patent and Trademark Office. Trademarks Registered. U.S. Patent and Trademark Office—Office of Electronic Information Products [CD-ROM], February 1999. Available at http://www.uspto.gov/web/offices/ac/ido/oeip/catalog/tmcassis.htm#TMregistered. Accessed June 15, 1999.
Voorhees, E. M., and Harman, D., Overview of the sixth text retrieval conference (TREC-6). Paper presented at The sixth text retrieval conference (TREC-6), November 19–21, 1997, 1998, Gaithersburg, MD.
Yu, C., and Meng, W., Principles of Query Processing for Advanced Database Applications, Morgan Kaufmann, San Francisco, CA, 1998.
Cohen, J., and Cohen, P., Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, Erlbaum, Hillsdale, NJ, 1983.
Phillips, J., The name game: New realities at FDA. Pharm. Exec. July; 2000:66–69.
Boring, D., The development and adoption of nonproprietary, established, and proprietary names for pharmaceuticals. Drug Inf. J. 31:621–634, 1997.
Boring, D. L., The CDER labeling and nomenclature committee: Structure, function, and process. Drug Inf. J. 31:7–11, 1997.
Boring, D. L., Stein, I. A., Di Domizio, G., United States: Trademark trainwrecks at FDA. Trademark World 1998:29–33.
Cohen, M. (ed.), Medication Errors, American Pharmaceutical Association, Washington, DC, 1999.
Kohn, L. T., Corrigan, J., Donaldson, M. S. (eds.), To Err is Human: Building a Safer Health System, Institute of Medicine, Washington, DC, 2000.
Institute for Safe Medication Practices. ISMP Medication Safety Alert! Vol. 7(7), Institute for Safe Medication Practices, Huntingdon Valley, PA, 2003.
U.S. Food and Drug Administration. Automated method of minimizing medication errors due to similar proprietary and established names (RFQ #223-02-5618), Center for Drug Evaluation and Research, U. S. Food and Drug Administration, 2002.
Pedhazur, E. J., Multiple Regression in Behavioral Research: Explanation and Prediction, Harcourt Brace, New York, 1997.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Lambert, B.L., Yu, C. & Thirumalai, M. A System for Multiattribute Drug Product Comparison. Journal of Medical Systems 28, 31–56 (2004). https://doi.org/10.1023/B:JOMS.0000021519.75230.e5
Issue Date:
DOI: https://doi.org/10.1023/B:JOMS.0000021519.75230.e5