Statistical classification of drug incidents due to look-alike sound-alike mix-ups.
It has been recognised that medication names that look or sound similar are a cause of medication errors. This study builds statistical classifiers for identifying medication incidents due to look-alike sound-alike mix-ups. A total of 227 patient safety incident advisories related to medication were obtained from the Canadian Patient Safety Institute's Global Patient Safety Alerts system. Eight feature selection strategies based on frequent terms, frequent drug terms and constituent terms were performed. Statistical text classifiers based on logistic regression, support vector machines with linear, polynomial, radial-basis and sigmoid kernels and decision tree were trained and tested. The models developed achieved an average accuracy of above 0.8 across all the model settings. The receiver operating characteristic curves indicated the classifiers performed reasonably well. The results obtained in this study suggest that statistical text classification can be a feasible method for identifying medication incidents due to look-alike sound-alike mix-ups based on a database of advisories from Global Patient Safety Alerts.
City University of Hong Kong, Hong Kong zoiewong@cityu.edu.hk zoiesywong@gmail.com.
Zoie Shui Yee
ZS
Wong
D000465:Algorithms; D016208:Databases, Factual / Q000706:statistics & numerical data*; D003663:Decision Trees; D006801:Humans; D008490:Medical Informatics; D008508:Medication Errors / Q000517:prevention & control* / Q000706:statistics & numerical data*; D015233:Models, Statistical; D017751:Safety Management / Q000458:organization & administration
International Classification for Patient Safety; look-alike sound-alike mix-ups; patient safety; statistical classifiers; text mining