Copyright © 2006 by American Society of Health-System Pharmacists
Geometric probability distribution for modeling of error risk during prescription dispensingBRIAN J. CARNAHAN, PH.D. (deceased), was Associate Professor, Department of Industrial and Systems Engineering, Auburn University, Auburn University, AL. SAEED MAGHSOODLOO, PH.D., is Professor, Department of Industrial and Systems Engineering, Auburn University. ELIZABETH A. FLYNN, PH.D., is Associate Research Professor, Center for Pharmacy Operations and Designs, Harrison School of Pharmacy, Auburn University. KENNETH N. BARKER, PH.D., is Sterling Professor and Director, Center for Pharmacy Operations and Designs, Harrison School of Pharmacy, Auburn University. Address correspondence to Dr. Maghsoodloo, Department of Industrial and Systems Engineering, 210 Dunstan Hall, Auburn University, Auburn University, AL 36849 (maghsood{at}eng.auburn.edu).
Summary. A cross-sectional descriptive study involving 50 pharmacies located in six cities across the United States was conducted. A pharmacist trained to detect dispensing errors recorded the number of prescriptions filled by each pharmacy staff member and noted which prescription represented the staff members first dispensing error (defined as any deviation from the prescribers order) made during the observation period. The KolmogorovSmirnov tests for discrete distributions revealed that the observed cumulative distribution of dispensing errors could have come from a geometric probability distribution that assumed dispensing error rates of 23%. In terms of risk analysis, this studys findings suggest that there can be a quantifiable statistical relationship between a measure of workload and the risk of committing at least one dispensing error. The ability to model dispensing errors using a geometric probability distribution enables the safety and health care practitioner to directly assess dispensing error risk as a function of a pharmacys accuracy rate and the number of prescriptions a pharmacy staff member should dispense during a work shift.
Conclusion. A geometric probability distribution effectively modeled the relationship between the number of prescriptions filled and the occurrence of the first dispensing errors.
Index terms: Dispensing; Errors, medication; Methodology; Models; Pharmacists; Pharmacy; Prescriptions; Quality assurance; Workload
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