Literature DB >> 22610029

Analyzing methods for improved management of workflow in an outpatient pharmacy setting.

Alex Jenkins1, Stephen F Eckel.   

Abstract

PURPOSE: The results of a workflow analysis at a large central outpatient pharmacy are reported, with theoretical modeling of potential efficiencies attainable through workflow enhancements.
SUMMARY: In keeping with concepts of "lean health care," a time-motion analysis was conducted at a central outpatient pharmacy that dispenses an average of 250 prescriptions per day. Through direct observation over an eight-week period, pharmacists' dispensing-oriented activities were categorized as either value-added (i.e., centered on direct pharmacist-patient contact and, hence, providing increased value to the patient) or non-value-added. The workflow analysis suggested opportunities to derive more value from pharmacists' time by shifting their efforts away from non-value-added activities (i.e., technical dispensing functions) toward value-added activities: engagement of patients on entry into the pharmacy, pharmacist order verification, and patient counseling. The theoretical application of two models of enhanced workflow indicated that model A (expansion of pharmacy technicians' standard duties to include prescription preparation, stock-container retrieval, and prescription processing) could reduce pharmacists' non-value-added time by about 55%, or more than six minutes per prescription, with an even greater (74%) potential reduction offered by model B (technician performance of checkout procedures in addition to expanded standard duties). Although the research site was atypical in its high staffing level relative to prescription volume, the findings suggest that similar workflow enhancements might be applicable in a range of community practice settings.
CONCLUSION: Through analysis of existing workflow in an outpatient pharmacy, opportunities to optimize the use of value-added pharmacist time in the dispensing process were identified.

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Mesh:

Year:  2012        PMID: 22610029     DOI: 10.2146/ajhp110389

Source DB:  PubMed          Journal:  Am J Health Syst Pharm        ISSN: 1079-2082            Impact factor:   2.637


  7 in total

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5.  Detecting dissonance in clinical and research workflow for translational psychiatric registries.

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6.  Using Machine Learning to Predict Early Preparation of Pharmacy Prescriptions at PSMMC - a Comparison of Four Machine Learning Algorithms.

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7.  Coming of age: pharmacy practice in the 21st century.

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  7 in total

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