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Using discrete event simulation to design a more efficient hospital pharmacy for outpatients

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Abstract

We present the findings of a discrete event simulation study of the hospital pharmacy outpatient dispensing systems at two London hospitals. Having created a model and established its face validity, we tested scenarios to estimate the likely impact of changes in prescription workload, staffing levels and skill-mix, and utilisation of the dispensaries’ automatic dispensing robots. The scenarios were compared in terms of mean prescription turnaround times and percentage of prescriptions completed within 45 min. The findings are being used to support business cases for changes in staffing levels and skill-mix in response to changes in workload.

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Acknowledgments

We would like to thank the dispensary staff and management at both hospital sites. We also would like to thank Martin Utley, Clinical Operational Research Unit, University College London for advice on many aspects of the study and Anand Pankhania, The School of Pharmacy, University of London for assistance with data collection.

This project was partly funded by a grant from the Hammersmith Hospitals Trustees' Research Committee. The Clinical Operational Research Unit receives funding from the Department of Health Policy Research Programme. The Centre for Medication Safety and Service Quality is affiliated with the Centre for Patient Safety and Service Quality at Imperial College Healthcare NHS Trust which is funded by the National Institute of Health Research.

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Correspondence to Bryony Dean Franklin.

Appendix

Appendix

Table 5 Input parameters to the baseline models of Charing Cross and Hammersmith Hospitals
Table 6 Mean processing times per task and number of items in the prescription (minutes:seconds)
Table 7 Daily prescription arrival rates
Table 8 Baseline staffing levels for Charing Cross and Hammersmith Hospitals

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Reynolds, M., Vasilakis, C., McLeod, M. et al. Using discrete event simulation to design a more efficient hospital pharmacy for outpatients. Health Care Manag Sci 14, 223–236 (2011). https://doi.org/10.1007/s10729-011-9151-1

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