ArticleIranian Red Crescent medical journal2014
Using queuing theory and simulation model to optimize hospital pharmacy performance.
Article in Iranian Red Crescent medical journal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 65 citations in OpenAlex.
- Safe Staffing Standards for Pharmacy Technicians in Hospital Settings.Journal of market access & health policy · 2025Article
- Article
- Optimisation of medication reconciliation using queueing theory: a computer experiment.International journal of clinical pharmacy · 2024Article
- Analyzing the queuing theory at the emergency department at King Hussein cancer center.BMC emergency medicine · 2023Article
- Improving service efficiency and throughput of cardiac surgery patients using Monte Carlo simulation: a queueing setting.Scientific reports · 2022Article
- Recent Iranian Health System Reform: An Operational Perspective to Improve Health Services Quality.International journal of health policy and management · 2018Article
- Bed Capacity Planning Using Stochastic Simulation Approach in Cardiac-surgery Department of Teaching Hospitals, Tehran, Iran.Iranian journal of public health · 2016Article
- Impact of value added services on patient waiting time at the ambulatory pharmacy Queen Elizabeth Hospital.Pharmacy practiceArticle
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Authors and funding
4 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundHospital pharmacy is responsible for controlling and monitoring the medication use process and ensures the timely access to safe, effective and economical use of drugs and medicines for patients and hospital staff.
objectivesThis study aimed to optimize the management of studied outpatient pharmacy by developing suitable queuing theory and simulation technique. PATIENTS AND
methodsA descriptive-analytical study conducted in a military hospital in Iran, Tehran in 2013. A sample of 220 patients referred to the outpatient pharmacy of the hospital in two shifts, morning and evening, was selected to collect the necessary data to determine the arrival rate, service rate, and other data needed to calculate the patients flow and queuing network performance variables. After the initial analysis of collected data using the software SPSS 18, the pharmacy queuing network performance indicators were calculated for both shifts. Then, based on collected data and to provide appropriate solutions, the queuing system of current situation for both shifts was modeled and simulated using the software ARENA 12 and 4 scenarios were explored.
resultsResults showed that the queue characteristics of the studied pharmacy during the situation analysis were very undesirable in both morning and evening shifts. The average numbers of patients in the pharmacy were 19.21 and 14.66 in the morning and evening, respectively. The average times spent in the system by clients were 39 minutes in the morning and 35 minutes in the evening. The system utilization in the morning and evening were, respectively, 25% and 21%. The simulation results showed that reducing the staff in the morning from 2 to 1 in the receiving prescriptions stage didn't change the queue performance indicators. Increasing one staff in filling prescription drugs could cause a decrease of 10 persons in the average queue length and 18 minutes and 14 seconds in the average waiting time. On the other hand, simulation results showed that in the evening, decreasing the staff from 2 to 1 in the delivery of prescription drugs, changed the queue performance indicators very little. Increasing a staff to fill prescription drugs could cause a decrease of 5 persons in the average queue length and 8 minutes and 44 seconds in the average waiting time.
conclusionsThe patients' waiting times and the number of patients waiting to receive services in both shifts could be reduced by using multitasking persons and reallocating them to the time-consuming stage of filling prescriptions, using queuing theory and simulation techniques.
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