Optimising Outpatient Pharmacy Staffing to Minimise Patients Queue Time using Discrete Event Simulation
Downloads
Background: To remain relevant in the customer-oriented market, hospitals must pay attention to the quality of services and meet customers' expectations from admission to discharge stage. For an outpatient customer, pharmacy is the last unit visited before discharge. It is likely to influence patient satisfaction and reflect the quality of hospital's service. However, at certain hospitals, the waiting time is long. Resources need to be deployed strategically to reduce queue time.
Objective: This research aims to arrange the number of staff (pharmacists and workers) in each station in the pharmacy outpatient service to minimise the queue time.
Methods: A discrete simulation method is used to observe the waiting time spent at the pharmacy. The simulation run is valid and effective to test the scenario.
Results: It is recommended to add more personnel for the non-compounding medicine and packaging to reduce the waiting time by 22.41%
Conclusion: By adding personnel to non-compounding and packaging stations, the system performance could be improved. Cost-effectiveness analysis should be done to corroborate the finding.
Keywords:Discrete Event Simulation, Hospital, Outpatient Service, Pharmacy Unit, System Analysis
Background: To remain relevant in the customer-oriented market, hospitals must pay attention to the quality of services and meet customers' expectations from admission to discharge stage. For an outpatient customer, pharmacy is the last unit visited before discharge. It is likely to influence patient satisfaction and reflect the quality of hospital's service. However, at certain hospitals, the waiting time is long. Resources need to be deployed strategically to reduce queue time.
Objective: This research aims to arrange the number of staff (pharmacists and workers) in each station in the pharmacy outpatient service to minimise the queue time.
Methods: A discrete simulation method is used to observe the waiting time spent at the pharmacy. The simulation run is valid and effective to test the scenario.
Results: It is recommended to add more personnel for the non-compounding medicine and packaging to reduce the waiting time by 22.41%
Conclusion: By adding personnel to non-compounding and packaging stations, the system performance could be improved. Cost-effectiveness analysis should be done to corroborate the finding.
Keywords:Discrete Event Simulation, Hospital, Outpatient Service, Pharmacy Unit, System Analysis
World Health Organization, Delivering quality health services: a global imperative for universal health coverage. 2018.
W. R. Doucette et al., "Organizational factors influencing pharmacy practice change,” Res. Soc. Adm. Pharm., vol. 8, no. 4, pp. 274–284, 2012.
A. L. McDowell and Y.-L. Huang, "Selecting a pharmacy layout design using a weighted scoring system,” Am. J. Heal. Pharm., vol. 69, no. 9, pp. 796–804, 2012.
C. G. Rodríguez-González et al., "Use of the EFQM excellence model to improve hospital pharmacy performance,” Res. Soc. Adm. Pharm., vol. 16, no. 5, pp. 710–716, 2020.
M. TrÅ¡an, M. Vehovc, K. Seme, and S. SrÄiÄ, "Evaluation of ATP bioluminescence for monitoring surface hygiene in a hospital pharmacy cleanroom,” J. Microbiol. Methods, vol. 168, p. 105785, 2020.
B. e Oliveira, J. de Vasconcelos, J. Almeida, and L. Pinto, "A Simulation-Optimisation approach for hospital beds allocation,” Int. J. Med. Inform., vol. 141, p. 104174, 2020.
C. Cubukcuoglu, P. Nourian, I. S. Sariyildiz, and M. F. Tasgetiren, "A discrete event simulation procedure for validating programs of requirements: The case of hospital space planning,” SoftwareX, vol. 12, p. 100539, 2020.
A. M. Best, C. A. Dixon, W. D. Kelton, C. J. Lindsell, and M. J. Ward, "Using discrete event computer simulation to improve patient flow in a Ghanaian acute care hospital,” Am. J. Emerg. Med., vol. 32, no. 8, pp. 917–922, 2014.
L. Keshtkar, W. Rashwan, W. Abo-Hamad, and A. Arisha, "A hybrid system dynamics, discrete event simulation and data envelopment analysis to investigate boarding patients in acute hospitals,” Oper. Res. Heal. Care, vol. 26, p. 100266, 2020.
A. M. Mosadeghrad, "Healthcare service quality: towards a broad definition,” Int. J. Health Care Qual. Assur., 2013.
A. Alodan, G. Alalshaikh, H. Alqasabi, S. Alomran, A. Abdelhadi, and B. Alkhayyal, "Studying the Efficiency of Waiting Time in Outpatient Pharmacy,” MethodsX, vol. 7, p. 100913, 2020.
K. F. See, N. M. Hamzah, and M.-M. Yu, "Metafrontier Efficiency Analysis for Hospital Pharmacy Services Using Dynamic Network DEA Framework,” Socioecon. Plann. Sci., p. 101044, 2021.
R. F. Al-Ahmadi, L. Al-Juffali, S. Al-Shanawani, and S. Ali, "Categorizing and understanding medication errors in hospital pharmacy in relation to human factors,” Saudi Pharm. J., vol. 28, no. 12, pp. 1674–1685, 2020.
L. G. Daina et al., "Improving performance of a pharmacy in a Romanian hospital through implementation of an internal management control system,” Sci. Total Environ., vol. 675, pp. 51–61, 2019.
E. Cicinelli et al., "An analysis of Canadian doctor of pharmacy hospital preceptor experiences in alternative preceptor models,” Curr. Pharm. Teach. Learn., 2020.
M. H. Rim, K. C. Thomas, J. Chandramouli, S. A. Barrus, and N. A. Nickman, "Implementation and quality assessment of a pharmacy services call center for outpatient pharmacies and specialty pharmacy services in an academic health system,” Bull. Am. Soc. Hosp. Pharm., vol. 75, no. 10, pp. 633–641, 2018.
J. Bellegarde, L. Bernard, P. Chennell, and V. Sautou, "On-call duties in hospital pharmacies: National survey and elaboration of a training program for pharmacy,” Ann. Pharm. Fr., vol. 79, no. 2, pp. 142–151, 2021.
L. Schepel et al., "Strategies for improving medication safety in hospitals: evolution of clinical pharmacy services,” Res. Soc. Adm. Pharm., vol. 15, no. 7, pp. 873–882, 2019.
A. W. Olson, R. Vaidyanathan, T. P. Stratton, B. J. Isetts, L. A. Hillman, and J. C. Schommer, "Patient-Centered Care preferences & expectations in outpatient pharmacist practice: A three archetype heuristic,” Res. Soc. Adm. Pharm., 2021.
B. Ratsimbazafimahefa, H.R., Sadeghipour, F., Trouiller, P., Pannatier, A., Allenet, "Description and analysis of hospital pharmacies in Madagascar,” Ann. Pharm. Françaises, vol. 76, no. 3, pp. 218–227, 2018, doi: https://doi.org/10.1016/j.pharma.2017.12.003.
M. Mohammadi and M. Shamohammadi, "Queuing analytic theory using witness simulation in hospital pharmacy,” Inter J Eng Tech, vol. 12, no. 6, p. 48, 2012.
T. E. Day, W. M. Li, A. Ingolfsson, and N. Ravi, "The use of queueing
and simulative analyses to improve an overwhelmed pharmacy call center,” J. Pharm. Pract., vol. 23, no. 5, pp. 492–495, 2010.
C. W. Spry and M. A. Lawley, "Evaluating hospital pharmacy staffing and work scheduling using simulation,” in Proceedings of the Winter Simulation Conference, 2005., 2005, p. 8 pp.
A. Abdelhadi and M. Shakoor, "Studying the efficiency of inpatient and outpatient pharmacies using lean manufacturing,” Leadersh. Heal. Serv., 2014.
N. Zhang et al., "Optimization of the Workflow of Outpatient Pharmacy in Our Hospital,” China Pharm., 2011.
M. Arafeh, M. A. Barghash, E. Sallam, and A. AlSamhouri, "Six Sigma applied to reduce patients' waiting time in a cancer pharmacy,” Int. J. Six Sigma Compet. Advant., vol. 8, no. 2, pp. 105–124, 2014.
B. Ahmad, K. Khairatul, and A. Farnaza, "An assessment of patient waiting and consultation time in a primary healthcare clinic,” Malaysian Fam. physician Off. J. Acad. Fam. Physicians Malaysia, 2017.
S. Suss, N. Bhuiyan, K. Demirli, and G. Batist, "Toward implementing patient flow in a cancer treatment center to reduce patient waiting time and improve efficiency,” J. Oncol. Pract., vol. 13, no. 6, pp. e530–e537, 2017.
M. Bahadori, S. M. Mohammadnejhad, R. Ravangard, and E. Teymourzadeh, "Using queuing theory and simulation model to optimize hospital pharmacy performance,” Iran. red crescent Med. J., vol. 16, no. 3, 2014.
W. D. Kelton, Simulation with ARENA. McGraw-hill, 2002.
H. A. Taha, Operations research an introduction. © Pearson Education Limited 2017, 2017.
F. S. Hillier and G. J. Liebermann, Operations research. Oldenbourg Wissenschaftsverlag, 2014.
J. F. Shortle, J. M. Thompson, D. Gross, and C. M. Harris, Fundamentals of queueing theory. John Wiley & Sons, 2018.
P. Sharma, "Discrete-event simulation,” Int. J. Sci. Technol. Res., vol. 4, no. 4, pp. 136–140, 2015.
B. Jahn, E. Theurl, U. Siebert, and K. P. Pfeiffer, "Tutorial in medical decision modeling incorporating waiting lines and queues using discrete event simulation,” Value Heal., vol. 13, no. 4, pp. 501–506, 2010.
M. R. Itami, D. Zell, F. Grigel, and R. Gimblett, "Generating confidence intervals for spatial simulations-determining the number of replications for spatial terminating simulations,” in International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, MODSIM 2005, 2005, pp. 141–148.
W. D. Law, Averill M Kelton, "Confidence Intervals for Steady-State Simulations: I. A Survey of Fixed Sample Size Procedures,” Oper. Res., vol. 32, no. 6, pp. 1221–1239, 1984.
M. Bhattacharyya, "To pool or not to pool: A comparison between two commonly used test statistics,” Int. J. Pure Appl. Math., vol. 89, no. 4, pp. 497–510, 2013.
B. L. Welch, "The generalization of ‘STUDENT'S'problem when several different population varlances are involved,” Biometrika, vol. 34, no. 1–2, pp. 28–35, 1947.
G. D. Ruxton, "The unequal variance t-test is an underused alternative to Student's t-test and the Mann–Whitney U test,” Behav. Ecol., vol. 17, no. 4, pp. 688–690, 2006.
Statistics How To, "What is Welch's Test for Unequal Variances?,” 2015. .
Z. Dan, H. Xiaoli, D. Weiru, W. Li, and H. Yue, "Outpatient pharmacy optimization using system simulation,” Procedia Comput. Sci., vol. 91, pp. 27–36, 2016.
D. G. Shimshak, D. G. Damico, and H. D. Burden, "A priority queuing model of a hospital pharmacy unit,” Eur. J. Oper. Res., vol. 7, no. 4, pp. 350–354, 1981.
Authors who publish with this journal agree to the following terms:
All accepted papers will be published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain copyright and grant the journal right of first publication. CC-BY Licenced means lets others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material for any purpose, even commercially).