Healthcare call center efficiency improvement using a simulation approach to achieve the organization’s target

References

• Kappa SF, McClain C, Wallace K, et al. Implementation of a centralized, cost-effective call center in a large urology community practice. Rev Urol. 2020;22(2):67.
   PubMedGoogle Scholar
• Griffith KN, Li D, Davies ML, et al. Call center performance affects patient perceptions of access and satisfaction. Am J Manag Care. 2019;25(9):e282.
   PubMed Web of Science ®Google Scholar
• Junewicz A, Youngner SJ. Patient-satisfaction surveys on a scale of 0 to 10: improving health care, or leading it astray? Hastings Cent Rep. 2015;45(3):43–51.
   PubMed Web of Science ®Google Scholar
• Durofchalk M. A comprehensive literature review of a call center’s impact on quality, access, and cost. University of Pittsburgh; 2019.
   Google Scholar
• Kelley BJ. Dramatic benefits from standard work implementation in a call center. J Med Prac Manag Mpm. 2018;33(6):378–382.
   Google Scholar
• Nah JE, Kim S. Workforce planning and deployment for a hospital reservation call center with abandonment cost and multiple tasks. Comput Ind Eng. 2013 Jun 1;65(2):297–309.
   Web of Science ®Google Scholar
• Rohleder T, Bailey B, Crum B, et al. Improving a patient appointment call center at Mayo Clinic. Int J Health Care Qual Assur. 2013 Sep 30.
   PubMedGoogle Scholar
• Rausch TM, Albrecht T, Baier D. Beyond the beaten paths of forecasting call center arrivals: on the use of dynamic harmonic regression with predictor variables. J Bus Econ. 2022;92(4):675–706.
   Google Scholar
• Manno A, Rossi F, Smriglio S, et al. Comparing deep and shallow neural networks in forecasting call center arrivals. Soft comput. 2022: 1–5.
   Web of Science ®Google Scholar
• Rausch TM, Albrecht T. The impact of lead time and model selection on the accuracy of call center arrivals’ forecasts; 2020.
   Google Scholar
• Rausch TM, Albrecht T. The impact of lead time and model selection on the accuracy of call center arrivals’ forecasts. Proceedings of the 28th European conference on information systems. 2020. p. 1–16.
   Google Scholar
• Albrecht T, Rausch TM, Derra ND. Call me maybe: methods and practical implementation of artificial intelligence in call center arrivals’ forecasting. J Bus Res. 2021;123:267–278.
   Web of Science ®Google Scholar
• Ibrahim R, Ye H, L’Ecuyer P, et al. Modeling and forecasting call center arrivals: a literature survey and a case study. Int J Forecast. 2016;32(3):865–874.
   Web of Science ®Google Scholar
• Thangaraj J. Limitation of Erlang B traffic model in elastic optical network for blocking probability estimation. J Opt Commun. 2021;42(2):249–258.
   Google Scholar
• Baronak I, Hartmann M, Polacek R. Properties of a modelled call centre. J Electr Eng. 2020;71(2):96–102.
   Web of Science ®Google Scholar
• Mughele ES, Susan K. Exploring mathematical models to improve routing protocol for call centre. Int J Comput Appl. 2020;975:8887.
   Google Scholar
• Kuncová M, Fábry J, Klímová AM. Discrete event simulation-model of a call center in SIMUL8 software. In ECMS 2020 (pp. 48–55).
   Google Scholar
• Serper N, Elif ŞE, ÇALIŞ B. Discrete event simulation model performed with data analytics for a call center optimization. Istanbul Bus Res. 2022;51(1).
   Web of Science ®Google Scholar
• Ta TA, Chan W, Bastin F, et al. A simulation-based decomposition approach for two-stage staffing optimization in call centers under arrival rate uncertainty. Eur J Oper Res. 2021;293(3):966–979.
   Web of Science ®Google Scholar
• Hou C, Cao B, Fan J. A data-driven method to predict service level for call centers. IET Commun. 2022 Jun;16(10):1241–1252.
   Web of Science ®Google Scholar
• Bateh J, Farah J. Reducing call center wait times through Six Sigma. J Bus Inquiry. 2017;17(2):131–148.
   Google Scholar
• Purba JS, Hidayati J. Performance improvement strategies to increase call center service level: a literature review. IOP conference series: materials science and engineering 2020 May 1 (Vol. 801, No. 1, p. 012147). IOP Publishing.
   Google Scholar
• Huang YL. Treatment allocation for the potential urgent patients in hospital emergency department to reduce length of stay – a simulation approach. Int J Healthcare. 2015;1(1).
   Google Scholar
• Wang J. Patient flow modeling and optimal staffing for emergency departments: A petri net approach. IEEE Trans Comput Soc Syst. 2022.
   Web of Science ®Google Scholar
• Rise Z R, Ershadi MM. An integrated HFMEA simulation-based multi-objective optimisation model to improve the performances of hospitals: a case study. J Simul. 2021: 1–22.
   Web of Science ®Google Scholar
• Watts PI, McDermott DS, Alinier G, et al. Healthcare simulation standards of best practiceTM simulation design. Clin Simul Nursing. 2021;58:14–21.
   Web of Science ®Google Scholar
• Ershadi MM, Shafaeizadeh A. Simulation-based improvement and modification for performances of hospitals: a case study. Int J Simul Process Model. 2021;16(2):90–104.
   Google Scholar
• Improta G, Guizzi G, Ricciardi C, et al. Agile six sigma in healthcare: case study at santobono pediatric hospital. Int J Environ Res Public Health. 2020;17(3):1052.
   PubMed Web of Science ®Google Scholar
• Kumar A, Krishnamurthi R, Nayyar A, et al. A novel smart healthcare design, simulation, and implementation using healthcare 4.0 processes. IEEE Access. 2020;8:118433–118471.
   Web of Science ®Google Scholar
• Pereira MA, Ferreira DC, Figueira JR, et al. Measuring the efficiency of the Portuguese public hospitals: a value modelled network data envelopment analysis with simulation. Expert Syst Appl. 2021;181:115169.
   Web of Science ®Google Scholar
• Lee CK. A review of applications of genetic algorithms in operations management. Eng Appl Artif Intell. 2018;76:1–2.
   Web of Science ®Google Scholar
• Peters J, Stephan D, Amon I, et al. Mixed integer programming versus evolutionary computation for optimizing a hard real-world staff assignment problem. Proceedings of the international conference on automated planning and scheduling. 2019 (Vol. 29, pp. 541–554).
   Google Scholar
• Katoch S, Chauhan SS, Kumar V. A review on genetic algorithm: past, present, and future. Multimed Tools Appl. 2021;80(5):8091–8126.
   PubMed Web of Science ®Google Scholar
• Apornak A, Raissi S, Keramati A, et al. Human resources optimization in hospital emergency using the genetic algorithm approach. Int J Healthc Manag. 2021;14(4):1441–1448.
   Web of Science ®Google Scholar
• Analyst Agency, & ProcedureFlow. The State of Contact Center Training. CallCenter helper; 2021. Retrieved October 17, 2022, from https://www.callcentrehelper.com/images/resources/2021/procedureflow-state-of-the-contact-centre-training-report-whitepaper-210623.pdf
   Google Scholar