Araştırma Makalesi
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Yıl 2023, Cilt: 41 Sayı: 2, 423 - 432, 30.04.2023

Abdulkadir Atalan Abdülkadir Keskin

Öz

Kaynakça

  • REFERENCES
  • [1] Atalan A, Donmez C. Employment of emergency advanced Nurses of Turkey: A discrete-event simu-lation application. Processes 2019;7:48. [CrossRef]
  • [2] Zhang X. Application of discrete event simulation in health care: a systematic review. BMC Health Serv Res 2018;18:687. [CrossRef]
  • [3] Kampa A, Gołda G, Paprocka I. Discrete Event Simulation Method as a Tool for Improvement of Manufacturing Systems. Computers 2017;6:10.[CrossRef]
  • [4] Norouzzadeh S, Riebling N, Carter L, Conigliaro J, Doerfler ME. Simulation modeling to optimize healthcare delivery in an outpatient clinic. 2015 Winter Simul. Conf., IEEE; 2015, p. 1355−1366.[CrossRef]
  • [5] Kelton WD. Simulation with Arena. 4th ed. Boston: Mass: WCB/McGraw-Hill; 2004.
  • [6] Günal MM, Pidd M. Discrete event simulation for performance modelling in health care: a review of the literature. J Simul 2010;4:42−51. [CrossRef]
  • [7] Altiok T, Melamed B. Simulation Modeling and Analysis with Arena. 1st ed. Cambridge, Massachutes: Academic Press; 2007. [CrossRef]
  • [8] Atalan A. A cost analysis with the discrete‐event simulation application in nurse and doctor employment management. J Nurs Manag 2022;30:733−741.[CrossRef]
  • [9] Lim ME, Worster A, Goeree R, Tarride JÉ. Simulating an emergency department: The impor-tance of modeling the interactions between physi-cians and delegates in a discrete event simulation. BMC Med Inform Decis Mak 2013;59:13. [CrossRef]
  • [10] Dayarathna VL, Mismesh H, Nagahisarchoghaei M, Alhumoud A. A discrete event simulation (DES) based approach to maximize the patient throughput in out-patient clinic. Eng Sci Technol J 2020;1:1−11. [CrossRef]
  • [11] Atalan A, Dönmez CC. Optimizing experimental simulation design for the emergency departments. Brazilian J Oper Prod Manag 2020;17:1−13. [CrossRef]
  • [12] Shahabi A, Raissi S, Khalili-Damghani K, Rafei M. Designing a resilient skip-stop schedule in rapid rail transit using a simulation-based optimization meth-odology. Oper Res 2019;21:1691-1721. [CrossRef]
  • [13] Baril C, Gascon V, Vadeboncoeur D. Discrete-event simulation and design of experiments to study ambu-latory patient waiting time in an emergency depart-ment. J Oper Res Soc 2019;70:2019−2038. [CrossRef]
  • [14] Nuñez-Perez N, Ortíz-Barrios M, McClean S, Salas-Navarro K, Jimenez-Delgado G, Castillo-Zea A. Discrete-event simulation to reduce waiting time in accident and emergency departments: A case study in a district general clinic. ubiquitous comput. Ambient Intell 2017, p. 352−63. [CrossRef]
  • [15] Babashov V, Aivas I, Begen MA, Cao JQ, Rodrigues G, D'Souza D, et al. Reducing patient waiting times for radiation therapy and ımproving the treat-ment planning process: a discrete-event simulation model (radiation treatment planning). Clin Oncol 2017;29:385−391. [CrossRef]
  • [16] Hasan I, Bahalkeh E, Yih Y. Evaluating intensive care unit admission and discharge policies using a discrete event simulation model. Simulation 2020;96:501−18. [CrossRef]
  • [17] Hamrock E, Paige K, Parks J, Scheulen J, Levin S. Discrete event simulation for healthcare organiza-tions: A tool for decision making. J Healthc Manag 2013;58:110−124. [CrossRef]
  • [18] Zeinali F, Mahootchi M, Sepehri MM. Resource planning in the emergency departments: A simula-tion-based metamodeling approach. Simul Model Pract Theory 2015;53:123−138. [CrossRef]
  • [19] Ahmed MA, Alkhamis TM. Simulation optimiza-tion for an emergency department healthcare unit in Kuwait. Eur J Oper Res 2009;198:936−942. [CrossRef]
  • [20] Connelly LG, Bair AE. Discrete event simulation of emergency department activity: A platform for system-level operations research. Acad Emerg Med 2004;11:1177- 1185. [CrossRef]
  • [21] Mohammed MA, Mohsin SK, Mohammed SJ. The effectiveness of using discrete event simulation to optimize the quality of service of outpatient in Iraq: A case study. Iraqi J Ind Res 2021;8:40−49. [CrossRef]
  • [22] Chang W-J, Chang Y-H. Design of a patient-cen-tered appointment scheduling with artificial neural network and discrete event simulation. J Serv Sci Manag 2018;11:71−82. [CrossRef]
  • [23] Czech M, Witkowski M, Williams EJ. Simulation Improves Patient Flow And Productivity At A Dental Clinic. ECMS 2007 Proc. Ed. by I. Zelinka, Z. Oplatkova, A. Orsoni, ECMS; 2007, p. 25−29.
  • [24] Kiley DP, Haley S, Saylor B, Saylor BL. The value of evidence-based computer simulation of oral health outcomes for management analysis of the Alaska Dental Health Aide Program. Institute of Social and Economic Research, University of Alaska Anchorage; 2008.
  • [25] Green L. Queueing Analysis in Healthcare. Patient Flow Reducing Delay Healthc. Deliv., 2006, p. 281−307. [CrossRef]
  • [26] Dönmez NFK, Atalan A, Dönmez CÇ. Desirability Optimization models to create the global Healthcare Competitiveness Index. Arab J Sci Eng 2020;45:7065−7076. [CrossRef]

Estimation of the utilization rates of the resources of a dental clinic by simulation

Yıl 2023, Cilt: 41 Sayı: 2, 423 - 432, 30.04.2023

Abdulkadir Atalan Abdülkadir Keskin

Öz

This study aimed to calculate the resource utilization rates, patient length of stay, and pa-tient waiting times of a small-scale dental clinic using the discrete-event simulation (DES) technique. Dentists, orthodontists, assistants, clerks, beds, triage areas were considered dental clinical resources in this study. In contrast to the traditional doctor-patient contact calculation of healthcare resource efficiencies, clinical resource efficiencies were calculated using the DES technique. Since the patient’s arrival both with and without an appointment, the patient arrival times were calculated with a uniform distribution. According to actual data, 100 patients were treated per day on average, while the number of patients treated by the dentists and ortho-dontists was simulated as 105 in the simulation model. The utilization rate of the dentists, orthodontists, assistants, and clerks were computed as 65.5%, 77.5%, 35.5%, and 45%, respec-tively. The beds reserved for dentists, the beds for orthodontists, the triage locations used by assistants, and the registry office where clerks work have a utilization rate of 68.25%, 89.5%, 31.0%, and 99.0%, respectively. The time a patient must spend in the clinic for treatment was calculated a maximum of 155.51 minutes and a minimum of 41.68 minutes. A patient waits an average of 15.45 minutes to complete the dental treatment in this clinic. Patients wait 15.81 minutes for an available staff member and 15.1 minutes for an available location to receive dental treatment. We presented this study as the best example for the dental clinics to provide results that should be obtained in a short time and at a low cost.

Anahtar Kelimeler

Dental Clinic Discrete Event Simulation Utilization Rates Length of Stay Waiting Time

Kaynakça

  • REFERENCES
  • [1] Atalan A, Donmez C. Employment of emergency advanced Nurses of Turkey: A discrete-event simu-lation application. Processes 2019;7:48. [CrossRef]
  • [2] Zhang X. Application of discrete event simulation in health care: a systematic review. BMC Health Serv Res 2018;18:687. [CrossRef]
  • [3] Kampa A, Gołda G, Paprocka I. Discrete Event Simulation Method as a Tool for Improvement of Manufacturing Systems. Computers 2017;6:10.[CrossRef]
  • [4] Norouzzadeh S, Riebling N, Carter L, Conigliaro J, Doerfler ME. Simulation modeling to optimize healthcare delivery in an outpatient clinic. 2015 Winter Simul. Conf., IEEE; 2015, p. 1355−1366.[CrossRef]
  • [5] Kelton WD. Simulation with Arena. 4th ed. Boston: Mass: WCB/McGraw-Hill; 2004.
  • [6] Günal MM, Pidd M. Discrete event simulation for performance modelling in health care: a review of the literature. J Simul 2010;4:42−51. [CrossRef]
  • [7] Altiok T, Melamed B. Simulation Modeling and Analysis with Arena. 1st ed. Cambridge, Massachutes: Academic Press; 2007. [CrossRef]
  • [8] Atalan A. A cost analysis with the discrete‐event simulation application in nurse and doctor employment management. J Nurs Manag 2022;30:733−741.[CrossRef]
  • [9] Lim ME, Worster A, Goeree R, Tarride JÉ. Simulating an emergency department: The impor-tance of modeling the interactions between physi-cians and delegates in a discrete event simulation. BMC Med Inform Decis Mak 2013;59:13. [CrossRef]
  • [10] Dayarathna VL, Mismesh H, Nagahisarchoghaei M, Alhumoud A. A discrete event simulation (DES) based approach to maximize the patient throughput in out-patient clinic. Eng Sci Technol J 2020;1:1−11. [CrossRef]
  • [11] Atalan A, Dönmez CC. Optimizing experimental simulation design for the emergency departments. Brazilian J Oper Prod Manag 2020;17:1−13. [CrossRef]
  • [12] Shahabi A, Raissi S, Khalili-Damghani K, Rafei M. Designing a resilient skip-stop schedule in rapid rail transit using a simulation-based optimization meth-odology. Oper Res 2019;21:1691-1721. [CrossRef]
  • [13] Baril C, Gascon V, Vadeboncoeur D. Discrete-event simulation and design of experiments to study ambu-latory patient waiting time in an emergency depart-ment. J Oper Res Soc 2019;70:2019−2038. [CrossRef]
  • [14] Nuñez-Perez N, Ortíz-Barrios M, McClean S, Salas-Navarro K, Jimenez-Delgado G, Castillo-Zea A. Discrete-event simulation to reduce waiting time in accident and emergency departments: A case study in a district general clinic. ubiquitous comput. Ambient Intell 2017, p. 352−63. [CrossRef]
  • [15] Babashov V, Aivas I, Begen MA, Cao JQ, Rodrigues G, D'Souza D, et al. Reducing patient waiting times for radiation therapy and ımproving the treat-ment planning process: a discrete-event simulation model (radiation treatment planning). Clin Oncol 2017;29:385−391. [CrossRef]
  • [16] Hasan I, Bahalkeh E, Yih Y. Evaluating intensive care unit admission and discharge policies using a discrete event simulation model. Simulation 2020;96:501−18. [CrossRef]
  • [17] Hamrock E, Paige K, Parks J, Scheulen J, Levin S. Discrete event simulation for healthcare organiza-tions: A tool for decision making. J Healthc Manag 2013;58:110−124. [CrossRef]
  • [18] Zeinali F, Mahootchi M, Sepehri MM. Resource planning in the emergency departments: A simula-tion-based metamodeling approach. Simul Model Pract Theory 2015;53:123−138. [CrossRef]
  • [19] Ahmed MA, Alkhamis TM. Simulation optimiza-tion for an emergency department healthcare unit in Kuwait. Eur J Oper Res 2009;198:936−942. [CrossRef]
  • [20] Connelly LG, Bair AE. Discrete event simulation of emergency department activity: A platform for system-level operations research. Acad Emerg Med 2004;11:1177- 1185. [CrossRef]
  • [21] Mohammed MA, Mohsin SK, Mohammed SJ. The effectiveness of using discrete event simulation to optimize the quality of service of outpatient in Iraq: A case study. Iraqi J Ind Res 2021;8:40−49. [CrossRef]
  • [22] Chang W-J, Chang Y-H. Design of a patient-cen-tered appointment scheduling with artificial neural network and discrete event simulation. J Serv Sci Manag 2018;11:71−82. [CrossRef]
  • [23] Czech M, Witkowski M, Williams EJ. Simulation Improves Patient Flow And Productivity At A Dental Clinic. ECMS 2007 Proc. Ed. by I. Zelinka, Z. Oplatkova, A. Orsoni, ECMS; 2007, p. 25−29.
  • [24] Kiley DP, Haley S, Saylor B, Saylor BL. The value of evidence-based computer simulation of oral health outcomes for management analysis of the Alaska Dental Health Aide Program. Institute of Social and Economic Research, University of Alaska Anchorage; 2008.
  • [25] Green L. Queueing Analysis in Healthcare. Patient Flow Reducing Delay Healthc. Deliv., 2006, p. 281−307. [CrossRef]
  • [26] Dönmez NFK, Atalan A, Dönmez CÇ. Desirability Optimization models to create the global Healthcare Competitiveness Index. Arab J Sci Eng 2020;45:7065−7076. [CrossRef]
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Klinik Kimya
Bölüm Research Articles
Yazarlar

Abdulkadir Atalan 0000-0003-0924-3685

Abdülkadir Keskin 0000-0002-4795-1028

Yayımlanma Tarihi 30 Nisan 2023
Gönderilme Tarihi 7 Eylül 2021
Yayımlandığı Sayı Yıl 2023 Cilt: 41 Sayı: 2

Kaynak Göster

Vancouver Atalan A, Keskin A. Estimation of the utilization rates of the resources of a dental clinic by simulation. SIGMA. 2023;41(2):423-32.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/