Strategi Penanganan Pengecualian Pasif, Reaktif, Antisipatif: Simulasi Diskrit Ketahanan Operasional Layanan EEG Rawat Jalan
-
Tikno Tikno
*
Universitas Internasional Semen Indonesia, Jawa Timur,
Indonesia
- Rais Rafdiadzan Universitas Internasional Semen Indonesia, Jawa Timur, Indonesia (*) Corresponding Author
Abstract
Outpatient electroencephalography (EEG) services face service-time variability, resource constraints, and operational disruptions that increase patient waiting time and Length of Stay (LoS). This study develops a High-Fidelity Discrete-Event Simulation (HF-DES) model that explicitly represents disruptions as operational exceptions in order to compare three handling strategies, namely Baseline (S1, passive), Active Handling (S2, reactive), and Anticipatory Resilience (S3, anticipatory), in terms of both efficiency and operational resilience. The model integrates service flows, probability distributions derived from Quantile Matching Optimization, resource capacities, operational calendars, and disruption mechanisms, and was verified and validated using the Mean Absolute Percentage Error (MAPE) and statistical tests. Experiments used 30 replications per scenario with Common Random Numbers and Holm–Bonferroni correction, together with stress testing through increased demand, machine disruptions, and service durations. The model is highly accurate (MAPE 0.59%; p > 0.05). Scenario S3 performs best, with a LoS of 63.59 minutes (75.73% lower than S1), a waiting time of 6.17 minutes, and the lowest Robustness Index of 12.7% (S1: 571.7%; S2: 16.8%). The superiority of S3 is theoretically grounded: because administrative completeness and resource readiness are verified before patients enter the critical pathway, exceptions are prevented from propagating into the queue, so process variability and cascading delays are reduced at their source rather than recovered after they occur; in queueing theory this reduces waiting time most strongly when utilization is high. Managerially, hospitals can improve both the efficiency and the resilience of EEG services through procedural redesign and hospital information system integration, without adding physicians, technicians, or machines.
Downloads
References
Abdullah, H. R., Lam, S. S. W., Ang, B. Y., Pourghaderi, A., Nguyen, F. N. H. L., Matchar, D. B., Tan, H. K., & Ong, M. E. H. (2022). Resuming elective surgery after COVID-19: A simulation modelling framework for guiding the phased opening of operating rooms. International Journal of Medical Informatics, 158, 104665. https://doi.org/10.1016/j.ijmedinf.2021.104665
Åhlin, P., Almström, P., & Wänström, C. (2023). Solutions for improved hospital-wide patient flows – a qualitative interview study of leading healthcare providers. BMC Health Services Research, 23(1), 17. https://doi.org/10.1186/s12913-022-09015-w
Ahmed, M. S., Zhinuk, F. A., Acharjee, S., Begum, S., Jobiullah, M. I., & Islam, S. (2025). AI-Driven Predictive Operations Management: A Business Science Framework For Dynamic Hospital Resource Optimization And Clinical Workflow Efficiency. International Journal of Professional Business Review, 10(8), e05628. https://doi.org/10.26668/businessreview/2025.v10i8.5628
Aljohani, H. M., Zaghdoun, F. M., Meraou, M. A., Alharthi, A. S., Almohri, W. A. J., Kalantan, Z. I., EL-Helbawy, A. A., Hussam, E., & Muse, A. H. (2025). A novel extension of the exponential distribution with application in modeling complex lifetime and environmental data. Scientific Reports, 15(1), 33581. https://doi.org/10.1038/s41598-025-18711-6
Andree, K., Ihde, S., Weske, M., & Pufahl, L. (2022). An exception handling framework for case management. Software and Systems Modeling, 21(3), 939–962. https://doi.org/10.1007/s10270-022-00993-3
Blasques, F., van Brummelen, J., Koopman, S. J., & Lucas, A. (2022). Maximum likelihood estimation for score-driven models. Journal of Econometrics, 227(2), 325–346. https://doi.org/10.1016/j.jeconom.2021.06.003
Chiacchio, F., Oliveri, L., Khodayee, S. M., & D’Urso, D. (2022). Performance Analysis of a Repairable Production Line Using a Hybrid Dependability Queueing Model Based on Monte Carlo Simulation. Applied Sciences, 13(1), 271. https://doi.org/10.3390/app13010271
Chouba, I., Amodeo, L., Arbaoui, T., Yalaoui, F., Laplanche, D., & Sanchez, S. (2022). Optimization of French Hospital Department Activities Using an Efficient Discrete Event Simulation Model. Applied Sciences, 12(19), 9498. https://doi.org/10.3390/app12199498
Dayarathna, V. L., Mismesh, H., Nagahisarchoghaei, M., & Alhumoud, A. (2020). A Discrete Event Simulation (DES) Based Approach To Maximize The Patient Throughput In Outpatient Clinic. Engineering Science & Technology Journal, 1(1), 1–11. https://doi.org/10.51594/estj.v1i1.36
Elalouf, A., & Wachtel, G. (2021). Queueing Problems in Emergency Departments: A Review of Practical Approaches and Research Methodologies. Operations Research Forum, 3(1), 2. https://doi.org/10.1007/s43069-021-00114-8
Freund, B. E., & Feyissa, A. M. (2022). EEG as an indispensable tool during and after the COVID-19 pandemic: A review of tribulations and successes. Frontiers in Neurology, 13. https://doi.org/10.3389/fneur.2022.1087969
Garcia-Vicuña, D., Esparza, L., & Mallor, F. (2022). Hospital preparedness during epidemics using simulation: the case of COVID-19. Central European Journal of Operations Research, 30(1), 213–249. https://doi.org/10.1007/s10100-021-00779-w
Gjerloev, A., Grieco, L., Lamé, G., & Crowe, S. (2026). Using design of experiments with discrete event simulation in operational research: a review. Journal of Simulation, 20(5), 603–619. https://doi.org/10.1080/17477778.2025.2527151
Ivanov, D. (2024). Two views of supply chain resilience. International Journal of Production Research, 62(11), 4031–4045. https://doi.org/10.1080/00207543.2023.2253328
Kamath, A., Poojari, S., & Varsha, K. (2025). Assessing the robustness of normality tests under varying skewness and kurtosis: a practical checklist for public health researchers. BMC Medical Research Methodology, 25(1), 206. https://doi.org/10.1186/s12874-025-02641-y
Kementerian Kesehatan Republik Indonesia. (2022). Keputusan Menteri Kesehatan Republik Indonesia Nomor HK.01.07/MENKES/1128/2022 tentang Standar Akreditasi Rumah Sakit. Kementerian Kesehatan Republik Indonesia.
Lacinova, K., Thokala, P., Nicholas, R., Dobay, P., Scalfaro, E., Angehrn, Z., Brennan, R., Boer, I., Lines, C., & Adlard, N. (2022). ENTIMOS: A Discrete Event Simulation Model for Maximising Efficiency of Infusion Suites in Centres Treating Multiple Sclerosis Patients. Applied Health Economics and Health Policy, 20(5), 731–742. https://doi.org/10.1007/s40258-022-00733-0
Ordu, M., Demir, E., Tofallis, C., & Gunal, M. M. (2023). A comprehensive and integrated hospital decision support system for efficient and effective healthcare services delivery using discrete event simulation. Healthcare Analytics, 4, 100248. https://doi.org/10.1016/j.health.2023.100248
Ouda, E., Sleptchenko, A., & Simsekler, M. C. E. (2023). Comprehensive review and future research agenda on discrete-event simulation and agent-based simulation of emergency departments. Simulation Modelling Practice and Theory, 129, 102823. https://doi.org/10.1016/j.simpat.2023.102823
Skapinyecz, R. (2025). Recent Trends in the Optimization of Logistics Systems Through Discrete-Event Simulation and Deep Learning. Algorithms, 18(9), 573. https://doi.org/10.3390/a18090573
Sundar, A., Rahmin, N. A. A., Chen, C. Y., & Nazihah, M. A. (2023). Discrete-event simulation for outpatient flow and emergency patient arrival in a haemodialysis unit. Mathematical Modeling and Computing, 10(4), 1196–1205. https://doi.org/10.23939/mmc2023.04.1196
Zamani, Z. (2023). Discrete Event Simulation for Clinic Design. International Journal of Environment, Architecture, and Societies, 3(01), 1–16. https://doi.org/10.26418/ijeas.2023.3.01.1-16
Zinoviev, D. (2024). Discrete Event Simulation: It’s Easy with SimPy! 1. https://arxiv.org/pdf/2405.01562
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Strategi Penanganan Pengecualian Pasif, Reaktif, Antisipatif: Simulasi Diskrit Ketahanan Operasional Layanan EEG Rawat Jalan
Copyright (c) 2026 Tikno Tikno, Rais Rafdiadzan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
