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World J Methodol. Sep 20, 2024; 14(3): 93930
Published online Sep 20, 2024. doi: 10.5662/wjm.v14.i3.93930
Method “Monte Carlo” in healthcare
Tsvetelina Velikova, Niya Mileva, Emilia Naseva
Tsvetelina Velikova, Medical Faculty, Sofia University St. Kliment Ohridski, Sofia 1407, Bulgaria
Niya Mileva, Medical Faculty, Medical University of Sofia, Sofia 1431, Bulgaria
Emilia Naseva, Faculty of Public Health “Prof. Tsekomir Vodenicharov, MD, Dsc,” Medical University of Sofia, Sofia 1431, Bulgaria
Author contributions: Velikova T and Naseva E were involved in conceptualizing the study and writing the manuscript; Mileva N wrote additional sections of the manuscript and crafted the tables; Velikova T was responsible for the critical revision of the manuscript for relevant intellectual content; All authors approved the final version of the paper prior to submission.
Supported by the European Union-NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria, No. BG-RRP-2.004-0008.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Emilia Naseva, PhD, Senior Scientist, Faculty of Public Health “Prof. Tsekomir Vodenicharov, MD, DSc,” Medical University of Sofia, 15 Acad I.E. Geshov Blvd, Sofia 1431, Bulgaria. e.naseva@foz.mu-sofia.bg
Received: March 8, 2024
Revised: May 12, 2024
Accepted: May 27, 2024
Published online: September 20, 2024
Processing time: 109 Days and 7.6 Hours
Abstract

In public health, simulation modeling stands as an invaluable asset, enabling the evaluation of new systems without their physical implementation, experimentation with existing systems without operational adjustments, and testing system limits without real-world repercussions. In simulation modeling, the Monte Carlo method emerges as a powerful yet underutilized tool. Although the Monte Carlo method has not yet gained widespread prominence in healthcare, its technological capabilities hold promise for substantial cost reduction and risk mitigation. In this review article, we aimed to explore the transformative potential of the Monte Carlo method in healthcare contexts. We underscore the significance of experiential insights derived from simulated experimentation, especially in resource-constrained scenarios where time, financial constraints, and limited resources necessitate innovative and efficient approaches. As public health faces increasing challenges, incorporating the Monte Carlo method presents an opportunity for enhanced system construction, analysis, and evaluation.

Keywords: Monte Carlo; Simulation; Healthcare; Modeling; Decision analysis; Stochastic methods; Statistical techniques; Health economics

Core Tip: The potential of the Monte Carlo method in healthcare spreads across decision-making, risk analysis, and modeling in healthcare. Emphasizing versatility, the method navigates uncertainties, offering insights for optimal resource allocation, cost-effectiveness evaluations, and strategic planning in the healthcare domain. The Monte Carlo technique could be demystified through clear illustrations and real-world examples, empowering practitioners to harness its power for robust analyses, enhancing decision accuracy, and contributing to improved healthcare strategies and outcomes.