Method “Monte Carlo” in healthcare

Table 1 What computer simulation models allow and what cannot

Advantages[8,9]	Limitations[2,3]
Allow conclusions to be drawn about a new system without having actually to build it or to make changes to an existing one without disrupting its operation	Cannot optimize but can only generate results from “WHAT-IF” queries
Allow the manager to visualize the operations of a new or existing system under different conditions	Cannot obtain correct results from inaccurate data
Allow us to see how different components interact and how this affects the overall system performance	Cannot describe system characteristics that were not included in the model
Allow general insight into the essence of the process	Cannot solve problems; they can only provide information that aids the process of developing a solution
Allow recognition of specific problems and problem areas in the studied system	Cannot give simple answers to complex problems
Assist in the development of particular policies and process plans
Improve system efficiency

Table 2 Application of Monte Carlo method in cancer treatment optimization, personalized medicine and drug efficacy modeling, predictive modeling for disease outcomes, evaluation of treatment risks and benefits, and economic impact assessment of interventions and treatments

Field	Specific issue	Outcomes	Ref.
Cancer treatment optimization	Radiation therapy simulations	Applications of the Monte Carlo method to model treatment heads for neutral and charged particle radiation therapy and specific in-room devices for imaging and therapy purposes	Park et al[22], 2021
	Dose delivery strategies	The method may be used to calculate dose distributions and further investigations aimed at improving dose delivery and planning in cancer patients	Chiuyo et al[23], 2022
Personalized medicine and drug efficacy modeling	Antibiotic dosing regimen analysis	The simulated therapeutic curve was virtually identical to that obtained experimentally	Milligan et al[21], 2013
Predictive modeling for disease outcomes	Infectious disease	The employed Bayesian Monte Carlo regression framework allows for incorporating prior domain knowledge, which makes it suitable for use on limited yet complex datasets as often encountered in epidemiology	Stojanović et al[24], 2019
	COVID-19	The method of the Monte Carlo algorithm was used to conduct Bayesian inference and illustrate the proposed approach with data on COVID-19 from 20 European countries. The approach performs well on simulated data and produces posterior predictions that fit reported cases, deaths, and hospital and intensive care occupancy well	Rehms et al[25], 2024
Evaluation of treatment risks and benefits	Application to the prophylaxis of deep vein thrombosis	The simulation was feasible to model the joint density of therapeutic risks and benefits of prophylaxis in patients with deep vein thrombosis	Lynd et al[26], 2004
Economic impact assessment of interventions and treatments	A Monte Carlo simulation approach for estimating the health and economic impact of interventions provided at SRCs	Using Monte Carlo simulation methods, the health and economic impact of SRCs can be reasonably estimated to demonstrate the utility of SRCs and justify their growing importance in the healthcare delivery landscape of the United States	Arenas et al[5], 2017

COVID-19: Coronavirus disease 2019; SRCs: Student-run clinics.