Simulation and critical care modeling.
OBJECTIVE
Decisions made in critical care are often complicated, requiring an in-depth understanding of the relations between complex diseases, available interventions, and patients with a wide range of characteristics. Standard modeling techniques such as decision trees and statistical modeling have difficulty in capturing these interactions as the complexity of the problem increases.


RESULTS
Recent models in the literature suggest that simulation modeling techniques such as Markov modeling, Monte Carlo simulation, and discrete-event simulation are useful tools for analyzing complex systems in critical care. These simulation techniques are reviewed briefly, and examples from the literature are presented to demonstrate their usefulness in understanding real problems in critical care.


CONCLUSIONS
Simulation models provide useful tools for organizing and analyzing the interactions between therapies, tradeoffs, and outcomes.
Kreke
Jennifer E
JE
Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Schaefer
Andrew J
AJ
Roberts
Mark S
MS
D003198:Computer Simulation; D003422:Critical Care; D006801:Humans; D008954:Models, Biological; D009010:Monte Carlo Method