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Using the Monte Carlo stochastic method to determine the optimal maintenance frequency of medical devices in real contexts
Título de la revista
Autores
Miguel-Cruz A.
Parra P.A.A.
Ocampo A.F.C.
Guao V.S.P.
Correal O. H.H.
Hernández N.P.C.
Cruz A.N.
Rojas J.S.S.
Torres D.A.Q.
Rodríguez-Dueñas, William R.
Fecha
2019
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Springer Verlag
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Abstract
The purpose of this study was to implement and validate a Monte Carlo Algorithm (MCA) to determine the best T value (the time between two preventative maintenances) that optimizes the achieved availability of equipment types. In doing so, we (1) collected 796 maintenance works orders from 16 medical devices installed in a 900-bed hospital; (2) we fitted the probability distributions for each of the inputs of the achieved availability mathematical model (the mean preventative and corrective service time (in hours)); (3) we generated a set of random inputs following a Weibull distribution of the achieved availability mathematical model; (4) we calculated the achieved availability for every random input generated; this process was repeated for “m” iterations (an accuracy of 1%, 95% CI, alpha = 0.05); (5) the trends of the mean achieved availability for the different maintenance T intervals versus mean time to failure (MTTF) for all the equipment types were plotted; finally, (6) the best T value with the maximum value of the achieved availability of a medical device type for a specific MTTF was the optimal target. The mean simulation time for all the cases was 12 min. The MCA was able to determine the best T value, optimizing the achieved availability in 81.25% of cases. In conclusion, the results showed that, on average, the T maintenance intervals determined by the MCA were statistically significantly different from the original T values suggested either by the clinical engineering department or third-party maintenance providers (MCATmean = 1.68 times/yr, ActualTmean = 2.56 times/yr, p = 0.008). © Springer Nature Singapore Pte Ltd. 2019.
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Keywords
Bioinformatics , Biomedical engineering , Biomedical equipment , Intelligent systems , Maintenance , Stochastic systems , Weibull distribution , Achieved availability , Clinical engineering , Maintenance intervals , Maintenance optimization , Monte carlo algorithms , Monte Carlo stochastic method , Optimal maintenance frequency , Preventative maintenance , Monte Carlo methods , Biomedical engineering , Clinical engineering , Maintenance optimization , Monte carlo simulation , Preventative maintenance frequency
