Using the Monte Carlo stochastic method to determine the optimal maintenance frequency of medical devices in real contexts

Antonio Miguel Cruz, Pedro Antonio Aya Parra, Andres Felipe Camelo Ocampo, Viena Sofia Plata Guao, Hector H. Correal O., Nidia Patricia Córdoba Hernández, Angelmiro Núñez Cruz, Jefferson Steven Sarmiento Rojas, Daniel Alejandro Quiroga Torres, William Ricardo Rodríguez-Dueñas

Research output: Contribution to journalConference article

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).

Original languageEnglish (US)
Pages (from-to)273-277
Number of pages5
JournalIFMBE Proceedings
Volume68
Issue number3
DOIs
StatePublished - Jan 1 2019
EventWorld Congress on Medical Physics and Biomedical Engineering, WC 2018 - Prague, Czech Republic
Duration: Jun 3 2018Jun 8 2018

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Biomedical Engineering

Cite this

Miguel Cruz, Antonio ; Aya Parra, Pedro Antonio ; Ocampo, Andres Felipe Camelo ; Guao, Viena Sofia Plata ; Correal O., Hector H. ; Hernández, Nidia Patricia Córdoba ; Cruz, Angelmiro Núñez ; Rojas, Jefferson Steven Sarmiento ; Torres, Daniel Alejandro Quiroga ; Rodríguez-Dueñas, William Ricardo. / Using the Monte Carlo stochastic method to determine the optimal maintenance frequency of medical devices in real contexts. In: IFMBE Proceedings. 2019 ; Vol. 68, No. 3. pp. 273-277.
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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).",
author = "{Miguel Cruz}, Antonio and {Aya Parra}, {Pedro Antonio} and Ocampo, {Andres Felipe Camelo} and Guao, {Viena Sofia Plata} and {Correal O.}, {Hector H.} and Hern{\'a}ndez, {Nidia Patricia C{\'o}rdoba} and Cruz, {Angelmiro N{\'u}{\~n}ez} and Rojas, {Jefferson Steven Sarmiento} and Torres, {Daniel Alejandro Quiroga} and Rodr{\'i}guez-Due{\~n}as, {William Ricardo}",
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Using the Monte Carlo stochastic method to determine the optimal maintenance frequency of medical devices in real contexts. / Miguel Cruz, Antonio; Aya Parra, Pedro Antonio; Ocampo, Andres Felipe Camelo; Guao, Viena Sofia Plata; Correal O., Hector H.; Hernández, Nidia Patricia Córdoba; Cruz, Angelmiro Núñez; Rojas, Jefferson Steven Sarmiento; Torres, Daniel Alejandro Quiroga; Rodríguez-Dueñas, William Ricardo.

In: IFMBE Proceedings, Vol. 68, No. 3, 01.01.2019, p. 273-277.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Using the Monte Carlo stochastic method to determine the optimal maintenance frequency of medical devices in real contexts

AU - Miguel Cruz, Antonio

AU - Aya Parra, Pedro Antonio

AU - Ocampo, Andres Felipe Camelo

AU - Guao, Viena Sofia Plata

AU - Correal O., Hector H.

AU - Hernández, Nidia Patricia Córdoba

AU - Cruz, Angelmiro Núñez

AU - Rojas, Jefferson Steven Sarmiento

AU - Torres, Daniel Alejandro Quiroga

AU - Rodríguez-Dueñas, William Ricardo

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N2 - 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).

AB - 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).

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