Reliability prediction for complex medical systems

Ion, R.A.; Sonnemans, P.J.M.; Wensing, T.

doi:10.1109/rams.2006.1677402

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…Although a number of researchers have considered the reliability prediction of medical systems at the design and development stage over the last two decades5, 6, less literature deals with the reliability prediction of medical devices while they are in use in hospitals. Ion et al 7 analyze the field data for medical imaging systems during the warranty period. Roelfsema8 presents the results of early reliability prediction for Philips medical systems based on field data.…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis of maintenance data for complex medical devices

Taghipour

Banjević

Jardine

2011

Quality & Reliability Eng

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This paper proposes a method to analyze statistically maintenance data for complex medical devices with censoring and missing information. It presents a classification of the different types of failures and establishes policies for analyzing data at the system and component levels taking into account the failure types. The results of this analysis can be used as basic assumptions in the development of a maintenance/inspection optimization model. As a case study, we present the reliability analysis of a general infusion pump from a hospital.

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Section: Introductionmentioning

confidence: 99%

Reliability analysis of maintenance data for complex medical devices

Taghipour

Banjević

Jardine

2011

Quality & Reliability Eng

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“…Six indices were derived from a questionnaire survey, focusing on the importance of the evaluation factors of events. The fault-risk evaluation index system's assessment matrix was constructed by comparing downtime, spare parts cost, failure recurrence, operational errors, uncontrollable factors, and weight allocation ( Table 1 ) [ [33] , [34] , [35] , [36] ]. The correlation degree analysis method determined the correlation level between different indicators, and MATLAB was used again to ascertain the matrix's consistency level and calculate the weight values ( Fig.…”

Section: Methodsmentioning

confidence: 99%

Continuous quality improvement project to reduce the downtime of medical linear accelerators: A case study at Zhejiang Cancer Hospital

Lu,

Wu,

Mao

et al. 2024

Heliyon

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“…New technology can be associated with periods of higher service and support demands as installation and manufacturing issues become apparent, unknown design deficiencies appear, and staff build familiarity and develop competencies [ 10 - 12 ]. To yield operational insights into the initial technical reliability of Halcyon, we divided the 30-month evaluation period into two phases: the entry-into-service period of six months and subsequent 24 months of routine clinical use.…”

Section: Methodsmentioning

confidence: 99%

Operational Insights From the Longitudinal Analysis of a Linear Accelerator Machine Log

Hoisak¹,

Kim²,

Atwood³

et al. 2021

Cureus

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Purpose This study aimed to perform a longitudinal analysis of linear accelerator (linac) technical faults reported with a cloud-based Machine Log system in use in a busy academic clinic and derive operational insights related to linac reliability, clinical utilization, and performance. Methods We queried the Machine Log system for the following parameters: linac type, number of reported technical faults, types of fault, number of faults where the linac was disabled, and estimated clinical downtime. The number of fractions treated and monitor units (MU) delivered were obtained from the record and verify system as metrics of linac utilization and to normalize the number of reported linac faults, facilitating inter-comparison. Two Varian TrueBeam C-arm linacs (Varian Medical Systems, Palo Alto, CA), one Varian 21iX C-arm linac (Varian Medical Systems, Palo Alto, CA), and one newly installed Varian Halcyon ring gantry linac (Varian Medical Systems, Palo Alto, CA) were evaluated. The linacs were studied over a 30-month period from September 2017 to March 2020. Results Over 30 months, comprising 677 clinical days, 1234 faults were reported from all linacs, including 153 “linac down” events requiring rescheduling or cancellation of treatments. The TrueBeam linacs reported nearly twice as many imaging, multileaf collimator (MLC), and beam generation faults per fraction, and MU as the Halcyon. Halcyon experienced fewer beam generation/steering, accessory, and cooling-related faults than the other linacs but reported more computer and networking issues. Although it employs a relatively new MLC design compared to the C-arm linacs and delivers primarily intensity-modulated treatments, Halcyon reported fewer MLC faults than the other linacs. The 21iX linac had the fewest software-related faults but was subject to the most cooling-related faults, which we attributed to extensive use of this linac for treatment techniques with extended beam-on times. Conclusions A longitudinal analysis of a cloud-based Machine Log system yielded operational insights into the utilization, performance, and technical reliability of the linacs in use at our institution. Several trends in linac sub-system reliability were identified and could be attributed to either age, design, clinical use, or operational demands. The results of this analysis will be used as a basis for designing linac quality assurance schedules that reflect actual linac usage and observed sub-system reliability. Such a practice may contribute to a clinic workflow subject to fewer disruptions from linac faults, ultimately improving efficiency and patient safety.

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Reliability prediction for complex medical systems

Cited by 8 publications

References 8 publications

Reliability analysis of maintenance data for complex medical devices

Reliability analysis of maintenance data for complex medical devices

Continuous quality improvement project to reduce the downtime of medical linear accelerators: A case study at Zhejiang Cancer Hospital

Operational Insights From the Longitudinal Analysis of a Linear Accelerator Machine Log

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