Predicting troubleshooting for medical devices

Efficiently restoring clinical usability to malfunctioning medical devices is a top priority. Given varying technician expertise levels and the time-sensitive nature of malfunctions, supporting technicians is essential. This assessment explores the potential of using recorded maintenance data within Ultimo and PEER to provide predictive guidance for resolving issues, and promptly identifying non-technical issues when relevant. This effort aims to enhance efficiency and patient safety.

If a medical device fails due to a malfunction, it is desirable to restore its clinical usability as quickly as possible. Knowledge for repairing medical devices varies among technicians, with not all possessing the same depth of expertise. However, each technician has sufficient knowledge to resolve the malfunction. Given the limited time available to technicians and the pressure to resolve malfunctions as soon as possible, efforts are being made to support technicians. Data for maintenance activities on medical devices is recorded in the Ultimo maintenance management system and is executed and documented according to protocols through the PEER software application.

The question is whether the recorded data can be used to provide predictive feedback to the technician regarding potential solution(s) for the specific malfunction. It is also desirable for it to become immediately evident if there is no technical cause for the report.

you are going to investige the following questions:

  • Investigate whether we can predict the troubleshooting solution for a specific malfunction based on the recorded maintenance history.
  • Examine how technicians can be supported in efficiently conducting their maintenance activities based on evidence-based maintenance.

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