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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
Published on: October 28, 2022
A prospective study on anesthesia machine fault identification
Eric R Larson1, Gregory A Nuttall, Brian D Ogren
1Department of Anesthesiology, Mayo College of Medicine, Rochester, Minnesota, USA.
Background:
Although few studies have been performed recently, several have suggested that some practitioners are not well able to detect preset anesthesia machine faults.
Methods:
We performed a prospective study to determine whether there is a correlation between duration of anesthesia practice and the ability to detect anesthesia machine faults. Our hypothesis was that more anesthesia practice would increase the ability to detect anesthesia machine faults. This study was performed during a nationally attended anesthesia meeting held at a large academic medical center, where 87 anesthesia providers were observed performing anesthesia machine checkouts. The participants were asked to individually check out an anesthesia machine with an unspecified number of preset faults. The primary outcome measures were the written listing of faults detected during an anesthesia machine checkout.
Results:
Of the five faults preset into the test machine, participants with 0-2 yr experience detected a mean of 3.7 faults, participants with 2-7 yr experience detected a mean of 3.6 faults, and participants with more than 7 yr experience detected a mean of 2.3 faults (P < 0.001).
Conclusions:
Our prospective study demonstrated that anesthesia machine checkout continues to be a problem.
