Lampotang S, Qian Y, Zhang RV
Department of Anesthesiology, University of Florida
Department of Anesthesiology, Nanjing Medical University
Introduction
Lack of understanding of the rationale for the individual tests in an anesthesia machine pre-use check may contribute to its incorrect execution or total omission. Failure to properly check the anesthesia machine continues to be a recurring problem. It is also one of the predisposing factors to critical incidents that may be most preventable through education and training. Transparent reality simulation (Lampotang et al 2005) of the anesthesia machine pre-use check with animated, color-coded gas molecules may help clinicians visualize and understand the rationale behind the pre-use check and how to properly execute it and interpret the results.
Background
Craig & Wilson (1981), in a survey of 81 anesthetic misadventures, found that human error was responsible for 53 (65%) of the incidents, with failure to perform a pre-anesthetic check the commonest associated factor (27 occurrences out of 81 incidents). The study suggested that failure to check occurred in at least a third of anesthetics.
Cooper et al (1984) found failure to check the commonest associated factor cited in 22% (223/1089) of critical incidents. The authors observed strong indications of a need to develop and use appropriate protocols for preoperative inspection of equipment. They also recommended classifying untoward events in terms of preventive strategy instead of outcome alone
Buffington et al. (1984) found that 13 (7.3%) of 190 study subjects failed to find any of 5 faults (3 of which would administer a hypoxic gas mixture) in an anesthesia machine even though all subjects were informed that faults were present. Only 6 (3.4%) subjects found all 5 faults. Average number of detected faults was 2.2 ± 1.2 out of 5, (44%) with 56% of faults remaining undetected. Practitioners with 10 years of experience or more detected more faults. Many who detected a specific fault related prior encounters with similar faults, suggesting that experience improved the ability to detect faults. The authors recommended that “Greater emphasis should be placed on aggressive system checking in education programs and in daily clinical practice”.
March & Crowley (1991) documented continued poor pre-use check performance. Four faults were introduced in each of two designs of anesthesia machine (NAD Narkomed.2A and Ohmeda Modulus II). In a study of 188 anesthesiologists who has been informed in advance that faults were present but did not know the number of faults, fault detection rate was uniformly poor whether using their own checkout methods (1.03/4; 25.8%) or the 1986 FDA checklist (1.20/4; 29.9%). Fault detection rate was not improved by the 1986 FDA checklist and the authors recommended rewriting the FDA checklist to improve its utility as a clinical tool. The authors also referenced information produced from an FDA state contract (1987) indicating that pre-use checkout practices were inconsistent and that use of the FDA (or similar) checklist was minimal.
More recently, Olympio et al. (1996) demonstrated that instructional review (using videotaped sessions) and intensive training sessions can improve pre-use check performance in a test group of 16 residents. Residents were evaluated on the completion rate, i.e., how many criteria were perfectly executed, on 30 specific criteria while performing the institutional checkout procedures. In the test group exposed to instructional review, completion rates improved from 69% to 81%. The authors concluded that their hypothesis that intensive review of the checkout procedures would lead to high completion rates was not proven. The authors cautioned that “This performance deficit may have implications for the ability of physicians to detect anesthesia machine faults.” The researchers used their institutional checklist which was similar to the 1993 FDA checkout recommendations, introduced while the study was under way.
Manley and Cuddeford (1996) compared the detection abilities of anesthesia providers before and after inclusion of the revised 1993 FDA checklist. Twenty-two anesthesia providers were tested to compare the number of prearranged anesthesia machine faults that could be detected with their usual checkout methods, and with the 1993 FDA checklist. When measured in terms of anesthesia providers missing more than 50% of programmed faults, the 1993 FDA checklist (40.9%) was not more effective than the provider's usual method (54.5%).
Recently, Lampotang et al (2005) reported preliminary results of a worldwide web survey on the anesthesia machine pre-use check. They found that half of respondents who identified themselves as residents replied that they had never been taught how to perform a pre-use check. 29% of respondents rated themselves as poor and 12% as excellent at understanding and performing an anesthesia machine pre-use check.
Method
To address this clear need for education and training in performing the anesthesia machine pre-use check, we developed with funding from the US Anesthesia Patient Safety Foundation, a transparent reality simulation of the 1993 Food and Drug Administration (FDA) anesthesia machine pre-use check that is accessible on the web at http://vam.anest.ufl.edu/members/preusecheck/checkoutvam.html. User registration is required to access the free simulation of the anesthesia machine pre-use check.
We used a learning object approach in implementing the simulation where each step of the 1993 FDA pre-sue check was encapsulated as an autonomous and re-usable learning object, each with its own content, practice and assessment module. The completed simulation will eventually be incorporated into a dynamic and collaborative e-learning system (Su et al 2005) that is also based on learning objects.
Results
Evaluation of the simulation’s effectiveness in training anesthesia providers to better understand and perform the anesthesia machine pre-use check will be performed when the practice and assessment modules of the simulation learning objects are completed.
Discussion
Anesthesia residents must be trained in their formative years about the importance of understanding, performing and interpreting the anesthesia machine pre-use check correctly and efficiently. The results from the web survey by Lampotang et al (2005) seem to indicate that non-compliance with the recommendation to perform an anesthesia machine pre-use check before every case is not limited to any particular country but is worldwide. Consequently, our effort at providing educational materials related to the anesthesia machine pre-use check will be international in scope. We are honored to launch our international efforts as part of the 2005 annual meeting of the Chinese Society of Anesthesiologists.
References:
Buffington, CW, Ramanathan S, Turndorf H: Detection of anesthesia machine faults. Anesthesia & Analgesia 63:79-82, 1984
Cooper JB, Newbower RS, Kitz RJ: An analysis of major errors and equipment failures in anesthesia management: Considerations for prevention and detection. Anesthesiology 60:34-42, 1984
Craig J, Wilson ME: A survey of anesthetic misadventures. Anaesthesia 36:933-36, 1981
Lampotang S, Lizdas D, Gravenstein N, Liem EB: Transparent reality, a simulation based on interactive dynamic graphical models emphasizing visualization. Education Technology, in press, 2005
Lampotang S, Moon S, Lizdas DE, Feldman JM, Zhang RV: Anesthesia machine pre-use check survey - Preliminary results. (abstracted) Anesthesiology, in press. 2005
Manley R, Cuddeford JD: An assessment of the effectiveness of the revised FDA checklist. American Association of Nurse Anesthetists Journal 64(3):277-82, 1996
March MG, Crowley JJ. An evaluation of anesthesiologists’ present checkout methods and the validity of the FDA checklist. Anesthesiology 75:724-729, 1991
Olympio MA, Goldstein MM, Mathes DD: Instructional review improves performance of anesthesia apparatus checkout procedures. Anesthesia & Analgesia 83:618-22, 1996
Su, Stanley Y.W., Lee, G., Lampotang, S., “Learning Object and Dynamic E-Learning Service Technologies for Simulation-based Medical Instruction”, submitted to the fifth IFIP conference on e-Commerce, e-Business, and e-Government, Poznan, Poland, October 26 - 28, 2005.
Copy from:http://www.anesthesia.org.cn/2005china/eng/eng08.doc
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