Introduction: As simulator fidelity (i.e., realism) increases from low to high, the simulator more closely resembles the real environment, but it also becomes more expensive. It is generally assumed that the use of high-fidelity simulators results in better learning; however, the effect of fidelity on learning non-technical skills (NTS) is unknown. This was a non-inferiority trial comparing the efficacy of high- vs low-fidelity simulators on learning NTS.
Methods: Thirty-six postgraduate medical trainees were recruited for the trial. During the pre-test phase, the trainees were randomly assigned to manage a scenario using either a high-fidelity simulator (HFS) or a low-fidelity simulator (LFS), followed by expert debriefing. All trainees then underwent a video recorded post-test scenario on a HFS, and the NTS were assessed between the two groups. The primary outcome was the overall post-test Ottawa Global Rating Scale (OGRS), while controlling for overall pre-test OGRS scores. Non-inferiority between the LFS and HFS was based on a non-inferiority margin of greater than 1.
Results: For our primary outcome, the mean (SD) post-test overall OGRS score was not significantly different between the HFS and LFS groups after controlling for pre-test overall OGRS scores [3.8 (0.9) vs 4.0 (0.9), respectively; mean difference, 0.2; 95% confidence interval, -0.4 to 0.8; P = 0.48]. For our secondary outcomes, the post-test total OGRS score was not significantly different between the HFS and LFS groups after controlling for pre-test total OGRS scores (P = 0.33). There were significant improvements in mean overall (P = 0.01) and total (P = 0.003) OGRS scores from pre-test to post-test. There were no significant associations between postgraduate year (P = 0.82) and specialty (P = 0.67) on overall OGRS performance.
Conclusion: This study suggests that low-fidelity simulators are non-inferior to the more costly high-fidelity simulators for teaching NTS to postgraduate medical trainees.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s12630-017-0973-2 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!