Impact of levels of simulation fidelity on training of interns in ACLS

Lauren W.einberger Conlon, David L. Rodgers, Frances S. Shofer, Gregg Y. Lipschik

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

AIM: Identifying the modality and fidelity of simulation that offers the greatest benefit to the learner is critical to Advanced Cardiac Life Support (ACLS) training. Our hypothesis is that participants who receive ACLS training on high-fidelity mannequins will perform better than those trained on low-fidelity mannequins.

METHODS: The study was performed in the context of an ACLS Initial Provider course for new postgraduate year 1 residents and involved 3 training arms: (1) low-fidelity, (2) mid-fidelity, and (3) high-fidelity. Educational outcomes were evaluated by written scores, student evaluations of the course, and expert rater reviews of megacode performance.

RESULTS: A convenience sample of 54 subjects was randomized to 1 of the 3 training arms. All 3 groups significantly improved based on written posttest scores (P < 0.0001); however, pretest to posttest improvement among the 3 training arms was not significantly different: low-fidelity = 42.3 (95% CI, 35.7-48.9); mid-fidelity = 41.3 (95% CI, 34.7-47.9); high-fidelity = 40.8 (95% CI, 34.3-47.5; P = 0.95). All participants felt the simulator environment was realistic regardless of level of fidelity. Participants in the high-fidelity group were less likely to feel comfortable in the simulator environment (P = 0.0045). Clinical performance as assessed by expert raters' megacode scores was better for high-fidelity (66.3) than mid-fidelity (60.1) (P = 0.04).

CONCLUSION: Overall, there was no difference among the 3 groups in test scores or perceived instructor or course quality; however, subjects trained on high-fidelity mannequins performed better than those trained on mid-fidelity with respect to megacode performance.

Original languageEnglish (US)
Pages (from-to)135-141
Number of pages7
JournalHospital practice (1995)
Volume42
Issue number4
DOIs
StatePublished - Oct 1 2014

All Science Journal Classification (ASJC) codes

  • Medicine(all)

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