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J Surg Res. 2007 May 15;139(2):229-35. Epub 2006 Dec 11.

The effectiveness of a human patient simulator in the ATLS shock skills station.

Author information

1
Department of Surgery, The Pennsylvania State College of Medicine, Hershey, Pennsylvania 17033, USA. rcherry@psu.edu

Abstract

BACKGROUND:

The use of simulation as a teaching adjunct in Advanced Trauma Life Support (ATLS) has not undergone rigorous psychometric testing. We hypothesized that an advanced, computer-controlled human patient simulator (HPS) would be a useful adjunct to the ATLS shock skills station.

MATERIALS AND METHODS:

Forty-four PGY-1 residents enrolled in ATLS courses were randomized into control (CTL) and experimental (EXP) groups. All students took a shock-specific pre- and post-test multiple choice question examination (MCQE). The EXP group used the HPS in the shock skills station; the CTL group was taught in a traditional manner. All students participated in an experimental, shock-specific objective structured clinical examination (OSCE) session at the end and had their performance evaluated. The EXP group was asked to evaluate the teaching effectiveness of the shock skills station.

RESULTS:

There were no statistically significant differences between the EXP and CTL groups with respect to the pre- and post-test MCQE or the change in scores. The groups were similar in their overall performance during the shock-specific OSCE. The EXP and CTL groups were equivalent with respect to shock recognition, identification of the type of shock, and ability to select the correct treatment plan. The shock skills station was rated from very good to excellent in 91% of the EXP group versus 63% in the CTL group. The EXP group rated the simulator most helpful in learning to analyze data from the monitors.

CONCLUSIONS:

Use of an advanced HPS during the ATLS shock skills station was equivalent to traditional teaching scenarios based on psychometric testing. Students subjectively preferred the simulator as a teaching tool and found it most useful in learning how to integrate data from hemodynamic monitors into clinical decision making.

PMID:
17161432
DOI:
10.1016/j.jss.2006.08.010
[Indexed for MEDLINE]

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