A cost-utility analysis of medium vs. high-fidelity human patient simulation manikins in nursing education.

Publisher:
Wiley
Publication Type:
Journal Article
Citation:
Journal of Clinical Nursing, 2011, 20 (23-24), pp. 3543 - 3552
Issue Date:
2011-12
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AIMS AND OBJECTIVES: This study presents a cost-utility analysis that compared medium- vs. high-fidelity human patient simulation manikins in nursing education. The analysis sought to determine whether the extra costs associated with high-fidelity manikins can justify the differences, if any, in the outcomes of clinical reasoning, knowledge acquisition and student satisfaction. BACKGROUND: Investment in simulated learning environments has increased at an unprecedented pace. One of the driving forces is the potential for simulation experiences to improve students' learning and engagement. A cost-effectiveness analysis is needed to inform decisions related to investment in and use of simulation equipment. METHOD: Costs associated with the use of medium- and high-fidelity manikins were calculated to determine the total cost for each. A cost-utility analysis using multiattribute utility function was then conducted to combine costs and three outcomes of clinical reasoning, knowledge acquisition and student satisfaction from a quasi-experimental study to arrive at an overall cost utility. RESULTS: The cost analysis indicated that to obtain equivalent clinical reasoning, knowledge acquisition and student satisfaction scores, it required $AU1·21 (US$ 1·14; €0·85) using medium-fidelity as compared with $AU6·28 (US$6·17; €4·40) for high-fidelity human patient simulation manikins per student. CONCLUSION: Based on the results of the cost-utility analysis, medium-fidelity manikins are more cost effective requiring one-fifth of the cost of high-fidelity manikins to obtain the same effect on clinical reasoning, knowledge acquisition and student satisfaction. RELEVANCE TO CLINICAL PRACTICE: It is important that decision-makers have an economic analysis that considers both the costs and outcomes of simulation to identify the approach that has the lowest cost for any particular outcome measure or the best outcomes for a particular cost.
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