Improving the Quality of Evaluation Data in Simulation-Based Healthcare Improvement Projects: A Practitioner's Guide to Choosing and Using Published Measurement Tools.

STATEMENT: Simulation is increasingly being used in healthcare improvement projects. The aims of such projects can be extremely diverse. Accordingly, the outcomes or participant attributes that need to be measured can vary dramatically from project-to-project and may include a wide range of nontechnical skills, technical skills, and psychological constructs. Consequently, there is a growing need for simulation practitioners to be able to identify suitable measurement tools and incorporate them into their work. This article provides a practical introduction and guide to the key considerations for practitioners when selecting and using such tools. It also offers a substantial selection of example tools, both to illustrate the key considerations in relation to choosing a measure (including reliability and validity) and to serve as a convenient resource for those planning a study. By making well-informed choices, practitioners can improve the quality of the data they collect, and the likelihood that their projects will succeed.

From bartending interruptions to medication delivery interruptions: Managing the risks of a high-fidelity simulation study with pilot research.

In this paper we describe the risks of complex applied research, especially in work domains where professional practitioners are scarce. For such research, careful preparation and piloting is needed, especially when estimating sample size is required for a full study. However, such pilot work may reduce the potential sample size for the full study. We describe how the these issues have been addressed in applied psychology contexts. We then present a case study illustrating how we determined sample size for a study investigating the impact of workplace interruptions on errors that intensive care unit nurses might make during medication preparation and administration. The pilot work was performed in a functionally related domain to nursing-bartending-and bartender participants filled cocktail orders. Pilot 1 investigated performance with 0 interruptions and applied a model from a field observation to estimate probable effect sizes and sample sizes with 1 or 4 interruptions per medication scenario. Pilot 2 collected empirical data on the effect of 1 or 4 interruptions per cocktail scenario on cocktail errors and estimated sample size for the medication study, which was subsequently successfully run. The applied community could benefit from further discussions about these issues and the means for addressing them. (PsycInfo Database Record (c) 2020 APA, all rights reserved).