AUTHOR=Mitchell Aaron A. , Ivimey-Cook Edward R. TITLE=Technology-enhanced simulation for healthcare professionals: A meta-analysis JOURNAL=Frontiers in Medicine VOLUME=10 YEAR=2023 URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1149048 DOI=10.3389/fmed.2023.1149048 ISSN=2296-858X ABSTRACT=Aim

There have been substantial changes in the simulation technology landscape, in particular virtual reality (VR), during the past decade, which have resulted in increased abundance and decreased cost. We therefore updated a previous meta-analysis conducted in 2011, aiming to quantify the impact of digital technology-enhanced simulation (T-ES) compared with traditional teaching in physicians, physicians-in-training, nurses, and nursing students.

Design

We conducted a meta-analysis consisting of randomized controlled trials published in English between January 2011 and December 2021 in peer-reviewed journals indexed in seven databases. Moderators for study duration, instruction, type of healthcare worker, type of simulation, outcome measure, and study quality rated by Medical Education Research Study Quality Instrument (MERSQI) score were included in our model and used to calculate estimated marginal means (EMMs).

Results

The overall effect of T-ES was positive across the 59 studies included in the analysis compared with traditional teaching [overall effect size 0.80 (95% CI 0.60, 1.00)]. This indicates that T-ES is effective in improving outcomes across a wide variety of settings and participants. The impact of T-ES was found to be greatest for expert-rated product metrics such as procedural success, and process metrics such as efficiency, compared with knowledge and procedure time metrics.

Conclusions

The impacts of T-ES training on the outcome measures included in our study were greatest in nurses, nursing students and resident physicians. T-ES was strongest in studies featuring physical high-fidelity mannequins or centers, compared with VR sensory environment T-ES, though there was considerable uncertainty in all statistical analyses. Further high-quality studies are required to assess direct effects of simulation training on patient and public health outcomes.