Using Bibliometric Analysis to Map the Impact of a Simulation Program's Published Scholarly Works.

ABSTRACT: Bibliometrics quantitatively evaluates the targeted literature sources and can help define research and scholarly publications' impact and demonstrate connections for authors, departments, or universities. This article presents a methodology for simulation programs to evaluate their influence in terms of both impact and scope of their published simulation-based healthcare scholarly output. Using the authors' home university and healthcare system as an example, the article outlines a methodology to map research and scholarly works networks within the systems, identify and map connections outside the system, and quantifiably score the overall impact of the simulation program's scholarly output using a common scoring metric, the h-index. This generates an objective measure of impact, rather than a subjective opinion of an organization's research and scholarly impact. The combination of an institutional h-index with mapping of simulation-based healthcare scholarly output provides a full, objective description of the institution's output and provides a benchmark for other simulation programs for comparison.

Artificial Intelligence and the Simulationists.

SUMMARY STATEMENT: The recent introduction of ChatGPT, an advanced, easy-to-use, and freely available artificial intelligence (AI) program, created new possibilities across many industries and professions including healthcare simulation. ChatGPT has the potential to streamline healthcare simulation-based education while also providing insights for the scenario development process that conventional case development may miss. However, there are issues related to accuracy, relevance, and structure of the products produced by the ChatGPT AI program. This article examines 2 AI-generated simulation case examples highlighting strengths and weaknesses while providing guidance on the use of ChatGPT as a simulation resource.