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Current Research Priorities in Healthcare Simulation: Results of a Delphi Survey.

2022·17 Zitationen·PubMed
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17

Zitationen

3

Autoren

2022

Jahr

Abstract

INTRODUCTION: In 2011 and 2017, the Society for Simulation in Healthcare Research Committee convened summits to develop a forward-thinking agenda for simulation research. After the second summit, the Society for Simulation in Healthcare Research Committee sought expert opinion on the most important research questions in healthcare simulation. This study used systematic methodology to develop a prioritized research agenda for healthcare simulation. METHODS: A modified Delphi approach was conducted in 3 survey rounds. During round 1, individuals with expertise in healthcare simulation research were recruited to submit important research questions. Submitted questions were reviewed and duplicates were removed. Remaining questions were synthesized into a concise, high-level list for further rating. During round 2, these reformulated questions were distributed to the same experts who ranked their importance on a 5-point Likert scale. Average question importance ratings were calculated and shared during round 3, and a final vote was taken to identify the highest priority items. RESULTS: Seventeen experts submitted 74 questions, which were reduced to 21 reformulated items. Variability in expert responses decreased significantly across survey rounds, indicating that consensus had been achieved. The top 3 research question identified by the experts were related to (1) the impact of system level simulation interventions on system efficiency, patient safety, and patient outcomes; (2) the return on investment of simulation for healthcare systems, and (3) whether a dose-response relationship exists between simulation training and performance/patient outcomes. CONCLUSIONS: The agenda developed in this study may help guide and focus researcher efforts and funding agency decisions, ultimately helping advance the field.

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Simulation-Based Education in HealthcareHealthcare Operations and Scheduling OptimizationArtificial Intelligence in Healthcare and Education
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