VitalSimML - A well-formed data structure to Capture Patient Monitoring Scenarios to facilitate the training of nurses via computer-based simulation

Abstract

Introduction: Patient monitoring is both a prevalent and critical nursing duty. Given that it requires the interpretation of vital signs and intricate decision-making, nurses could benefit from simulation-based training. Currently there is a lack of an open data structure for capturing training scenarios that can be used to augment simulation software and virtual reality applications. Methods: Twenty patient monitoring scenarios were analysed to identify the key common elements that are used to provide simulation. These elements aided the development of a data structure for storing training scenarios. Results: A well-formed eXtensible Markup Language (XML) data structure, currently titled VitalSimML, has been developed for capturing patient monitoring scenarios, which can be used for simulation-based training using dynamic intelligent software solutions. Conclusion: VitalSimML is the first attempt at a digital format for capturing patient monitoring scenarios.