What Can Analytics for Teamwork Proxemics Reveal About Positioning Dynamics In Clinical Simulations?

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Effective teamwork is critical to improve patient outcomes in healthcare. However, achieving this capability requires that pre-service nurses develop the spatial abilities they will require in their clinical placements, such as: learning when to remain close to the patient and to other team members; positioning themselves correctly at the right time; and deciding on specific team formations (e.g. face-to-face or side-by-side) to enable effective interaction or avoid disrupting clinical procedures. However, positioning dynamics are ephemeral and can easily become occluded by the multiple tasks nurses have to accomplish. Digital traces automatically captured by indoor positioning sensors can be used to address this problem for the purpose of improving nurses’ reflection, learning and professional development. This paper presents a modelling approach that transforms nurses’ low-level position traces to higher-order proxemics constructs in simulation-based teamwork training.To illustrate our approach, we conducted an in-the-wild study with 55 undergraduate students and five educators from whom positioning traces were captured in eleven authentic nursing education classes. Low-level x-y data was used in models of three proxemics constructs: i) co-presence in interactional spaces, ii)socio-spatial formations (i.e. f-formations), and ii) presence in spaces of interest. Through a number of vignettes, we illustrate how indoor positioning analytics can be used to address questions that educators and researchers have about teamwork in healthcare simulation settings.