IntroductionClinician movement and workflow analysis provides an opportunity to identify inefficiencies during trauma resuscitation care. Inefficient workflows may represent latent safety threats (LSTs), defined as unrecognised system-based elements that can negatively impact patients. In situ simulation (ISS) can be used to model resuscitation workflows without direct impact on patients. We report the pilot application of a novel, tracing tool to track clinician movement during high-fidelity ISS trauma sessions.MethodsTwelve unannounced ISSs were conducted. An open source, Windows-based video overlay tracing tool was developed to generate a visual representation of participant movement during ISS. This tracing tool used a manual mouse tracking algorithm to produce point-by-point location information of a selected participant in a video. The tracing tool was applied to video recordings of clinicians performing a cricothyroidotomy during ISS trauma scenarios. A comparative workflow and movement analysis was completed, which included distance travelled and space utilisation. This data was visually represented with time-lapsed movement videos and heat maps.ResultsA fourfold difference in the relative distance travelled was observed between participants who performed a cricothyroidotomy during an ISS trauma resuscitation. Variation in each participant’s movement was attributable to three factors: (1) team role assignment and task allocation; (2) knowledge of clinical space: equipment location and path to equipment retrieval; and (3) equipment bundling. This tool facilitated LST identification related to cricothyroidotomy performance.ConclusionThis novel tracing tool effectively generated a visual representation of participants’ workflows and quantified movement during ISS video review. An improved understanding of human movement during ISS trauma resuscitations provides a unique opportunity to augment simulation debriefing, conduct human factor analysis of system elements (eg, tools/technology, physical environment/layout) and foster change management towards efficient workflows.
Abstract. Super-resolution aims at increasing image resolution by algorithmic means and has progressed over the recent years due to advances in the fields of computer vision and deep learning. Convolutional Neural Networks based on a variety of architectures have been applied to the problem, e.g. autoencoders and residual networks. While most research focuses on the processing of photographs consisting only of RGB color channels, little work can be found concentrating on multi-band, analytic satellite imagery. Satellite images often include a panchromatic band, which has higher spatial resolution but lower spectral resolution than the other bands. In the field of remote sensing, there is a long tradition of applying pan-sharpening to satellite images, i.e. bringing the multispectral bands to the higher spatial resolution by merging them with the panchromatic band. To our knowledge there are so far no approaches to super-resolution which take advantage of the panchromatic band. In this paper we propose a method to train state-of-the-art CNNs using pairs of lower-resolution multispectral and high-resolution pan-sharpened image tiles in order to create super-resolved analytic images. The derived quality metrics show that the method improves information content of the processed images. We compare the results created by four CNN architectures, with RedNet30 performing best.
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