... IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support. IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support最新文献
Pub Date : 2018-06-01Epub Date: 2018-08-02DOI: 10.1109/COGSIMA.2018.8423995
Roger D Dias, Heather M Conboy, Jennifer M Gabany, Lori A Clarke, Leon J Osterweil, George S Avrunin, David Arney, Julian M Goldman, Giuseppe Riccardi, Steven J Yule, Marco A Zenati
In the surgical setting, team members constantly deal with a high-demand operative environment that requires simultaneously processing a large amount of information. In certain situations, high demands imposed by surgical tasks and other sources may exceed team member's cognitive capacity, leading to cognitive overload which may place patient safety at risk. In the present study, we describe a novel approach to integrate an objective measure of team member's cognitive load with procedural, behavioral and contextual data from real-life cardiac surgeries. We used heart rate variability analysis, capturing data simultaneously from multiple team members (surgeon, anesthesiologist and perfusionist) in a real-time and unobtrusive manner. Using audio-video recordings, behavioral coding and a hierarchical surgical process model, we integrated multiple data sources to create an interactive surgical dashboard, enabling the analysis of the cognitive load imposed by specific steps, substeps and/or tasks. The described approach enables us to detect cognitive load fluctuations over time, under specific conditions (e.g. emergencies, teaching) and in situations that are prone to errors. This in-depth understanding of the relationship between cognitive load, task demands and error occurrence is essential for the development of cognitive support systems to recognize and mitigate errors during complex surgical care in the operating room.
{"title":"Development of an Interactive Dashboard to Analyze Cognitive Workload of Surgical Teams During Complex Procedural Care.","authors":"Roger D Dias, Heather M Conboy, Jennifer M Gabany, Lori A Clarke, Leon J Osterweil, George S Avrunin, David Arney, Julian M Goldman, Giuseppe Riccardi, Steven J Yule, Marco A Zenati","doi":"10.1109/COGSIMA.2018.8423995","DOIUrl":"10.1109/COGSIMA.2018.8423995","url":null,"abstract":"<p><p>In the surgical setting, team members constantly deal with a high-demand operative environment that requires simultaneously processing a large amount of information. In certain situations, high demands imposed by surgical tasks and other sources may exceed team member's cognitive capacity, leading to cognitive overload which may place patient safety at risk. In the present study, we describe a novel approach to integrate an objective measure of team member's cognitive load with procedural, behavioral and contextual data from real-life cardiac surgeries. We used heart rate variability analysis, capturing data simultaneously from multiple team members (surgeon, anesthesiologist and perfusionist) in a real-time and unobtrusive manner. Using audio-video recordings, behavioral coding and a hierarchical surgical process model, we integrated multiple data sources to create an interactive surgical dashboard, enabling the analysis of the cognitive load imposed by specific steps, substeps and/or tasks. The described approach enables us to detect cognitive load fluctuations over time, under specific conditions (e.g. emergencies, teaching) and in situations that are prone to errors. This in-depth understanding of the relationship between cognitive load, task demands and error occurrence is essential for the development of cognitive support systems to recognize and mitigate errors during complex surgical care in the operating room.</p>","PeriodicalId":91827,"journal":{"name":"... IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support. IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/COGSIMA.2018.8423995","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36781682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-01Epub Date: 2017-05-18DOI: 10.1109/COGSIMA.2017.7929610
Heather M Conboy, George S Avrunin, Lori A Clarke, Leon J Osterweil, Stefan C Christov, Julian M Goldman, Steven J Yule, Marco A Zenati
Despite significant efforts to reduce preventable adverse events in medical processes, such events continue to occur at unacceptable rates. This paper describes a computer science approach that uses formal process modeling to provide situationally aware monitoring and management support to medical professionals performing complex processes. These process models represent both normative and non-normative situations, and are validated by rigorous automated techniques such as model checking and fault tree analysis, in addition to careful review by experts. Context-aware Smart Checklists are then generated from the models, providing cognitive support during high-consequence surgical episodes. The approach is illustrated with a case study in cardiovascular surgery.
{"title":"Cognitive Support During High-Consequence Episodes of Care in Cardiovascular Surgery.","authors":"Heather M Conboy, George S Avrunin, Lori A Clarke, Leon J Osterweil, Stefan C Christov, Julian M Goldman, Steven J Yule, Marco A Zenati","doi":"10.1109/COGSIMA.2017.7929610","DOIUrl":"https://doi.org/10.1109/COGSIMA.2017.7929610","url":null,"abstract":"<p><p>Despite significant efforts to reduce preventable adverse events in medical processes, such events continue to occur at unacceptable rates. This paper describes a computer science approach that uses formal process modeling to provide situationally aware monitoring and management support to medical professionals performing complex processes. These process models represent both normative and non-normative situations, and are validated by rigorous automated techniques such as model checking and fault tree analysis, in addition to careful review by experts. Context-aware Smart Checklists are then generated from the models, providing cognitive support during high-consequence surgical episodes. The approach is illustrated with a case study in cardiovascular surgery.</p>","PeriodicalId":91827,"journal":{"name":"... IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support. IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/COGSIMA.2017.7929610","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35206612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
... IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support. IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support