Autonomic activity and surgical flow disruptions in healthcare providers during cardiac surgery

Cardiac surgery represents a complex sociotechnical environment relying on a combination of technical and non-technical team-based expertise. Surgical flow disruptions (SFDs) may be influenced by a variety of sources, including social, environmental, and emotional factors affecting healthcare provid...

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Main Authors: Kennedy-Metz, Lauren R., Bizzego, Andrea, Dias, Roger D., Furlanello, Cesare, Esposito, Gianluca, Zenati, Marco A.
Other Authors: School of Social Sciences
Format: Conference or Workshop Item
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/144444
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1444442023-03-11T20:22:13Z Autonomic activity and surgical flow disruptions in healthcare providers during cardiac surgery Kennedy-Metz, Lauren R. Bizzego, Andrea Dias, Roger D. Furlanello, Cesare Esposito, Gianluca Zenati, Marco A. School of Social Sciences 2020 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) Social sciences::Psychology Surgery Cardiac Surgery Cardiac surgery represents a complex sociotechnical environment relying on a combination of technical and non-technical team-based expertise. Surgical flow disruptions (SFDs) may be influenced by a variety of sources, including social, environmental, and emotional factors affecting healthcare providers (HCPs). Many of these factors can be readily observed, except for emotional factors (i.e. distress), which represents an underappreciated yet critical source of SFDs. The aim of this study was to demonstrate the sensitivity of autonomic activity metrics to detect an SFD during cardiac surgery. We integrated heart rate variability (HRV) analysis with observation-based annotations to allow data triangulation. Following a critical medication administration error by the anesthesiologist in-training, data sources were consulted to identify events precipitating this near- miss event. Using pyphysio, an open-source physiological signal processing package, we analyzed the attending anesthesiologists’ HRV, specifically the low frequency (LF) power, high frequency (HF) power, LF/HF ratio, standard deviation of normal-to-normal (SDNN), and root mean square of the successive differences (RMSSD) as indicators of ANS activity. A heightened SNS response in the attending anesthesiologists’ physiological arousal was observed as elevations in LF power and LF/HF ratio, as well as depressions in HF power, SDNN, and RMSSD prior to the near- miss event. The attending anesthesiologist subjectively confirmed a state of high distress induced by task-irrelevant environmental factors during this time. Qualitative analysis of audio/video recordings objectively revealed that the autonomic nervous system (ANS) activation detected was temporally associated with an argument over operating room management. This study confirms that it is possible to recognize detrimental psychophysiological influences in cardiac surgery procedures via advanced HRV analysis. To our knowledge, ours is the first such case demonstrating ANS activity coinciding with strong self-reported emotion during live surgery using HRV. Despite extensive experience in the cardiac OR, transient but intense emotional changes may have the potential to disrupt attention processes in even the most experienced HCP. A primary implication of this work is the possibility to detect real-time ANS activity, which could enable personalized interventions to proactively mitigate downstream adverse events. Additional studies on our large database of surgical cases are underway and new studies are actively being planned to confirm this preliminary observation. Accepted version 2020-11-05T07:29:21Z 2020-11-05T07:29:21Z 2020 Conference Paper Kennedy-Metz, L. R., Bizzego, A., Dias, R. D., Furlanello, C., Esposito, G., & Zenati, M. A. (2020). Autonomic activity and surgical flow disruptions in healthcare providers during cardiac surgery. Proceedings of the 2020 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), 200-204. doi:10.1109/CogSIMA49017.2020.9216076 978-1-7281-6001-6 https://hdl.handle.net/10356/144444 10.1109/CogSIMA49017.2020.9216076 200 204 en © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/CogSIMA49017.2020.9216076 application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Social sciences::Psychology
Surgery
Cardiac Surgery
spellingShingle Social sciences::Psychology
Surgery
Cardiac Surgery
Kennedy-Metz, Lauren R.
Bizzego, Andrea
Dias, Roger D.
Furlanello, Cesare
Esposito, Gianluca
Zenati, Marco A.
Autonomic activity and surgical flow disruptions in healthcare providers during cardiac surgery
description Cardiac surgery represents a complex sociotechnical environment relying on a combination of technical and non-technical team-based expertise. Surgical flow disruptions (SFDs) may be influenced by a variety of sources, including social, environmental, and emotional factors affecting healthcare providers (HCPs). Many of these factors can be readily observed, except for emotional factors (i.e. distress), which represents an underappreciated yet critical source of SFDs. The aim of this study was to demonstrate the sensitivity of autonomic activity metrics to detect an SFD during cardiac surgery. We integrated heart rate variability (HRV) analysis with observation-based annotations to allow data triangulation. Following a critical medication administration error by the anesthesiologist in-training, data sources were consulted to identify events precipitating this near- miss event. Using pyphysio, an open-source physiological signal processing package, we analyzed the attending anesthesiologists’ HRV, specifically the low frequency (LF) power, high frequency (HF) power, LF/HF ratio, standard deviation of normal-to-normal (SDNN), and root mean square of the successive differences (RMSSD) as indicators of ANS activity. A heightened SNS response in the attending anesthesiologists’ physiological arousal was observed as elevations in LF power and LF/HF ratio, as well as depressions in HF power, SDNN, and RMSSD prior to the near- miss event. The attending anesthesiologist subjectively confirmed a state of high distress induced by task-irrelevant environmental factors during this time. Qualitative analysis of audio/video recordings objectively revealed that the autonomic nervous system (ANS) activation detected was temporally associated with an argument over operating room management. This study confirms that it is possible to recognize detrimental psychophysiological influences in cardiac surgery procedures via advanced HRV analysis. To our knowledge, ours is the first such case demonstrating ANS activity coinciding with strong self-reported emotion during live surgery using HRV. Despite extensive experience in the cardiac OR, transient but intense emotional changes may have the potential to disrupt attention processes in even the most experienced HCP. A primary implication of this work is the possibility to detect real-time ANS activity, which could enable personalized interventions to proactively mitigate downstream adverse events. Additional studies on our large database of surgical cases are underway and new studies are actively being planned to confirm this preliminary observation.
author2 School of Social Sciences
author_facet School of Social Sciences
Kennedy-Metz, Lauren R.
Bizzego, Andrea
Dias, Roger D.
Furlanello, Cesare
Esposito, Gianluca
Zenati, Marco A.
format Conference or Workshop Item
author Kennedy-Metz, Lauren R.
Bizzego, Andrea
Dias, Roger D.
Furlanello, Cesare
Esposito, Gianluca
Zenati, Marco A.
author_sort Kennedy-Metz, Lauren R.
title Autonomic activity and surgical flow disruptions in healthcare providers during cardiac surgery
title_short Autonomic activity and surgical flow disruptions in healthcare providers during cardiac surgery
title_full Autonomic activity and surgical flow disruptions in healthcare providers during cardiac surgery
title_fullStr Autonomic activity and surgical flow disruptions in healthcare providers during cardiac surgery
title_full_unstemmed Autonomic activity and surgical flow disruptions in healthcare providers during cardiac surgery
title_sort autonomic activity and surgical flow disruptions in healthcare providers during cardiac surgery
publishDate 2020
url https://hdl.handle.net/10356/144444
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