Augmented Cognition in the Operating Room

With the tremendous advances in technology and computational systems that occurred in the last three decades, we have integrated novel technologies in virtually all human activities, with the ultimate goal of improving performance and enhancing safety in the workplace. As a high-stakes, high-risk human activity, with increasing level of complexity, surgery has also incorporated computational systems to the clinical workflow in the operating room (OR) in order to optimize processes and support the surgical team. In the OR, cognition is extended outside individual team members’ minds toward the entire surgical team and, even further, throughout all human and nonhuman systems involved during surgery. In this chapter, we describe the foundations that underpin augmented cognition in the OR, as well as the existing evidence in this realm. Lastly, we discuss future implications and applications of cognitive augmentation in the surgical setting.

[1]  Nicolas Padoy,et al.  A Multi-view RGB-D Approach for Human Pose Estimation in Operating Rooms , 2017, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[2]  Robert J. K. Jacob,et al.  Brain measurement for usability testing and adaptive interfaces: an example of uncovering syntactic workload with functional near infrared spectroscopy , 2009, CHI.

[3]  James D. Hollan,et al.  Distributed cognition: toward a new foundation for human-computer interaction research , 2000, TCHI.

[4]  R Flin,et al.  Development of a rating system for surgeons' non‐technical skills , 2006, Medical education.

[5]  Brian Hazlehurst,et al.  Distributed cognition in the heart room: How situation awareness arises from coordinated communications during cardiac surgery , 2007, J. Biomed. Informatics.

[6]  George S. Avrunin,et al.  Toward Improving Surgical Outcomes by Incorporating Cognitive Load Measurement into Process-Driven Guidance , 2018, 2018 IEEE/ACM International Workshop on Software Engineering in Healthcare Systems (SEHS).

[7]  Guy Rosman,et al.  Machine learning and coresets for automated real-time video segmentation of laparoscopic and robot-assisted surgery , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[8]  Stefl Me,et al.  To Err is Human: Building a Safer Health System in 1999. , 2001, Frontiers of health services management.

[9]  John E. Ziewacz,et al.  Simulation-based trial of surgical-crisis checklists. , 2013, The New England journal of medicine.

[10]  Barbara Endicott-Popovsky,et al.  Augmented Cognition for Socio-Technical Systems , 2019, HCI.

[11]  Rajesh Aggarwal,et al.  Synchronized video and motion analysis for the assessment of procedures in the operating theater. , 2005, Archives of surgery.

[12]  Bradley Hayes,et al.  Robotic assistance in the coordination of patient care , 2018, Int. J. Robotics Res..

[13]  Peter Jüni,et al.  First-year Analysis of the Operating Room Black Box Study. , 2018, Annals of surgery.

[14]  Robert G. Radwin,et al.  Modeling Surgical Technical Skill Using Expert Assessment for Automated Computer Rating , 2017, Annals of surgery.

[15]  Teodor P. Grantcharov,et al.  Using Data to Enhance Performance and Improve Quality and Safety in Surgery. , 2017, JAMA surgery.

[16]  David M Studdert,et al.  Analysis of errors reported by surgeons at three teaching hospitals. , 2003, Surgery.

[17]  Jon A. van Heerden A Prospective Randomized Trial on Heart Rate Variability of the Surgical Team During Laparoscopic and Conventional Sigmoid Resection—Invited Critique , 2001 .

[18]  Larry H Hollier,et al.  Review of "Medical Error-the Third Leading Cause of Death in the US" by Makary MA and Daniel M in BMJ 353: i2139, 2016. , 2017, The Journal of craniofacial surgery.

[19]  M. Makary,et al.  Medical error—the third leading cause of death in the US , 2016, British Medical Journal.

[20]  Russell H. Taylor,et al.  Surgical data science for next-generation interventions , 2017, Nature Biomedical Engineering.

[21]  Steven Yule,et al.  Non-technical skills for surgeons: The NOTSS behaviour marker system , 2015 .

[22]  Dong-Han Ham,et al.  Research Trends of Cognitive Systems Engineering Approaches to Human Error and Accident Modelling in Complex Systems , 2011 .

[23]  Brian Hazlehurst,et al.  Distributed cognition: An alternative model of cognition for medical informatics , 2008, Int. J. Medical Informatics.

[24]  Eduardo Salas,et al.  Patient Safety in the Cardiac Operating Room: Human Factors and Teamwork A Scientific Statement From the American Heart Association , 2013, Circulation.

[25]  Edward H Shortliffe,et al.  Role of cognition in generating and mitigating clinical errors , 2015, BMJ Quality & Safety.

[26]  Nicolai Schoch,et al.  Surgical Data Science: Enabling Next-Generation Surgery , 2017, ArXiv.

[27]  E. Hutchins,et al.  Constructing Meaning from Space, Gesture, and Speech , 1997 .

[28]  George S. Avrunin,et al.  Smart Checklists to Improve Healthcare Outcomes , 2016, 2016 IEEE/ACM International Workshop on Software Engineering in Healthcare Systems (SEHS).

[29]  E. Salas,et al.  Team cognition : understanding the factors that drive process and performance , 2004 .

[30]  Ron Fulbright,et al.  Cognitive Augmentation Metrics Using Representational Information Theory , 2017, HCI.

[31]  Erich Wintermantel,et al.  Development of a Fully Automated Testing Device for Biological, Minimal Invasive and Tissue Engineered Heart Valve Prostheses , 2015 .

[32]  M. Zenati,et al.  Systematic review of measurement tools to assess surgeons' intraoperative cognitive workload , 2018, The British journal of surgery.

[33]  Julie A. Shah,et al.  Computational design of mixed-initiative human–robot teaming that considers human factors: situational awareness, workload, and workflow preferences , 2017, Int. J. Robotics Res..

[34]  Anwesha Khasnobish,et al.  Design and development of portable galvanic skin response acquisition and analysis system , 2016, 2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI).

[35]  S. P. Marshall,et al.  The Index of Cognitive Activity: measuring cognitive workload , 2002, Proceedings of the IEEE 7th Conference on Human Factors and Power Plants.

[36]  Marco A. Zenati,et al.  Cognitive Engineering to Improve Patient Safety and Outcomes in Cardiothoracic Surgery. , 2020, Seminars in thoracic and cardiovascular surgery.

[37]  David A. Kobus,et al.  Overview of the DARPA Augmented Cognition Technical Integration Experiment , 2004, Int. J. Hum. Comput. Interact..

[38]  George S. Avrunin,et al.  Development of an Interactive Dashboard to Analyze Cognitive Workload of Surgical Teams During Complex Procedural Care , 2018, 2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA).

[39]  P. Pronovost,et al.  A systematic review of behavioural marker systems in healthcare: what do we know about their attributes, validity and application? , 2014, BMJ quality & safety.

[40]  R. Reznick,et al.  Validation of an objective structured assessment of technical skill for surgical residents , 1996, Academic medicine : journal of the Association of American Medical Colleges.