research-article

Toward improving surgical outcomes by incorporating cognitive load measurement into process-driven guidance

Authors:

George S. Avrunin,

Lori A. Clarke,

Heather M. Conboy,

Leon J. Osterweil,

Steven J. Yule,

Julian M. Goldman,

Marco A. ZenatiAuthors Info & Claims

SEHS '18: Proceedings of the International Workshop on Software Engineering in Healthcare Systems

Pages 2 - 9

https://doi.org/10.1145/3194696.3194705

Published: 28 May 2018 Publication History

Abstract

This paper summarizes the accomplishments and recent directions of our medical safety project. Our process-based approach uses a detailed, rigorously-defined, and carefully validated process model to provide a dynamically updated, context-aware and thus, "Smart" Checklist to help process performers understand and manage their pending tasks [7]. This paper focuses on support for teams of performers, working independently as well as in close collaboration, in stressful situations that are life critical. Our recent work has three main thrusts: provide effective real-time guidance for closely collaborating teams; develop and evaluate techniques for measuring cognitive load based on biometric observations and human surveys; and, using these measurements plus analysis and discrete event process simulation, predict cognitive load throughout the process model and propose process modifications to help performers better manage high cognitive load situations.

This project is a collaboration among software engineers, surgical team members, human factors researchers, and medical equipment instrumentation experts. Experimental prototype capabilities are being built and evaluated based upon process models of two cardiovascular surgery processes, Aortic Valve Replacement (AVR) and Coronary Artery Bypass Grafting (CABG). In this paper we describe our approach for each of the three research thrusts by illustrating our work for heparinization, a common subprocess of both AVR and CABG. Heparinization is a high-risk error-prone procedure that involves complex team interactions and thus highlights the importance of this work for improving patient outcomes.

References

[1]

David Arney, Julian M. Goldman, Susan F. Whitehead, and Insup Lee. 2010. Improving Patient Safety with X-Ray and Anesthesia Machine Ventilator Synchronization: A Medical Device Interoperability Case Study. Springer, Berlin, Heidelberg, 96--109.

[2]

Dave Arney, Jeff Plourde, and Julian Goldman. 2018. OpenICE medical device interoperability platform overview and requirement analysis. Biomed. Eng./Biomedizinische Technik 63, 1 (Feb. 2018), 39--48.

[3]

ASTM International. 2009. ASTM F2761-2009. Medical Devices and Medical Systems---Essential Safety Requirements for Equipment Comprising the Patient-Centric Integrated Clinical Environment (ICE), Part 1: General Requirements and Conceptual Model.

[4]

George S. Avrunin, Lori A. Clarke, Leon J. Osterweil, Stefan C. Christov, Bin Chen, Elizabeth A. Henneman, Philip L. Henneman, Lucinda Cassells, and Wilson Mertens. 2010. Experience modeling and analyzing medical processes: UMass/Baystate medical safety project overview. In Proc. of the 1st ACM Int. Health Inform. Symp. 316--325.

Digital Library

[5]

Daniel Boorman. 2001. Today's electronic checklists reduce likelihood of crew errors and help prevent mishaps. International Civil Aviation Organization Journal 56, 1 (2001), 17--36.

[6]

Aaron G. Cass, Barbara Staudt Lerner, Stanley M. Sutton, Jr., Eric K. McCall, Alexander Wise, and Leon J. Osterweil. 2000. Little-JIL/Juliette: A process definition language and interpreter. In Proc. of the 22nd Int. Conf. on Softw. Eng. ACM, New York, NY, USA, 754--757.

Digital Library

[7]

Stefan C. Christov, Heather M. Conboy, Nancy Famigletti, George S. Avrunin, Lori A. Clarke, and Leon J. Osterweil. 2016. Smart Checklists to Improve Healthcare Outcomes. In Proc. of the Int. Workshop on Softw. Eng. in Healthcare Syst. (SEHS '16). ACM, New York, NY, USA, 54--57.

Digital Library

[8]

Heather M. Conboy, George S. Avrunin, Lori A. Clarke, Leon J. Osterweil, Julian M. Goldman, Steven J. Yule, Marco A. Zenati, and Stefan C. Christov. 2017. Cognitive support during high-consequence episodes of care in cardiovascular surgery. In 2017 IEEE Conf. on Cognitive and Computational Aspects of Situation Management (CogSIMA). 1--3.

[9]

Roger D. Dias, M. C. Ngo-Howard, M. T. Boskowski, Marco A. Zenati, and Steven J. Yule. 2018. Systematic Review of Measurement Tools to Assess Surgeons' Intra-operative Cognitive Workload. Br J Surg (2018). Published online.

[10]

Roger D. Dias, Marco A. Zenati, Gabany Jennifer M., Heather M. Conboy, Lori A. Clarke, George S. Avrunin, Leon J. Osterweil, Julian M. Goldman, and Steven J. Yule. 2018. Embedding Real-Time Measure of Surgeons' Cognitive Load into Cardiac Surgery Process Modeling. Academic Surgical Congress (2018).

[11]

Jonathan F. Finks, Nicholas H. Osborne, and John D. Birkmeyer. 2011. Trends in Hospital Volume and Operative Mortality for High-Risk Surgery. N Engl J of Med 364, 22 (2011), 2128--2137.

[12]

Alan Finley and Charles Greenberg. 2013. Heparin Sensitivity and Resistance: Management During Cardiopulmonary Bypass. Anesth Analg 116, 6 (2013), 1210--1222.

[13]

Rhona H. Flin, George G. Youngson, and Steven Yule (Eds.). 2015. Enhancing surgical performance: a primer in non-technical skills. Boca Raton.

[14]

Brigette M. Hales and Peter J. Pronovost. 2006. The checklist: a tool for error management and performance improvement. J Crit Care 21 (2006), 231--235.

[15]

Sandra G. Hart and Lowell E. Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. Advances in Psychology 52 (1988), 139--183.

[16]

L. A. Hassell, A. V. Parwani, L. Weiss, M. A. Jones, and J. Y. 2010. Challenges and opportunities in the adoption of College of American Pathologists checklists in electronic format: perspectives and experience of reporting pathology protocols project (RPP2) participant laboratories. Archives of Pathology and Laboratory Medicine 134, 8 (2010), 1152--1159.

[17]

Brian Hazlehurst, Paul N. Gorman, and Carmit K. McMullen. 2008. Distributed cognition: An alternative model of cognition for medical informatics. Int J of Med Inform 77, 4 (2008), 226--234.

[18]

S. A. Hirji, C. Tarola, H. Amirfarzan, S. J. Yule, J. M. Gabany, R. D. Dias, J. A. Quin, M. Haime, and M. A. Zenati. 2017. Utility and Feasibility of Intra- and Postoperative Crisis Management Checklists in Cardiac Surgery. In Proceedings, 14th Annual Society of Thoracic Surgeons Critical Care Conference.

[19]

Thomas G. Kannampallil, Joanna Abraham, and Vimla L. Patel. 2016. Methodological framework for evaluating clinical processes: A cognitive informatics perspective. Journal of Biomedical Informatics 64 (2016), 342 -- 351.

[20]

Marc I. Kellner, Raymond J. Madachy, and David Raffo. 1999. Software process simulation modeling: Why? What? How? Journal of Systems and Software 46, 2-3 (1999), 91--105.

[21]

L. T. Kohn, J. M. Corrigan, and M. S. Donaldson (Eds.). 1999. To Err Is Human: Building a Safer Health System. National Academies Press.

[22]

Florent Lalys and Pierre Jannin. 2014. Surgical Process Modelling: A Review. Int J of Computer-Assisted Radiology and Surg 9 (2014), 495--511.

[23]

Nancy Leveson, Aubrey Samost, Sidney Dekker, Stan Finkelstein, and Jai Raman. 2017. A Systems Approach to Analyzing and Preventing Hospital Adverse Events. To appear in J Patient Saf (2017).

[24]

Fitzgerald M, Cameron P, Mackenzie C, and et al. 2011. Trauma resuscitation errors and computer-assisted decision support. Archives of Surgery 146, 2 (2011), 218--225.

[25]

Lena Maier-Hein, Swaroop S. Vedula, Stefanie Speidel, Nassir Navab, Ron Kikinis, Adrian Park, Matthias Eisenmann, Hubertus Feussner, Germain Forestier, Stamatia Giannarou, Makoto Hashizume, Darko Katic, Hannes Kenngott, Michael Kranzfelder, Anand Malpani, Keno März, Thomas Neumuth, Nicolas Padoy, Carla Pugh, Nicolai Schoch, Danail Stoyanov, Russell Taylor, Martin Wagner, Gregory D. Hager, and Pierre Jannin. 2017. Surgical data science for next-generation interventions. Nature Biomedical Engineering 1 (Sept. 2017), 691--696.

[26]

Shan Nan, Pieter Van Gorp, Xudong Lu, Uzay Kaymak, Hendrikus Korsten, Richard Vdovjak, and Huilong Duan. 2017. A meta-model for computer executable dynamic clinical safety checklists. BMC Medical Informatics and Decision Making 17, 1 (12 Dec 2017), 170.

[27]

David Raffo. 1998. Software Process Simulation Modeling (ProSim'98): Workshop Report. Empirical Software Engineering 3, 4 (1998), 407--412.

Digital Library

[28]

M. S. Raunak and L. J. Osterweil. 2013. Resource Management for Complex, Dynamic Environments. IEEE Transactions on Software Engineering 39, 3 (March 2013), 384--402.

Digital Library

[29]

Jeffrey Robbins. 2011. Hospital Checklists. Critical Care Nursing Quarterly 34, 2 (2011).

[30]

N. Saberi, M. Mahvash, and M. A. Zenati. 2015. An artificial system for selecting the optimal surgical team. In 2015 37th Annual Int. Conf. of the IEEE Eng. in Medicine and Biology Society (EMBC). 218--221.

[31]

Seung Yeob Shin, Yuriy Brun, and Leon J. Osterweil. 2016. Specification and Analysis of Human-intensive System Resource-utilization Policies. In Proceedings of the International Workshop on Software Engineering in Healthcare Systems (SEHS '16). ACM, New York, NY, USA, 8--14.

Digital Library

[32]

Linda Shore-Lesserson, Robert A. Baker, Victor A. Ferraris, Philip E. Greilich, David Fitzgerald, Philip Roman, and John W. Hammon. 2018. The Society of Thoracic Surgeons, The Society of Cardiovascular Anesthesiologists, and the American Society of ExtraCorporeal Technology: Clinical Practice Guidelines---Anticoagulation During CardioPulmonary Bypass. The Annals of Thoracis Surgery 105 (2018), 650--62. Co-published in Anestesia & Analgesia and the Journal of ExtraCorporeal Technology.

[33]

C. L. Tarola, J. A. Quin, M. E. Haime, J. M. Gabany, K. B. Taylor, K. B. Leissner, and M. A. Zenati. 2016. Computer-Assisted Process Modeling to Enhance Intraoperative Safety in Cardiac Surgery. JAMA Surg 151, 12 (2016), 1183--1186.

[34]

J. A. Wahr, R. L. Prager, J. H. Abernathy III, E. A. Martinez, et al. 2013. Patient Safety in the Cardiac Operating Room: Human Factors and Teamwork: A Scientific Statement from the American Heart Association. Circulation 128, 10 (2013), 1139--69.

[35]

D. A. Wiegmann, A. W. ElBardissi, and J. A. Dearani. 2007. Disruptions in surgical flow and their relationship to surgical errors: an exploratory investigation. Surgery 142, 5 (Nov. 2007), 658--65.

[36]

M. R. Wilson, J. M. Poolton, N. Malhotra, K. Ngo, E. Bright, and R. S. W. Masters. 2011. Development and Validation of a Surgical Workload Measure: The Surgery Task Load Index (SURG-TLX). World J Surg 35, 9 (2011), 1961--1969.

[37]

M. A. Zenati, M. Grumbine, R. Pieraccini, J. A. Gabany, J. Quin, M. Haime, and J. Goldman. 2015. A Novel Point-of-Care Intraoperative Vigilance System to Enhance Surgical Quality and Safety. In NIH-IEEE 2015 Strategic Conf. on Healthcare Innovations and Point-of-Care Technologies.

[38]

He Zhang, Barbara A. Kitchenham, and Dietmar Pfahl. 2010. Software Process Simulation Modeling: An Extended Systematic Review. In ICSP 2010. 309--320.

Digital Library

[39]

John E. Ziewacz, Alexander F. Arriaga, Angela M. Bader, William R. Berry, Lizabeth Edmondson, Judith M. Wong, Stuart R. Lipsitz, David L. Hepner, Sarah Peyre, Steven Nelson, Daniel J. Boorman, Douglas S. Smink, Stanley W. Ashley, and Atul A. Gawande. 2011. Crisis Checklists for the Operating Room: Development and Pilot Testing. Journal of the American College of Surgeons 213, 2 (2011), 212--217.e10.

Cited By

Junger DJust EBrandenburg JWagner MSchaumann KKlenzner TBurgert O(2023)Toward an interoperable, intraoperative situation recognition system via process modeling, execution, and control using the standards BPMN and CMMNInternational Journal of Computer Assisted Radiology and Surgery10.1007/s11548-023-03004-y19:1(69-82)Online publication date: 24-Aug-2023
https://doi.org/10.1007/s11548-023-03004-y
Kennedy-Metz LArshanskiy MKeller SArney DDias RZenati M(2022)Association Between Operating Room Noise and Team Cognitive Workload in Cardiac Surgery2022 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)10.1109/CogSIMA54611.2022.9830675(89-93)Online publication date: 6-Jun-2022
https://doi.org/10.1109/CogSIMA54611.2022.9830675
Dias RKennedy-Metz LYule SGombolay MZenati M(2022)Assessing Team Situational Awareness in the Operating Room via Computer Vision2022 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)10.1109/CogSIMA54611.2022.9830664(94-96)Online publication date: 6-Jun-2022
https://doi.org/10.1109/CogSIMA54611.2022.9830664
Show More Cited By

Index Terms

Toward improving surgical outcomes by incorporating cognitive load measurement into process-driven guidance
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Computer supported cooperative work
2. Software and its engineering
  1. Software notations and tools
    1. Development frameworks and environments
      1. Integrated and visual development environments

Recommendations

DeepPhase: Surgical Phase Recognition in CATARACTS Videos
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Abstract
Automated surgical workflow analysis and understanding can assist surgeons to standardize procedures and enhance post-surgical assessment and indexing, as well as, interventional monitoring. Computer-assisted interventional (CAI) systems based on ...
Physiological synchronization and entropy as measures of team cognitive load
Graphical abstract

Display Omitted
Highlights
- The operating room (OR) is a high-risk and complex environment.
- Cognitive ...
Abstract
The operating room (OR) is a high-risk and complex environment, where multiple specialized professionals work as a team to effectively care for patients in need of surgical interventions. Surgical tasks impose high cognitive demands on ...
Model analysis of coronary hemodynamics incorporating autoregulation

This paper models the autoregulation function for the coronary circulation and analyzes the pulsatile coronary hemodynamics. The autoregulation function acts to maintain a constant average blood flow in the arterioles, as determined by the oxygen ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SEHS '18: Proceedings of the International Workshop on Software Engineering in Healthcare Systems

May 2018

63 pages

ISBN:9781450357340

DOI:10.1145/3194696

Program Chairs:
Ita Richardson
University of Limerick, Limerick, Ireland
,
Jens Weber
University of Victoria, Victoria, Canada

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS: Computer Society

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 May 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ICSE '18

Sponsor:

SIGSOFT
IEEE-CS

ICSE '18: 40th International Conference on Software Engineering

May 28, 2018

Gothenburg, Sweden

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
198
Total Downloads

Downloads (Last 12 months)20
Downloads (Last 6 weeks)3

Reflects downloads up to 21 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Junger DJust EBrandenburg JWagner MSchaumann KKlenzner TBurgert O(2023)Toward an interoperable, intraoperative situation recognition system via process modeling, execution, and control using the standards BPMN and CMMNInternational Journal of Computer Assisted Radiology and Surgery10.1007/s11548-023-03004-y19:1(69-82)Online publication date: 24-Aug-2023
https://doi.org/10.1007/s11548-023-03004-y
Kennedy-Metz LArshanskiy MKeller SArney DDias RZenati M(2022)Association Between Operating Room Noise and Team Cognitive Workload in Cardiac Surgery2022 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)10.1109/CogSIMA54611.2022.9830675(89-93)Online publication date: 6-Jun-2022
https://doi.org/10.1109/CogSIMA54611.2022.9830675
Dias RKennedy-Metz LYule SGombolay MZenati M(2022)Assessing Team Situational Awareness in the Operating Room via Computer Vision2022 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)10.1109/CogSIMA54611.2022.9830664(94-96)Online publication date: 6-Jun-2022
https://doi.org/10.1109/CogSIMA54611.2022.9830664
Junger DBeyersdorffer PKücherer CBurgert O(2022)Service-oriented Device Connectivity interface for a situation recognition system in the ORInternational Journal of Computer Assisted Radiology and Surgery10.1007/s11548-022-02666-417:11(2161-2171)Online publication date: 20-May-2022
https://doi.org/10.1007/s11548-022-02666-4
Kennedy-Metz LDias RPhillips AShapeton AZorca SLeissner KZenati M(2021)Prevalence of Surgical Flow Disruptions Across Intra-operative High- and Low-Workload Phases in Cardiac SurgeryProceedings of the International Symposium on Human Factors and Ergonomics in Health Care10.1177/232785792110124510:1(263-266)Online publication date: 22-Jul-2021
https://doi.org/10.1177/2327857921101245
Ezzat AKogkas AHolt JThakkar RDarzi AMylonas G(2021)An eye-tracking based robotic scrub nurse: proof of conceptSurgical Endoscopy10.1007/s00464-021-08569-wOnline publication date: 8-Jun-2021
https://doi.org/10.1007/s00464-021-08569-w
Kennedy-Metz LDias RSrey RRance GFurlanello CZenati M(2020)Sensors for Continuous Monitoring of Surgeon’s Cognitive Workload in the Cardiac Operating RoomSensors10.3390/s2022661620:22(6616)Online publication date: 19-Nov-2020
https://doi.org/10.3390/s20226616
Dias RShah JZenati M(2020)Artificial intelligence in cardiothoracic surgeryMinerva Cardioangiologica10.23736/S0026-4725.20.05235-468:5Online publication date: Nov-2020
https://doi.org/10.23736/S0026-4725.20.05235-4

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents