CC BY-NC-ND 4.0 · ACI open 2023; 07(01): e1-e7
DOI: 10.1055/s-0042-1760267
Original Article

Implementation of Eye-Tracking Technology to Monitor Clinician Fatigue in Routine Clinical Care: A Feasibility Study

Bashar Kadhim
1   Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, United States
,
Saif Khairat
2   Carolina Health Informatics Program, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
3   School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
,
Fangyong Li
4   Yale School of Public Health, New Haven, Connecticut, United States
,
Isabel T. Gross
5   Department of Pediatrics (Emergency Medicine), Yale School of Medicine, New Haven, Connecticut, United States
,
Bidisha Nath
1   Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, United States
,
Ronald G. Hauser
6   Department of Laboratory Medicine, Yale School of Medicine, New Haven, Connecticut, United States
7   Department of Pathology and Laboratory Medicine, VA Connecticut Healthcare System, West Haven, Connecticut, United States
,
Edward R. Melnick
1   Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, United States
4   Yale School of Public Health, New Haven, Connecticut, United States
› Institutsangaben
Funding None.

Abstract

Background Physician fatigue increases the likelihood of medical errors. Eye-tracking technology offers an unobtrusive and objective way to measure fatigue but has only been implemented in controlled settings.

Objective Our objective was to determine the feasibility of capturing physiological indicators of fatigue using eye-tracking technology in a real-world clinical setting.

Methods A mixed-methods feasibility study was performed in a convenience sample of clinicians practicing in an urban, academic emergency department from November 11 to December 15, 2021. Outcomes included fatigue assessed at the beginning and end of each shift via eye-tracking (with low scores indicating greater fatigue) and self-report.

Results Among 15 participants, self-reported fatigue and task load increased from the beginning to the end of their shift (fatigue visual analog scale [FVAS] 3.7–4.6, p = 0.04; physician task load [PTL] 97.7–154.3, p = 0.01). It was feasible to collect eye-tracking data at a fixed computer workstation with twice daily calibration and 61% capture of reliable data when the clinician was working at the study computer. Eye-tracking metrics did not change significantly from the beginning to the end of the shift. Eye metric fatigue score was associated with the change in PTL score (r 0.59, p = 0.02) but not FVAS. This association persisted after adjusting for age, gender, and role, with every 10-point increase in PTL, there was a 0.02-point increase in fatigue score (p = 0.04).

Conclusion It is unclear whether the inability to detect fatigue via eye-tracking in routine clinical care was due to confounding factors, the technology, study design, sample size, or an absence of physiological fatigue. Further research and advances in functionality are needed to determine the eye-tracking technology's role in measuring clinician fatigue in routine care.

Author Contributions

B.K. and E.R.M. conceived of the work. All authors designed the study. B.K. acquired the data. B.K., B.N., and E.R.M. drafted the initial manuscript. All authors analyzed the data and revised the manuscript and approved the final version submitted for publication. B.K. takes responsibility for all aspects of the work.




Publikationsverlauf

Eingereicht: 03. Mai 2022

Angenommen: 13. November 2022

Artikel online veröffentlicht:
19. Januar 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 
  • References

  • 1 Thomas NK. Resident burnout. JAMA 2004; 292 (23) 2880-2889
  • 2 Caldwell JA, Caldwell JL, Thompson LA, Lieberman HR. Fatigue and its management in the workplace. Neurosci Biobehav Rev 2019; 96: 272-289
  • 3 Melnick ER, Dyrbye LN, Sinsky CA. et al. The association between perceived electronic health record usability and professional burnout among US physicians. Mayo Clin Proc 2020; 95 (03) 476-487
  • 4 Melnick ER, Harry E, Sinsky CA. et al. Perceived electronic health record usability as a predictor of task load and burnout among US physicians: mediation analysis. J Med Internet Res 2020; 22 (12) e23382
  • 5 Khairat S, Coleman C, Ottmar P, Jayachander DI, Bice T, Carson SS. Association of electronic health record use with physician fatigue and efficiency. JAMA Netw Open 2020; 3 (06) e207385
  • 6 Harry E, Sinsky C, Dyrbye LN. et al. Physician task load and the risk of burnout among US physicians in a national survey. Jt Comm J Qual Patient Saf 2021; 47 (02) 76-85
  • 7 Carayon P, Wetterneck TB, Alyousef B. et al. Impact of electronic health record technology on the work and workflow of physicians in the intensive care unit. Int J Med Inform 2015; 84 (08) 578-594
  • 8 Patterson PD, Weaver MD, Fabio A. et al. Reliability and validity of survey instruments to measure work-related fatigue in the emergency medical services setting: a systematic review. Prehosp Emerg Care 2018; 22 (Suppl. 01) 17-27
  • 9 Dawson D, Searle AK, Paterson JL. Look before you (s)leep: evaluating the use of fatigue detection technologies within a fatigue risk management system for the road transport industry. Sleep Med Rev 2014; 18 (02) 141-152
  • 10 O'Reilly-Shah VN. Factors influencing healthcare provider respondent fatigue answering a globally administered in-app survey. PeerJ 2017; 5: e3785-e3785
  • 11 Ashraf H, Sodergren MH, Merali N, Mylonas G, Singh H, Darzi A. Eye-tracking technology in medical education: a systematic review. Med Teach 2018; 40 (01) 62-69
  • 12 Morad Y, Lemberg H, Yofe N, Dagan Y. Pupillography as an objective indicator of fatigue. Curr Eye Res 2000; 21 (01) 535-542
  • 13 Szabadi E. Functional neuroanatomy of the central noradrenergic system. J Psychopharmacol 2013; 27 (08) 659-693
  • 14 van der Wel P, van Steenbergen H. Pupil dilation as an index of effort in cognitive control tasks: a review. Psychon Bull Rev 2018; 25 (06) 2005-2015
  • 15 Prsa M, Dicke PW, Thier P. The absence of eye muscle fatigue indicates that the nervous system compensates for non-motor disturbances of oculomotor function. J Neurosci 2010; 30 (47) 15834-15842
  • 16 Di Stasi LL, McCamy MB, Macknik SL. et al. Saccadic eye movement metrics reflect surgical residents' fatigue. Ann Surg 2014; 259 (04) 824-829
  • 17 Zargari Marandi R, Madeleine P, Omland Ø, Vuillerme N, Samani A. Eye movement characteristics reflected fatigue development in both young and elderly individuals. Sci Rep 2018; 8 (01) 13148
  • 18 Morris TL, Miller JC. Electrooculographic and performance indices of fatigue during simulated flight. Biol Psychol 1996; 42 (03) 343-360
  • 19 Finke C, Pech LM, Sömmer C. et al. Dynamics of saccade parameters in multiple sclerosis patients with fatigue. J Neurol 2012; 259 (12) 2656-2663
  • 20 Khanna D, Pope JE, Khanna PP. et al. The minimally important difference for the fatigue visual analog scale in patients with rheumatoid arthritis followed in an academic clinical practice. J Rheumatol 2008; 35 (12) 2339-2343
  • 21 Herscovitch J, Broughton R. Sensitivity of the Stanford Sleepiness Scale to the effects of cumulative partial sleep deprivation and recovery oversleeping. Sleep 1981; 4 (01) 83-91
  • 22 de Rodez Benavent SA, Nygaard GO, Harbo HF. et al. Fatigue and cognition: Pupillary responses to problem-solving in early multiple sclerosis patients. Brain Behav 2017; 7 (07) e00717
  • 23 Körber M, Cingel A, Zimmermann M, Bengler K. Vigilance decrement and passive fatigue caused by monotony in automated driving. Procedia Manuf 2015; 3: 2403-2409
  • 24 Steinhauer SR, Siegle GJ, Condray R, Pless M. Sympathetic and parasympathetic innervation of pupillary dilation during sustained processing. Int J Psychophysiol 2004; 52 (01) 77-86
  • 25 Duchowski AT, Duchowski AT. Eye Tracking Methodology: Theory and Practice. Springer; 2017
  • 26 Munn SM, Stefano L, Pelz JB. Fixation-identification in dynamic scenes: comparing an automated algorithm to manual coding. Proceedings of the 5th symposium on Applied perception in graphics and visualization; 2008; Los Angeles, California
  • 27 Komogortsev O, Gobert DV, Dai Z. Classification algorithm for saccadic oculomotor behavior. (Report No. TXSTATE-CS-TR-2010-23). Texas State University-San Marcos, Department of Computer Science; 2010
  • 28 Dowiasch S, Marx S, Einhäuser W, Bremmer F. Effects of aging on eye movements in the real world. Front Hum Neurosci 2015; 9: 46
  • 29 McCambridge J, Witton J, Elbourne DR. Systematic review of the Hawthorne effect: new concepts are needed to study research participation effects. J Clin Epidemiol 2014; 67 (03) 267-277