To investigate the effect of a cognitive aid on the visual attention distribution of the operator using the Salience Effort Expectancy Value (SEEV) model.
Background
Cognitive aids aim to support an operator during the execution of a task. The effect of cognitive aids on performance is frequently evaluated but whether a cognitive aid improved, for example, attention distribution has not been considered.
Method
We built the Expectancy Value (EV) model version which can be considered to indicate optimal attention distribution for a given event. We analyzed the eye tracking data of emergency physicians while using a cognitive aid application versus no application during a simulated in-hospital cardiac arrest scenario.
Results
The EV model could fit the attention distribution in such a simulated emergency situation. Partially supporting our hypothesis, the cognitive aid application group showed a significantly better EV model fit than the no application group in the first phases of the event, but a worse fit in the last phase.
Conclusion
We demonstrated that a cognitive aid affected attention distribution and that the SEEV model provides the means of capturing these effects. We suggest that the aid supported and improved visual attention distribution in the stressful first phases of a cardiopulmonary resuscitation but may have focused attention on objects that are relevant for lower priority goals in the last phase.
Application
The SEEV model can provide insights into expected and unexpected effects of cognitive aids on visual attention distribution and may help to design better artifacts.
AbellaB. S.AlvaradoJ. P.MyklebustH.EdelsonD. P.BarryA.O’HearnN.Vanden HoekT. L.BeckerL. B. (2005). Quality of cardiopulmonary resuscitation during in-hospital cardiac arrest. The Journal of the American Medical Association, 293(3), 305–310. https://doi.org/10.1001/jama.293.3.305
2.
BosA. J.RuscioD.CassavaughN. D.LachJ.LachJ.GunaratneP.BacksR. W. (2015). Comparison of novice and experienced drivers using the SEEV model to predict attention allocation at intersections during simulated driving. [Paper presentation]. 8th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, Salt Lake City, UT, June 22-25, 2015. https://doi.org/10.17077/drivingassessment.1560
3.
BurianB. K.CleboneA.DismukesK.RuskinK. J. (2018). More than a tick box: Medical checklist development, design, and use. Anesthesia and Analgesia, 126(1), 223–232. https://doi.org/10.1213/ANE.0000000000002286
4.
CassavaughN. D.BosA.McDonladC.GunaratneP.BacksR. W. (2013). Assessment of the SEEV model to predict attention allocation at intersections during simulated driving. [Paper presentation]. Proceedings of the 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, Bolton Landing, NY, June17-20, 2013. https://doi.org/10.17077/drivingassessment.1509
EndsleyM. R. (2020). The divergence of objective and subjective situation awareness: A meta-analysis. Journal of Cognitive Engineering and Decision Making, 14(1), 34–53. https://doi.org/10.1177/1555343419874248
7.
FeuerstackS.WortelenB. (2017). A model-driven tool for getting insights into car drivers monitoring behavior. [Paper presentation]. IEEE intelligent Vehicles Symposium (IV),Redondo Beach, CA, June 11-14, 2017. https://doi.org/10.1109/IVS.2017.7995824
8.
FieldL. C.McEvoyM. D.SmalleyJ. C.ClarkC. A.McEvoyM. B.RiekeH.NietertP. J.FurseC. M. (2014). Use of an electronic decision support tool improves management of simulated in-hospital cardiac arrest. Resuscitation, 85(1), 138–142. https://doi.org/10.1016/j.resuscitation.2013.09.013
9.
FletcherK. A.BedwellW. L. (2014). Cognitive aids: Design suggestions for the medical field. Proceeding of the International Symposium on Human Factors and Ergonomics in Health Care, 3(1), 148–152. https://doi.org/10.1177/2327857914031024
10.
GrundgeigerT.BeckhK.HappelO. (2020). The effect of effort on the allocation of visual attention during the induction of general anesthesia: A SEEV model analysis. [Paper presentation]. Proceedings of the 64th Human Factors and Ergonomics Society Annual Meeting,Seattle, WA, October 5 - 9, 2020. https://doi.org/10.1177/1071181320641081
11.
GrundgeigerT.HahnF.WurmbT.MeybohmP.HappelO. (2021). The use of a cognitive aid app supports guideline-conforming cardiopulmonary resuscitations: A randomized study in a high-fidelity simulation. Resuscitation Plus, 7, 100152. https://doi.org/10.1016/j.resplu.2021.100152
12.
GrundgeigerT.HohmA.MichalekA.EgenolfT.MarkusC.HappelO. (in press). The validity of the SEEV model as a process measure of situation awareness: The example of a simulated endotracheal intubation. Human Factors. Advance online publication. https://doi.org/10.1177/0018720821991651
13.
GrundgeigerT.HuberS.ReinhardtD.SteinischA.HappelO.WurmbT. (2019). Cognitive aids in acute care: Investigating how cognitive aids affect and support in-hospital emergency teams [Paper presentation]. Conference on Human Factors in Computing Systems (CHI), Glasgow, UK, May 4-9, 2019. https://doi.org/10.1145/3290605.3300884
14.
GrundgeigerT.KlöffelC.MohmeS.WurmbT.HappelO. (2017). An investigation into the effects of real vs. simulated cases and level of experience on the distribution of visual attention during induction of general anaesthesia. Anaesthesia, 72(5), 624–632. https://doi.org/10.1111/anae.13821
15.
GrundgeigerT.WurmbT.HappelO. (2015). Eye tracking in anesthesiology: Literature review, methodological issues, and research topics. [Paper presentation]. 59thAnnual Meeting of the Human Factors and Ergonomics Society, Los Angeles, CA, October 26-30, 2015. https://doi.org/10.1177/1541931215591106
16.
GrundgeigerT.WurmbT.HappelO. (2020). Modelling visual attention of junior and senior anesthesiologists during the induction of general anesthesia in real and simulated cases. IEEE Transactions on Human-Machine Systems, 50(4), 317–326. https://doi.org/10.1109/THMS.2020.2983817
17.
HorreyW. J.WickensC. D.ConsalusK. P. (2006). Modeling drivers’ visual attention allocation while interacting with in-vehicle technologies. Journal of Experimental Psychology: Applied, 12(2), 67–78. https://doi.org/10.1037/1076-898X.12.2.67
18.
HuntE. A.WalkerA. R.ShaffnerD. H.MillerM. R.PronovostP. J. (2008). Simulation of in-hospital pediatric medical emergencies and cardiopulmonary arrests: Highlighting the importance of the first 5 minutes. Pediatrics, 121(1), e34–e43. https://doi.org/10.1542/peds.2007-0029
19.
JonesI.Ann HayesJ.WilliamsJ.LonsdaleH. (2019). Does electronic decision support influence advanced life support in simulated cardiac arrest?British Journal of Cardiac Nursing, 14(2), 72–79. https://doi.org/10.12968/bjca.2019.14.2.72
KohR. Y.ParkT.WickensC. D.OngL. T.ChiaS. N. (2011). Differences in attentional strategies by novice and experienced operating theatre scrub nurses. Journal of Experimental Psychology: Applied, 17(3), 233–246. https://doi.org/10.1037/a0025171
22.
KooT. K.LiM. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine, 15(2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012
23.
LelaidierR.BalançaB.BoetS.FaureA.LilotM.LecomteF.LehotJ. J.RimmeléT.CejkaJ. C. (2017). Use of a hand-held digital cognitive aid in simulated crises: The MAX randomized controlled trial. British Journal of Anaesthesia, 119(5), 1015–1021. https://doi.org/10.1093/bja/aex256
MarshallS. (2013). The use of cognitive aids during emergencies in anesthesia: A review of the literature. Anesthesia and Analgesia, 117(5), 1162–1171. https://doi.org/10.1213/ANE.0b013e31829c397b
26.
MarshallS. D.MehraR. (2014). The effects of a displayed cognitive aid on non-technical skills in a simulated ‘can’t intubate, can’t oxygenate’ crisis. Anaesthesia, 69(7), 669–677. https://doi.org/10.1111/anae.12601
27.
McEvoyM. D.FieldL. C.MooreH. E.SmalleyJ. C.NietertP. J.ScarbroughS. H. (2014). The effect of adherence to ACLS protocols on survival of event in the setting of in-hospital cardiac arrest. Resuscitation, 85(1), 82–87. https://doi.org/10.1016/j.resuscitation.2013.09.019
MooreK.GugertyL. (2010). Development of a novel measure of situation awareness: The case for eye movement analysiss. [Paper presentation]. Proceedings of the 54th Human Factors and Ergonomics Society Annual Meeting,San Francisco, CA, September 27 - October 1, 2010. https://doi.org/10.1177/154193121005401961
30.
ParsonsS. E.CarterE. A.WaterhouseL. J.FritzeenJ.KelleherD. C.O’connellK. J.SarcevicA.BakerK. M.NelsonE.WernerN. E.Boehm-DavisD. A.BurdR. S. (2014). Improving ATLS performance in simulated pediatric trauma resuscitation using a checklist. Annals of Surgery, 259(4), 807–813. https://doi.org/10.1097/sla.0000000000000259
31.
ReinhardtD.KraftA.-K.AlbertM.GrundgeigerT.HappelO.SteinischA.WurmbT. (2015). A tablet-based documentation tool for in-hospital resuscitations. [Paper presentation]. 59thAnnual Meeting of the Human Factors and Ergonomics Society, Los Angeles, CA, October 26 - 30, 2015. https://doi.org/10.1177/1541931215591114
32.
SachedinaA. K.BlissettS.RemtullaA.SridharK.MorrisonD. (2019). Preparing the next generation of code blue leaders through simulation: what’s missing?Simulation in Healthcare, 14(2), 77–81. https://doi.org/10.1097/SIH.0000000000000343
33.
SchriverA. T.MorrowD. G.WickensC. D.TalleurD. A. (2008). Expertise differences in attentional strategies related to pilot decision making. Human Factors, 50(6), 864–878. https://doi.org/10.1518/001872008X374974
34.
SchulzC. M.SchneiderE.FritzL.VockerothJ.HapfelmeierA.BrandtT.KochsE. F.SchneiderG. (2011). Visual attention of anaesthetists during simulated critical incidents. British Journal of Anaesthesia, 106(6), 807–813. https://doi.org/10.1093/bja/aer087
35.
SjöbergF.SchönningE.Salzmann-EriksonM. (2015). Nurses’ experiences of performing cardiopulmonary resuscitation in intensive care units: A qualitative study. Journal of Clinical Nursing, 24(17–18), 2522–2528. https://doi.org/10.1111/jocn.12844
36.
SoarJ.NolanJ. P.BöttigerB. W.PerkinsG. D.LottC.CarliP.PellisT.SandroniC.SkrifvarsM. B.SmithG. B.SundeK.DeakinC. D. (2015). European resuscitation council guidelines for resuscitation 2015. Resuscitation, 95, 100–147. https://doi.org/10.1016/j.resuscitation.2015.07.016
37.
SteelmanK. S.McCarleyJ. S.WickensC. D. (2011). Modeling the control of attention in visual workspaces. Human Factors, 53(2), 142–153. https://doi.org/10.1177/0018720811404026
38.
SteelmanK. S.McCarleyJ. S.WickensC. D. (2017). Theory-based models of attention in visual workspaces. International Journal of Human–Computer Interaction, 33(1), 35–43. https://doi.org/10.1080/10447318.2016.1232228
39.
WickensC. D. (2015). Noticing events in the visual workplace: The SEEV and NSEEV models. In HoffmanR. R.HancockP. A.ScerboM. W.ParasuramanR.SzalmaJ. L. (Eds.), Cambridge Handbook of Applied Perception Research (pp. 749–768). Cambridge.
40.
WickensC. D.GohJ.HellebergJ.HorreyW. J.TalleurD. A. (2003). Attentional models of multitask pilot performance using advanced display technology. Human Factors, 45(3), 360–380. https://doi.org/10.1518/hfes.45.3.360.27250
WickensC. D.McCarleyJ. S.AlexanderA. L.ThomasL. C.AmbinderM.ZhengS. (2008). Attention-situation awareness (A-SA) model of pilot error. In FoyleD.HooeyB. (Eds.), Human performance models in aviation (pp. 213–239). Taylor & Francis.
WuL.CirimeleJ.LeachK.CardS.ChuL.HarrisonT. K.KlemmerS. R. (2014). Supporting crisis response with dynamic procedure aids. [Paper presentation]. DIS '14: Designing Interactive Systems Conference2014, Vancouver, BC, June 21-25, 2014. https://doi.org/10.1145/2598510.2598565
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