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Articles

Impact of cognitive workload and situation awareness on clinicians’ willingness to use an artificial intelligence system in clinical practice

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  • Alzahrani, A. S., Gay, V., Alturki, R., & AlGhamdi, M. J. (2021). Towards understanding the usability attributes of AI-enabled eHealth mobile applications. Journal of Healthcare Engineering, 2021, 5313027. https://doi.org/10.1155/2021/5313027
  • Andriana, I., Riyanto, D., & Darmawan, D. (2019). Workload and motivation on employees performance analyzed by information technology. IOP Conference Series: Materials Science and Engineering, 662(2), 022120. https://doi.org/10.1088/1757-899X/662/2/022120
  • Asan, O., & Choudhury, A. (2021). Research trends in artificial intelligence applications in human factors health care: Mapping review. JMIR Human Factors, 8(2), e28236. https://doi.org/10.2196/28236
  • Aydoğan, R., Sharpanskykh, A., & Lo, J. (2015). A trust-based situation awareness model. Multi-Agent Systems.
  • Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021). Managing artificial intelligence. MIS Quarterly, 45(3), 1433–1450.
  • Brown, R. D., & Galster, S. M. (2004). Effects of reliable and unreliable automation on subjective measures of mental workload, situation awareness, trust and confidence in a dynamic flight task. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 48(1), 147–151. https://doi.org/10.1177/154193120404800132
  • Buettner, R. (2013). Cognitive workload of humans using artificial intelligence systems: Towards objective measurement applying eye-tracking technology. Annual Conference on Artificial Intelligence.
  • Chan, V. C. H., Ross, G. B., Clouthier, A. L., Fischer, S. L., & Graham, R. B. (2022). The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review. Applied Ergonomics, 98, 103574. https://doi.org/10.1016/j.apergo.2021.103574
  • Chao, C.-M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10(1652)p, 1652. https://doi.org/10.3389/fpsyg.2019.01652
  • Chavaillaz, A., Schwaninger, A., Michel, S., & Sauer, J. (2019). Expertise, automation and trust in X-ray screening of cabin baggage. Frontiers in Psychology, 10, 256. https://doi.org/10.3389/fpsyg.2019.00256
  • Choudhury, A. (2021). Quantitatively exploring perceived risk on use of an artificial intelligence system: Blood utilization calculator_RCode. Retrieved 2021, from https://doi.org/10.5281/zenodo.5396675
  • Choudhury, A., & Asan, O. (2022). Impact of accountability, training, and human factors on the use of artificial intelligence in healthcare: Exploring the perceptions of healthcare practitioners in the US. Human Factors in Healthcare, 2, 100021. https://doi.org/10.1016/j.hfh.2022.100021
  • Choudhury, A., Asan, O., & Medow, J. E. (2022). Effect of risk, expectancy, and trust on clinicians’ intent to use an artificial intelligence system – Blood Utilization Calculator. Applied Ergonomics, 101, 103708. https://doi.org/10.1016/j.apergo.2022.103708
  • Chowdhury, M., & Sadek, A. W. (2012). Advantages and limitations of artificial intelligence. Artificial Intelligence Applications to Critical Transportation Issues, 6(3), 360–375.
  • Citroen, C. L. (2011). The role of information in strategic decision-making. International Journal of Information Management, 31(6), 493–501. https://doi.org/10.1016/j.ijinfomgt.2011.02.005
  • Connor, J. P., Cunningham, A. M., Raife, T., Rose, W. N., & Medow, J. E. (2017). Standardization of transfusion practice in organ donors using the Digital Intern, an electronic decision support algorithm. Transfusion, 57(6), 1369–1375. https://doi.org/10.1111/trf.14066
  • Connor, J. P., Raife, T., & Medow, J. E. (2018). Outcomes of red blood cell transfusions prescribed in organ donors by the Digital Intern, an electronic decision support algorithm. Transfusion, 58(2), 366–371. https://doi.org/10.1111/trf.14424
  • Connor, J. P., Raife, T., Medow, J. E., Ehlenfeldt, B. D., & Sipsma, K. (2018). The blood utilization calculator, a target-based electronic decision support algorithm, increases the use of single-unit transfusions in a large academic medical center. Transfusion, 58(7), 1689–1696. https://doi.org/10.1111/trf.14637.
  • Dang, Y., Zhang, Y., Brown, S. A., & Chen, H. (2020). Examining the impacts of mental workload and task-technology fit on user acceptance of the social media search system. Information Systems Frontiers, 22(3), 697–718. https://doi.org/10.1007/s10796-018-9879-y
  • Dehn, D. M. (2008). Assessing the impact of automation on the air traffic controller: the SHAPE questionnaires. Air Traffic Control Quarterly, 16(2), 127–146. https://doi.org/10.2514/atcq.16.2.127
  • Dembrower, K., Wåhlin, E., Liu, Y., Salim, M., Smith, K., Lindholm, P., Eklund, M., & Strand, F. (2020). Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study. The Lancet Digital Health, 2(9), e468–e474. https://doi.org/10.1016/S2589-7500(20)30185-0
  • Dupuis, M., Khadeer, S., & Huang, J. (2017). “I Got the Job!”: An exploratory study examining the psychological factors related to status updates on Facebook. Computers in Human Behavior, 73, 132–140. https://doi.org/10.1016/j.chb.2017.03.020
  • Endsley, M. R. (1995a). Measurement of situation awareness in dynamic systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1), 65–84. https://doi.org/10.1518/001872095779049499
  • Endsley, M. R. (1995b). Toward a theory of situation awareness in dynamic systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1), 32–64. https://doi.org/10.1518/001872095779049543
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. 10.2307/3151312
  • Fries, V. C., Wiesche, M., & Krcmar, H. (2016). The dualism of workarounds: Effects of technology and mental workload on improvement and noncompliant behavior within organizations. ICIS.
  • Gang, N., Sibi, S., Michon, R., Mok, B., Chafe, C., & Ju, W. (2018). Don’t Be alarmed: sonifying autonomous vehicle perception to increase situation awareness [Paper presentation]. Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Toronto, ON, Canada. https://doi.org/10.1145/3239060.3265636
  • Ghazizadeh, M., Peng, Y., Lee, J. D., & Boyle, L. N. (2012). Augmenting the technology acceptance model with trust: Commercial drivers’ attitudes towards monitoring and feedback. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 56(1), 2286–2290. https://doi.org/10.1177/1071181312561481
  • Gusenleitner, N., Siedl, S., Stübl, G., Polleres, A., Recski, G., Sommer, R., … Moser, B. A. (2019). Facing mental workload in AI-transformed working environments [Paper presentation]. H-Workload 2019: 3rd International Symposium on Human Mental Workload: Models and Applications (Works in Progress).
  • Hafizoğlu, F. M., & Sen, S. (2019). Understanding the influences of past experience on trust in human-agent teamwork. ACM Transactions on Internet Technology, 19(4), 1–22. https://doi.org/10.1145/3324300
  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis. Springer.
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
  • Hayes, A. F., & Coutts, J. J. (2020). Use Omega Rather than Cronbach’s Alpha for estimating reliability. But…. Communication Methods and Measures, 14(1), 1–24. https://doi.org/10.1080/19312458.2020.1718629
  • Hoff, K. A., & Bashir, M. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human Factors, 57(3), 407–434.
  • Hu, B., Li, S., Chen, Y., Kavi, R., & Coppola, S. (2021). Applying deep neural networks and inertial measurement unit in recognizing irregular walking differences in the real world. Applied Ergonomics, 96, 103414. https://doi.org/10.1016/j.apergo.2021.103414
  • Kainz, B., Heinrich, M. P., Makropoulos, A., Oppenheimer, J., Mandegaran, R., Sankar, S., Deane, C., Mischkewitz, S., Al-Noor, F., Rawdin, A. C., Ruttloff, A., Stevenson, M. D., Klein-Weigel, P., & Curry, N. (2021). Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning. NPJ Digital Medicine, 4(1), 137. https://doi.org/10.1038/s41746-021-00503-7
  • Killingsworth, K., Miller, S. A., & Alavosius, M. P. (2016). A behavioral interpretation of situation awareness: Prospects for organizational behavior management. Journal of Organizational Behavior Management, 36(4), 301–321. https://doi.org/10.1080/01608061.2016.1236056
  • Lee, D. K. L., & Borah, P. (2020). Self-presentation on Instagram and friendship development among young adults: A moderated mediation model of media richness, perceived functionality, and openness. Computers in Human Behavior, 103, 57–66. https://doi.org/10.1016/j.chb.2019.09.017
  • Li, D., Wang, X., Menassa, C. C., & Kamat, V. R. (2020). 12 – Understanding the impact of building thermal environments on occupants’ comfort and mental workload demand through human physiological sensing. Woodhead Publishing.
  • Oksanen, A., Savela, N., Latikka, R., & Koivula, A. (2020). Trust toward robots and artificial intelligence: An experimental approach to human–technology interactions. Frontiers in Psychology, 11(3336), 568256. https://doi.org/10.3389/fpsyg.2020.568256
  • Patel, R. S., Bachu, R., Adikey, A., Malik, M., & Shah, M. (2018). Factors related to physician burnout and its consequences: A review. Behavioral Science (Basel), 8(11):98–105. https://doi.org/10.3390/bs8110098
  • Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. The Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
  • Rhee, K. Y. (2010). Different effects of workers’ trust on work stress, perceived stress, stress reaction, and job satisfaction between Korean and Japanese workers. Safety and Health at Work, 1(1), 87–97. https://doi.org/10.5491/SHAW.2010.1.1.87
  • Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Teuwen, J., Broeders, M., Gennaro, G., Clauser, P., Helbich, T. H., Chevalier, M., Mertelmeier, T., Wallis, M. G., Andersson, I., Zackrisson, S., Sechopoulos, I., & Mann, R. M. (2019). Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study. European Radiology, 29(9), 4825–4832. https://doi.org/10.1007/s00330-019-06186-9
  • Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
  • Sadana, D., Kummangal, B., Moghekar, A., Banerjee, K., Kaur, S., Balasubramanian, S., Tolich, D., Han, X., Wang, X., Hanane, T., Mireles‐Cabodevila, E., Quraishy, N., Duggal, A., & Krishnan, S. (2021). Adherence to blood product transfusion guidelines—An observational study of the current transfusion practice in a medical intensive care unit. Transfusion Medicine, 31(4), 227–235. https://doi.org/10.1111/tme.12771
  • Saremi, M. L., & Bayrak, A. E. (2021). A survey of important factors in human – Artificial intelligence trust for engineering system design [Paper presentation]. Paper Presented at the Proceedings of the ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference.
  • Segars, A. H. (1997). Assessing the unidimensionality of measurement: A paradigm and illustration within the context of information systems research. Omega, 25(1), 107–121. 10.1016/S0305-0483(96)00051-5
  • Smith, R. G., & Eckroth, J. (2017). Building AI applications: Yesterday, today, and tomorrow. AI Magazine, 38(1), 6–22. https://doi.org/10.1609/aimag.v38i1.2709
  • Suarthana, J. H. P., & Riana, I. G. (2016). The effect of psychological contract breach and workload on intention to leave: Mediating role of job stress. Procedia – Social and Behavioral Sciences, 219, 717–723. https://doi.org/10.1016/j.sbspro.2016.05.056
  • Tevell, M., & Burns, P. C. (2000). The effects of perceived risk on mental workload. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 44(37), 682–682. https://doi.org/10.1177/154193120004403739
  • Turek, M. (2021). Explainable artificial intelligence (XAI). Retrieved 2021, from https://www.darpa.mil/program/explainable-artificial-intelligence.
  • Voorhees, C. M., Brady, M. K., Calantone, R., & Ramirez, E. (2016). Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies. Journal of the Academy of Marketing Science, 44(1), 119–134. https://doi.org/10.1007/s11747-015-0455-4
  • Wang, L., & Zhou, Z.-H. (2016). Cost-saving effect of crowdsourcing learning. IJCAI.
  • Zhang, M., Zhang, P., Liu, Y., Wang, H., Hu, K., & Du, M. (2021). Influence of perceived stress and workload on work engagement in front-line nurses during COVID-19 pandemic. Journal of Clinical Nursing, 30(11-12), 1584–1595. https://doi.org/10.1111/jocn.15707
  • Zhao, Y., Li, T., Zhang, X., & Zhang, C. (2019). Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future. Renewable and Sustainable Energy Reviews, 109, 85–101. https://doi.org/10.1016/j.rser.2019.04.021

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