This article examines the joint cognitive systems framework as a foundation for understanding human–machine collaboration. Using Meleis’s theory evaluation model, we describe its origins, principles, and applications and then critique its strengths and limitations. Findings highlight challenges in trust, workload, and integration that limit effective use in health care. Application to obstetric anesthesia demonstrates how combining provider expertise with machine learning insights may improve maternal risk assessment. We propose the modernized extension, human–AI joint cognitive systems, to incorporate ethics and sociocultural factors, supporting safer and more effective clinical decision making.
CookR. I.WoodsD. D. (1996). Special section: Adapting to new technology in the operating room. Human Factors, 38(4), 593–613. https://doi.org/10.1518/001872096778827224
4.
GittermanA.KlausJ.WilliamsK.MurphyJ. (2021). Peripartum optimization and coordination of collaborative care practice: A critical role for the obstetric anesthesiologist in combating maternal morbidity and mortality. Current Opinion in Anaesthesiology, 34(3), 205–211. https://doi.org/10.1097/ACO.0000000000000984
HollnagelE.WoodsD. D. (1983). Cognitive systems engineering: New wine in new bottles. International Journal of Man–Machine Studies, 18(6), 583–600. https://doi.org/10.1016/S0020-7373(83)80034-0
9.
HollnagelE.WoodsD. D. (2005). Joint cognitive systems: Foundations of cognitive systems engineering. CRC Press. https://doi.org/10.1201/9781420038194
10.
HollnagelE.WoodsD. D.LevesonN. G. (2006). Resilience engineering: Concepts and precepts. Ashgate.
11.
KlumpnerT. T.BauerM. E.KheterpalS. (2017). Current applications of big data in obstetric anesthesiology. Current Opinion in Anesthesiology, 30(3), 300. https://doi.org/10.1097/ACO.0000000000000452
12.
MaguireL. (2024). Joint cognitive systems: Ideas with impact for designing safer, more resilient automated systems. IEEE Software, 41(3), 96–99. https://doi.org/10.1109/MS.2024.3362648
13.
MeleisA. I. (2011). A model for evaluation of theories: Description, analysis, critiques, testing, and support. In Theoretical nursing: Development and progress (5th ed., pp. 179–206). Wolters Kluwer Health/Lippincott Williams & Wilkins.
14.
PotterS. S.WoodsD. D.RothE. M.FowlkesJ.HoffmanR. R. (2006). Evaluating the effectiveness of a joint cognitive system: Metrics, techniques, and frameworks. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 50(3), 314–318. https://doi.org/10.1177/154193120605000322
15.
PrinzelL. J.KroisP.EllisK. K.VincentM.StephensC.OzaN.ChanceyE. T.DaviesM.MahR.AckersonJ.InfeldS. I.KigginsD.MatthewsB. L. (2024, January8). The adaptable and resilient safety system: The human factor in future in-time aviation safety management systems. AIAA SCITECH 2024 Forum, Orlando, FL. https://doi.org/10.2514/6.2024-1603
16.
RasmussenJ. (1974). The human data processor as a system component: Bits and pieces of a model (Risø-M 1722). Risø National Laboratory.
TristE. (1981). The evolution of socio-technical systems: A conceptual framework and an action research program. Ontario Quality of Working Life Centre.
19.
von BertalanffyL. (2009). General system theory: Foundations, development, applications (Rev. ed.). Braziller.
20.
WaljeeA. K.HigginsP. D. R.SingalA. G. (2014). A primer on predictive models. Clinical and Translational Gastroenterology, 5(1), e44. https://doi.org/10.1038/ctg.2013.19
21.
WoodsD. D. (1987). Commentary: Cognitive engineering in complex and dynamic worlds. International Journal of Man–Machine Studies, 27(5–6), 571–585. https://doi.org/10.1016/S0020-7373(87)80016-0
WormA. (1998). Joint tactical cognitive systems: Modeling, analysis, and performance assessment. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 42(3), 315–319. https://doi.org/10.1177/154193129804200328
24.
XuW.GaoZ. (2024). Applying HCAI in developing effective human–AI teaming: A perspective from human–AI joint cognitive systems. Interactions, 31(1), 32–37. https://doi.org/10.1145/3635116
