The recent launch of complex artificial intelligence (AI) in the domain of healthcare has embedded perplexities within patients, clinicians, and policymakers. The opaque and complex nature of artificial intelligence makes it challenging for clinicians to interpret its outcome. Incorrect interpretation and poor utilization of AI might hamper patient safety. The principles of human factors and ergonomics (HFE) can assist in simplifying AI design and consecutively optimize human performance ensuring better understanding of AI outcome, their interaction with the clinical workflow. In this paper, we discuss the interactions of providers with AI and how HFE can influence these interacting components to patient safety.
AsanO.ScanlonM. C.CrottyB.HoldenR. J.FlynnK. E. (2019). Parental perceptions of displayed patient data in a pediatric intensive care unit: An example of unintentional empowerment. Pediatric critical care medicine, 20(5), 435. doi: 10.1097/PCC.0000000000001895
2.
BelluckP. (2017). First digital pill approved to worries about biomedical ‘big brother. New York Times.
3.
CarayonP.HundtA. S.KarshB. T.GursesA. P.AlvaradoC. J.SmithM.BrennanP. F. (2006). Work system design for patient safety: the SEIPS model (Vol. 15). BMJ. doi: 10.1136/qshc.2005.015842
4.
CarayonP.WooldridgeA.HoonakkerP.HundtA. S.KellyM. M. (2020). SEIPS 3.0: Human-centered design of the patient journey for patient safety. Applied Ergonomics, 84, 103033-103033. doi: 10.1016/j.apergo.2019.103033
5.
CarayonP.XieA.KianfarS. (2014). Human factors and ergonomics as a patient safety practice. BMJ Qual Saf, 23(3), 196–205. doi: 10.1136/bmjqs-2013-001812
DanyszK.CicirelloS.MingleE.AssuncaoB.TetarenkoN.MockuteR.. . . DesaiS. (2019). Artificial intelligence and the future of the drug safety professional. Drug safety, 42(4), 491–497. doi: 10.1007/s40264-018-0746-z
8.
FengX.BobayK.WeissM. (2008). Patient safety culture in nursing: a dimensional concept analysis. Journal of advanced nursing, 63(3), 310–319. doi: 10.1111/j.1365-2648.200804728.xCitations:72
9.
GoldhahnJ.RamptonV.SpinasG. (2018). Could artificial intelligence make doctors obsolete?Bmj, 363, 4563-4563. doi: 10.1136/bmj.k4563
10.
HoldenJ.ValdezR. (2014, August). Conceptualizing the Patient Work System, Part B: Macroergonomic Challenges. In and others (Ed.), Human factors in organizational design and management-xi nordic ergonomics society annual conference. doi: 10.4122/dtu:2137
11.
HoldenR. J.CarayonP.GursesA. P.HoonakkerP.HundtS.OzokA. A.Rivera-RodriguezA. J. (2013). SEIPS 2.0: a human factors framework for studying and improving the work of healthcare professionals and patients. Ergonomics, 56(11), 1669-1686. doi: 10.1080/00140139.2013.838643
12.
JalalS.NicolaouS.ParkerW. (2019). Artificial intelligence, radiology, and the way forward. Canadian Association of Radiologists’ Journal, 70(1), 10–12. doi: 10.1016/j.carj.2018.09.004
13.
JiangF.JiangY.ZhiH.DongY.LiH.MaS.. . .WangY. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4), 230–243. doi: 10.1136/svn-2017-000101
14.
KunnemanM.MontoriV. M.Castaneda-GuarderasA.HessE. P. (2016). What Is Shared Decision Making? (and What It Is Not). Academic Emergency Medicine, 23(12), 1320-1324. doi: 10.1111/acem.13065,publisher=
15.
LeonardM.GrahamS.BonacumD. (2004). The human factor: the critical importance of effective teamwork and communication in providing safe care. Quality and Safety in Health Care. Quality and Safety in Health Care, 13, 85-90. 15465961. doi: 10.1136/qhc.13.suppl_1.i85
16.
McCormickJ. B.GreenM. J.ShapiroD. (2018). Medication Nonadherence: There’s an App for That!Mayo Clinic Proceedings, 93(10), 1346-1350. doi: 10.1016/j.mayocp.2018.05.029
17.
MittelmanM.MarkhamS.TaylorM. (2018). Patient commentary: Stop hyping artificial intelligence—patients will always need human doctors. BMJ, 363, k4669-k4669. doi: 10.1136/bmj.k4669
ObermeyerZ.EmanuelE. J. (2016). Predicting the future — big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216-1219. doi: 10.1056/NEJMp1606181
20.
ParasuramanR.ManzeyD. H. (2010). Complacency and Bias in Human Use of Automation: An Attentional Integration. Human Factors: The Journal of the Human Factors and Ergonomics Society, 52(3), 381-410. doi: 10.1177/0018720810376055
21.
ParikhR. B.ObermeyerZ.NavatheA. S. (2019). Regulation of predictive analytics in medicine. Science, 363(6429), 810-812. doi: 10.1126/science.aaw0029
22.
PreusseK. C.MitznerT. L.FaussetC. B.RogersW. A. (2014). Older Adults’ Changes in Intent to Adopt Wellness Management Technologies. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 58(1), 200-204. doi: 10.1177/1541931214581042
23.
RobertsK. J. (1999). Patient empowerment in the United States: a critical commentary. Health Expectations, 2(2), 82-92. doi: 10.1177/1363459319831343
24.
TuffreyI.GiatrasN.GouldingL.AbrahamE.FenwickL.EdwardsC.HollinsS. (2013). Identifying the factors affecting the implementation of strategies to promote a safer environment for patients with learning disabilities in NHS hospitals: a mixed-methods study-Health Services and Delivery Research. In Health Services and Delivery Research. Southampton (UK. doi: 10.3310/hsdr01130
25.
YuK.-H.KohaneI. S. (2019). Framing the challenges of artificial intelligence in medicine. BMJ Quality & Safety, 28(3), 238-241. doi: 10.1136/bmjqs-2018-008551
