Why AI Monitoring Faces Resistance and What Healthcare Organizations Can Do About It: An Emotion-Based Perspective

Overview

Journal J Med Internet Res

Publisher JMIR Publications

Date 2025 Jan 31

PMID 39889282

Authors

Karl Werder

Lan Cao

Eun Hee Park

Balasubramaniam Ramesh

Affiliations

Soon will be listed here.

Abstract

Continuous monitoring of patients' health facilitated by artificial intelligence (AI) has enhanced the quality of health care, that is, the ability to access effective care. However, AI monitoring often encounters resistance to adoption by decision makers. Healthcare organizations frequently assume that the resistance stems from patients' rational evaluation of the technology's costs and benefits. Recent research challenges this assumption and suggests that the resistance to AI monitoring is influenced by the emotional experiences of patients and their surrogate decision makers. We develop a framework from an emotional perspective, provide important implications for healthcare organizations, and offer recommendations to help reduce resistance to AI monitoring.

References

Benjamens S, Dhunnoo P, Mesko B . The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med. 2020; 3:118. PMC: 7486909. DOI: 10.1038/s41746-020-00324-0. View

Schumacher K, Stewart B, Archbold P, Dodd M, Dibble S . Family caregiving skill: development of the concept. Res Nurs Health. 2000; 23(3):191-203. DOI: 10.1002/1098-240x(200006)23:3<191::aid-nur3>3.0.co;2-b. View

Bergen G, Stevens M, Burns E . Falls and Fall Injuries Among Adults Aged ≥65 Years - United States, 2014. MMWR Morb Mortal Wkly Rep. 2016; 65(37):993-998. DOI: 10.15585/mmwr.mm6537a2. View

Loewenstein G, Weber E, Hsee C, Welch N . Risk as feelings. Psychol Bull. 2001; 127(2):267-86. DOI: 10.1037/0033-2909.127.2.267. View

Campbell S, Roland M, Buetow S . Defining quality of care. Soc Sci Med. 2000; 51(11):1611-25. DOI: 10.1016/s0277-9536(00)00057-5. View

Wand A, Zhong B, Chiu H, Draper B, De Leo D . COVID-19: the implications for suicide in older adults. Int Psychogeriatr. 2020; 32(10):1225-1230. PMC: 7235297. DOI: 10.1017/S1041610220000770. View

Bevan J, Pecchioni L . Understanding the impact of family caregiver cancer literacy on patient health outcomes. Patient Educ Couns. 2008; 71(3):356-64. DOI: 10.1016/j.pec.2008.02.022. View

Richardson J, Smith C, Curtis S, Watson S, Zhu X, Barry B . Patient apprehensions about the use of artificial intelligence in healthcare. NPJ Digit Med. 2021; 4(1):140. PMC: 8455556. DOI: 10.1038/s41746-021-00509-1. View

Peek S, Wouters E, van Hoof J, Luijkx K, Boeije H, Vrijhoef H . Factors influencing acceptance of technology for aging in place: a systematic review. Int J Med Inform. 2014; 83(4):235-48. DOI: 10.1016/j.ijmedinf.2014.01.004. View

10.

Luijkx K, Peek S, Wouters E . "Grandma, You Should Do It--It's Cool" Older Adults and the Role of Family Members in Their Acceptance of Technology. Int J Environ Res Public Health. 2015; 12(12):15470-85. PMC: 4690935. DOI: 10.3390/ijerph121214999. View

11.

Balagurunathan Y, Mitchell R, El Naqa I . Requirements and reliability of AI in the medical context. Phys Med. 2021; 83:72-78. PMC: 8915137. DOI: 10.1016/j.ejmp.2021.02.024. View

12.

Esmaeilzadeh P . Use of AI-based tools for healthcare purposes: a survey study from consumers' perspectives. BMC Med Inform Decis Mak. 2020; 20(1):170. PMC: 7376886. DOI: 10.1186/s12911-020-01191-1. View

13.

Frojd C, Swenne C, Rubertsson C, Gunningberg L, Wadensten B . Patient information and participation still in need of improvement: evaluation of patients' perceptions of quality of care. J Nurs Manag. 2011; 19(2):226-36. DOI: 10.1111/j.1365-2834.2010.01197.x. View

14.

Racine N, Chow C, Hamwi L, Bucsea O, Cheng C, Du H . Health Care Professionals' and Parents' Perspectives on the Use of AI for Pain Monitoring in the Neonatal Intensive Care Unit: Multisite Qualitative Study. JMIR AI. 2024; 3:e51535. PMC: 11041412. DOI: 10.2196/51535. View

15.

Bekbolatova M, Mayer J, Ong C, Toma M . Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives. Healthcare (Basel). 2024; 12(2). PMC: 10815906. DOI: 10.3390/healthcare12020125. View

16.

Berridge C, Wetle T . Why Older Adults and Their Children Disagree About In-Home Surveillance Technology, Sensors, and Tracking. Gerontologist. 2019; 60(5):926-934. DOI: 10.1093/geront/gnz068. View

17.

Rademakers J, Delnoij D, de Boer D . Structure, process or outcome: which contributes most to patients' overall assessment of healthcare quality?. BMJ Qual Saf. 2011; 20(4):326-31. DOI: 10.1136/bmjqs.2010.042358. View

18.

Sterling D, OConnor J, Bonadies J . Geriatric falls: injury severity is high and disproportionate to mechanism. J Trauma. 2001; 50(1):116-9. DOI: 10.1097/00005373-200101000-00021. View

19.

Karpathakis K, Pencheon E, Cushnan D . Learning From International Comparators of National Medical Imaging Initiatives for AI Development: Multiphase Qualitative Study. JMIR AI. 2024; 3:e51168. PMC: 11041418. DOI: 10.2196/51168. View

20.

Aldao A . The Future of Emotion Regulation Research: Capturing Context. Perspect Psychol Sci. 2015; 8(2):155-72. DOI: 10.1177/1745691612459518. View