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

Area of Science:

• Health Informatics
• Artificial Intelligence in Medicine
• Patient Experience

Background:

• Continuous patient health monitoring via artificial intelligence (AI) enhances healthcare quality and access.
• Despite benefits, AI monitoring adoption faces significant resistance from decision-makers.
• Organizational assumptions often attribute resistance to rational cost-benefit evaluations, overlooking emotional factors.

Purpose of the Study:

• To challenge the assumption that resistance to AI monitoring is solely based on rational evaluation.
• To propose a framework for understanding AI monitoring resistance from an emotional perspective.
• To offer actionable recommendations for healthcare organizations to reduce adoption barriers.

Main Methods:

• Literature review and conceptual framework development.
• Analysis of emotional influences on technology adoption in healthcare.
• Synthesis of research on patient and surrogate decision-maker experiences.

Main Results:

• Resistance to AI monitoring is significantly influenced by patients' and surrogate decision-makers' emotional responses.
• Emotional factors such as fear, trust, and perceived control play a crucial role.
• A framework is developed to illustrate the interplay between emotional experiences and AI adoption.

Conclusions:

• Healthcare organizations must address the emotional dimensions of AI monitoring to overcome adoption resistance.
• Understanding and mitigating negative emotional experiences are key to successful AI implementation.
• Recommendations focus on empathetic communication, user involvement, and building trust in AI systems.