JMIR Publications

ABSTRACT

Background:

Patient-centered care, grounded in respect, empathy, communication and shared decision-making, remains central to modern healthcare. The digital transformation of health systems is reshaping this landscape, with artificial intelligence (AI) emerging as a key driver of change. Applied across functions such as diagnostic support, predictive analytics, patient communication and pathway management, AI can improve accuracy, streamline workflows, reduce administrative burdens and free time for patient engagement. However, the integration of AI raises concerns regarding transparency, equity, accountability, and trust, with potential implications for both rehumanization and dehumanization.

Objective:

This study examines how the adoption of AI in healthcare may influence patient-centered care, exploring its potential to promote rehumanization or contribute to dehumanization. The objective is to identify the factors that shape these outcomes at the macro (policy and infrastructure), meso (institutional practices) and micro (individual behaviors and interactions) levels.

Methods:

To address these questions, the study adopts a qualitative methodology and draws on the perspectives of experts with substantial international experience across European healthcare systems, with some participants also contributing insights from the U.S. market. 20 semi-structured interviews were conducted with healthcare leaders, medical professionals, researchers, legal experts and industry consultants. This multi-stakeholder approach enabled a nuanced examination of how AI integration is perceived and experienced across macro, meso and micro levels of healthcare.

Results:

The analysis explored how system-level factors may influence rehumanizing or dehumanizing outcomes of AI integration. At the macro level, eight factors were identified, including regulatory frameworks, policy priorities and infrastructure, which determine whether efficiency pressures override patient-centered values. At the meso level, five factors were highlighted, such as institutional strategies, workflows and leadership, shaping how AI tools are embedded into care delivery. At the micro level, seven factors related to individual behaviors, trust and doctor–patient interaction dynamics influenced whether AI supports empathy and engagement or diminishes them. Rehumanizing potentials included reducing administrative burden, optimizing patient pathway management, improving health communication and enhancing decision-making. Risks included shorter consultations, reduced empathy, overreliance on automation and erosion of professional identity. Without deliberate alignment with patient-centered principles, efficiency gains may fail to improve patient experience and could undermine the human dimensions of care.

Conclusions:

This study is among the first to empirically assess how AI may affect patient-centered care through rehumanizing and dehumanizing dynamics. Outcomes depend not only on technical capabilities but also on regulatory frameworks, institutional strategies and cultural adaptation. By mapping influencing factors across macro, meso and micro levels, the research provides insights for decision-makers to ensure efficiency gains remain aligned with patient-centered principles. Realizing AI’s promise requires coordinated action to preserve empathy, trust and interpersonal connection, ensuring that innovation strengthens rather than weakens the human dimensions of care.