Exploring Physicians’ and Nurses’ Meaning-Making in AI-Driven Decision Support for Oncology Treatment Planning within Digital Health Platforms
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Abstract
Digital health systems and platforms have increasingly integrated artificial intelligence to support clinical decision-making, reshaping how healthcare is delivered and experienced in contemporary practice. Within this domain, AI-driven decision support systems (AI-DSS) have become prominent, yet understanding has largely focused on technical performance and adoption rather than clinicians’ lived experiences. What remains insufficiently understood is how clinicians interpret, experience, and make meaning of AI-DSS in their everyday clinical practice, particularly in relation to professional identity, ethical responsibility, and decision-making. Here we show that an interpretative phenomenological approach provides critical insight into these experiential dimensions by revealing how clinicians actively negotiate meaning and authority when engaging with AI-DSS. This study employed a hermeneutic phenomenological design involving in-depth semi-structured interviews with 15 clinicians working in hospital-based digital health environments who routinely use AI-DSS in their practice. Data were analyzed using interpretative phenomenological analysis (IPA), including iterative coding, identification of meaning units, and thematic abstraction grounded in participants’ lived narratives. The analysis generated four interrelated themes: (1) AI-DSS as a co-analyst shaping diagnostic reasoning; (2) tensions between algorithmic recommendations and professional autonomy; (3) emotional responses ranging from trust to skepticism; and (4) the reconfiguration of ethical responsibility in human–AI collaboration. The findings demonstrate that clinicians experience AI-DSS as sociotechnical presences that influence professional identity, evoke emotional and ethical tensions, and transform clinical decision-making into a reflective human–AI dialogue. Rather than passively adopting AI outputs, participants described actively interpreting, validating, and sometimes resisting algorithmic recommendations, thereby reconstructing authority within digitally mediated care. These findings advance understanding of digital health systems by foregrounding clinicians’ lived experiences and highlight the importance of human-centered design and future research that integrates phenomenological insights into the development of AI-supported healthcare.
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