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Modern health systems generate, store, and analyze unprecedented biological information, yet translating it into coherent longitudinal care remains difficult. Fragmentation across encounters, specialties, organizations, and digital systems contributes to delayed diagnosis, inconsistent interpretation, and management misaligned with patients' health-related context. This paper introduces Clinical Coherence as a framework for understanding how clinically meaningful knowledge is integrated and preserved within health systems. Clinical Coherence is defined as the capacity to maintain a consistent representation of a patient's condition over time through integration of biological knowledge, Clinical Phenomenological Data, and interpretive clinical judgment. Clinical Phenomenological Data refers to structured representations of integrated life experiences relevant to health and wellbeing, including symptoms, function, uncertainty, family systems, spiritual or existential concerns, treatment burden, and signals that shape illness perception and management. These forms of knowledge are vulnerable to fragmentation as care becomes distributed across organizational and technological boundaries. Drawing on healthcare quality, continuity of care, patient-centered medicine, Narrative Medicine, complexity science, knowledge management, and clinical governance, Clinical Coherence is conceptualized as an emergent systems property rather than an attribute of an individual clinician, encounter, or technology. Duty of Care Governance is presented as the architecture through which knowledge is preserved through sensing, responsibility, escalation, and feedback. Primary Ovarian Insufficiency serves as a sentinel condition showing how fragmentation across domains can undermine care despite biomedical knowledge. As digital health and artificial intelligence reshape healthcare, Clinical Coherence offers a framework for evaluating whether systems preserve the conditions through which information becomes knowledge and understanding becomes care.
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Lawrence Merle Nelson (2026) studied this question.
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