Keeping the Patient Visible when Pharmacy Services Become Data-Driven

Data-driven pharmacy services can combine electronic medication records, patient-generated information, clinical decision support, and artificial intelligence to improve the organization and interpretation of medication-related data. However, a technically comprehensive record may still obscure the person whose treatment is being considered. Important explanations, preferences, practical capabilities, and social constraints may be absent, outdated, poorly interpreted, or disconnected from the eventual decision. This article develops an original Patient-Visibility Framework for examining whether the patient remains meaningfully represented when pharmacy services become data-driven. Patient visibility is proposed as a decision property arising when relevant context is captured with provenance, interpreted for the current medication task, open to patient correction, discussed when uncertainty or discordance exists, connected transparently to professional action, and updated over time. The framework organizes visibility through four interacting layers: narrative, preference, capability, and social context. These layers enter a sociotechnical pathway comprising context capture, interpretation, pharmacist–patient dialogue, medication action, follow-up, and accountability. Proposed failure modes include patient–record substitution, narrative flattening, preference freezing, capability misclassification, context staleness, unequal missingness, workflow bypass, and responsibility diffusion. Evaluation therefore requires more than model performance. Construct validity, representational fidelity, workflow feasibility, medication-safety consequences, patient experience, equity, governance, and temporal validity must be examined separately. The framework is a conceptual and evaluative contribution rather than a validated instrument, clinical protocol, or deployment standard. Its categories, relationships, transition gates, and proposed indicators require empirical testing across pharmacy tasks, populations, technologies, and care settings.