Medication-related clinical decision support is commonly designed to detect potentially hazardous prescriptions, interactions, doses, monitoring gaps, or patient–drug mismatches. Detection, however, does not establish that an alert is valid, clinically relevant, sufficiently contextualized, understood by the appropriate professional, or translated into a defensible medication decision. This article develops an original Medication-Sensemaking Model for hospital pharmacy. The model distinguishes signal production from the collaborative construction of meaning and positions alerts as inputs to professional reasoning rather than completed decisions. It proposes a sequence comprising signal-validity assessment, patient–drug–workflow context assembly, relevance adjudication, uncertainty appraisal, rationale formation, collaborative interpretation, action or monitored deferral, escalation, and governance feedback. Human and digital contributions are treated as interdependent but non-equivalent: computational systems may identify patterns and retrieve information, whereas professional authority, accountability, contextual judgment, and responsibility for escalation remain organizationally assigned. Evaluation should therefore extend beyond alert frequency, acceptance, and override rates to include contextual completeness, rationale quality, medication-task performance, workflow consequences, safety, equity, escalation reliability, generalizability, and lifecycle governance. The model is an evidence-informed conceptual synthesis rather than a validated clinical pathway. Its components, transition rules, and escalation boundaries require prospective testing across alert classes, professional configurations, patient populations, and hospital infrastructures. The principal contribution is a structured account of how hospital pharmacy may move from detecting medication signals toward constructing reviewable, uncertainty-aware, and collaboratively actionable medication meaning without equating automation with professional judgment or technical performance with clinical benefit.