A Practice Maturity Model for Moving from Digital Tools to Intelligence-Augmented Pharmacy

Digital adoption in pharmacy is frequently inferred from the presence of electronic records, automated dispensing, decision-support alerts, or artificial-intelligence applications. Such indicators describe technological availability but do not establish whether an organization can use intelligence-generating systems safely, effectively, equitably, and accountably. This article proposes an original practice-maturity model for distinguishing digitization from governed intelligence augmentation. Organizational maturity is defined as the sustainable capability to align a bounded medication-use purpose with dependable data, task-appropriate evidence, workflow integration, professional authority, medication-safety controls, equity assessment, governance, workforce competence, and lifecycle learning. The model combines eight maturity domains, five organizational levels, and conjunctive transition gates. The levels progress from Digitized Foundation through Connected Rule-Based Support, Bounded Validated Intelligence, Supervised Workflow Augmentation, and Governed Intelligence Augmentation. Progression is non-compensatory: technical sophistication cannot offset a critical deficiency in data integrity, safety, human authority, equity, or accountability. Each transition therefore requires evidence appropriate to its claim, moving from infrastructure integrity to model assurance, prospective workflow evaluation, demonstrated practice value, and continuing lifecycle oversight. The model is intended for structured organizational self-assessment, comparative research, implementation planning, and identification of evidence gaps rather than numerical ranking or procurement approval. Validation should examine content validity, inter-rater reliability, construct validity, predictive validity, sensitivity to organizational change, and associations with workflow, safety, equity, and implementation outcomes. The framework remains conceptual, context-dependent, and reversible. It does not establish clinical benefit, regulatory conformity, universal applicability, or deployment readiness, but offers a testable architecture for evaluating when digital pharmacy becomes responsibly intelligence-augmented practice.