Communicating Calibrated Uncertainty at the Point of Dispensing

Digital and artificial-intelligence-supported medication systems increasingly generate risk estimates, alerts, classifications, and recommendations that may appear more certain than their evidential basis permits. At the point of dispensing, pharmacists must interpret these outputs within brief encounters while considering incomplete data, medication-related consequences, patient preferences, and the limits of model applicability. Existing approaches commonly separate technical calibration, uncertainty display, clinical consequence, pharmacist judgement, and patient communication, leaving no coherent mechanism for translating uncertainty into a bounded medication decision. This article proposes a Point-of-Dispensing Uncertainty-Communication Framework that connects five functions: characterizing the source of uncertainty; appraising calibration and applicability; assessing the clinical consequence of error; constructing a decision-relevant communication package; and adapting that package through pharmacist mediation. The framework distinguishes confidence from calibration, uncertainty from recommendation, and model output from professional authority. It proposes five possible action boundaries: routine dispensing with counselling, enhanced uncertainty counselling, additional verification, temporary deferral, and escalation. The contribution is conceptual and non-empirical. Its proposed relationships require validation across technical performance, patient comprehension, appropriate human–AI reliance, medication-decision quality, workflow burden, safety, equity, and governance. Communication should preserve the source, direction, and practical meaning of uncertainty rather than merely simplify presentation. The framework does not establish universal thresholds, clinical effectiveness, regulatory acceptability, or deployment readiness. Its original contribution is to organize calibrated uncertainty as a pharmacist-mediated, consequence-sensitive communication and action problem rather than as a model metric or display feature alone.