Clinical data undergo multiple transformations – from clinical study report (CSR) to peer-reviewed manuscript, to healthcare professional (HCP) materials, to patient-facing content – with each handoff introducing potential distortion through compression, simplification, or strategic framing. These distortions can accumulate, ultimately affecting patient understanding, treatment adherence, and trust. Com munication integrity should be viewed and governed end-to-end rather than assessed separately at each stage, since the patient experiences the cumulative effect of all upstream choices. Key distortions include selective outcome reporting, downplayed safety signals, visual distortion of effect sizes, and oversimplified patient materials. A framework emphasising traceability to source data, reconciliation between CSR and downstream materials, patient co-design, and post-distribution monitoring is proposed. It also examines AI’s dual role – as both a tool for consistency-checking and a potential amplifier of bias or hallucination – recommending transparency, traceability, and mandatory human sign-off. Ultimately, success in communication of clinical data should be measured by patient comprehension, wellbeing and outcomes, not procedural compliance alone.