Treating AI-Generated Medication Plans as Reversible and Auditable Clinical Objects

Artificial intelligence can generate medication plans that appear clinically coherent while remaining difficult to inspect, update, attribute, or reconstruct. When such plans persist only as prose, their assumptions, supporting evidence, model provenance, professional review, and subsequent modifications may become disconnected from the decisions they influence. This article proposes that an AI-generated medication plan should be represented as a reversible and auditable clinical-information object rather than treated as ephemeral text or an automatically executable medication order. The proposed Auditable Medication-Plan Object Model combines persistent identity, patient- and time-specific context, structured medication actions, rationale, evidence dependencies, uncertainty, model provenance, professional authorization, immutable versions, governance states, and append-only audit events. Material modification creates a successor version without erasing its predecessor. Reversal is separated into informational withdrawal or rollback and, where medication-related action has already occurred, clinical corrective or compensating action. Approval is represented as a version-specific professional decision rather than evidence of correctness or safety. Evaluation should test identity integrity, lineage completeness, state-transition validity, evidence traceability, recoverability, role comprehension, workflow burden, equity implications, and the separation of technical restoration from clinical recovery. The model is an original conceptual synthesis intended to organize future specification, human-factors assessment, workflow simulation, and prospective evaluation. It does not establish clinical effectiveness, medication-safety improvement, regulatory acceptability, legal sufficiency, universal applicability, or deployment readiness.