Search

Hospital boards and quality committees depend on monthly clinical governance dashboards to oversee safety, quality, compliance, and organizational risk. Yet producing these reports often requires repeated manual consolidation of metrics, audit findings, incident summaries, and executive commentary from fragmented operational systems. Manual dashboard assembly can delay insight, increase administrative burden, and introduce errors when data are copied across spreadsheets, documents, and presentation templates. These workflows also make it difficult to maintain consistent language, trace every claim to its source, and produce timely board-ready narratives. This article proposes a generative AI framework that ingests quality metrics, audit reports, incident logs, compliance indicators, and executive reporting templates to produce a complete draft clinical governance dashboard. The framework is conceptual and intended to support first-draft preparation rather than autonomous publication. The proposed architecture includes a multi-source data ingestion layer, a template-guided large language model, a factual verification module, and a collaborative human-review interface. Together, these components could transform scattered institutional data into structured narrative sections, exception summaries, and draft governance commentary. The framework could reduce report preparation time, improve formatting consistency, and allow quality specialists to focus on interpretation, escalation, and improvement planning rather than repetitive assembly. Its value would depend on source grounding, auditability, privacy protection, and a clear approval workflow. Automated draft governance reporting is an audacious but feasible application of generative AI in complex health systems. With careful design, human oversight, and rigorous evaluation, such systems could support higher-integrity reporting without replacing clinical accountability.