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Healthcare quality improvement increasingly relies on routinely collected data to identify preventable harm, missed care opportunities, adverse outcomes, and variation in performance. Artificial intelligence predictive models may support earlier detection of quality risks and enable more proactive monitoring than retrospective audits alone. This systematic review examined artificial intelligence predictive models for healthcare quality improvement from 2017 to 2024. The review focused on patient safety events, care gaps, adverse clinical outcomes, and performance monitoring systems. A PRISMA 2020-compliant search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore for peer-reviewed English-language studies published from 2017 through 2024. Dual screening, structured data extraction, risk-of-bias assessment, and narrative synthesis were used. The evidence base showed growing use of machine learning for pressure injuries, sepsis, readmission, mortality, ICU transfer, and continuous monitoring. However, most studies remained retrospective model-development or validation studies, while fewer described deployment within formal quality improvement workflows. Technical progress in predictive modelling for quality improvement is substantial, but evidence of sustained improvement in care processes, safety outcomes, or organisational performance remains limited. Stronger prospective evaluation and clearer integration with improvement methods are needed.

Hospital accreditation requires organizations to demonstrate that clinical, operational, safety, and governance processes are aligned with externally defined standards. This demonstration depends on extensive internal documentation, including current policies, procedure manuals, audit reports, quality dashboards, meeting records, training logs, and department-level evidence files. Accreditation preparation is often conducted through manual document searches across fragmented repositories. This process is slow, resource-intensive, and vulnerable to missed evidence, outdated documents, inconsistent interpretations, and duplication of staff effort. This article proposes a retrieval-augmented generation system designed to support hospital accreditation preparation without replacing human judgment. The system indexes internal accreditation-relevant documents and allows authorized users to ask natural-language questions that receive synthesized, citation-backed responses. The proposed system includes a multi-source ingestion pipeline, a metadata-rich vector database, a permissioned large language model, a citation-grounding layer, and a human review dashboard. Together, these components would support evidence retrieval, gap identification, traceability, and accreditation team collaboration. The system could assist accreditation teams by reducing time spent locating and cross-referencing evidence. It would also be expected to improve the completeness and consistency of preparation materials by grounding responses in authoritative internal sources. A specialized retrieval-augmented generation system could help hospitals move from episodic accreditation preparation toward continuous audit readiness. Its value would depend on document quality, governance safeguards, human review, and rigorous evaluation in real accreditation workflows.