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The rapid expansion of unstructured narrative data within patient safety event (PSE) reporting systems presents both a valuable source of safety intelligence and a major analytical challenge for healthcare organizations. Traditional manual review processes are labor-intensive, subjective, and incapable of scaling to the vast volumes of incident reports generated across modern health systems. Artificial intelligence techniques, particularly natural language processing and machine learning, provide scalable approaches for extracting meaningful insights from these narratives. This narrative review synthesizes advances in AI-enabled PSE analytics across three interconnected domains: automated narrative mining, data-driven taxonomy development, and integration within learning health systems that transform safety data into continuous improvement cycles. Evidence indicates that AI methods can improve event classification, accelerate detection of emerging safety signals, and reduce the analytical burden on safety teams. However, challenges remain regarding model generalisability, interpretability, and governance. AI-driven narrative analytics is emerging as a foundational component of next-generation safety intelligence infrastructures.

Clinical documentation is central to continuity of care, coding, billing, compliance, and quality measurement, yet it remains a major source of administrative burden for physicians. Artificial intelligence, especially natural language processing, has been proposed as a way to improve documentation quality, coding accuracy, billing support, and clinical workflow efficiency. This systematic review synthesised evidence from 2017 to 2023 on natural language processing methods applied to clinical documentation improvement. The review focused on automated clinical coding, note quality, billing support, computer-assisted physician documentation, and physician workflow efficiency. A PRISMA 2020-compliant search strategy was applied to PubMed, Scopus, IEEE Xplore, and Web of Science for publications from 1 January 2017 to 31 December 2023. Screening was performed in duplicate, and eligible studies were narratively synthesised by documentation domain, model type, deployment maturity, and evaluation approach. The evidence base expanded rapidly during the review period, especially in automated coding and clinical note generation. Several studies reported technically promising systems for ICD coding, documentation summarisation, and speech-derived notes, whereas billing outcomes and sustained workflow effects were less commonly evaluated. Natural language processing for clinical documentation improvement appears most mature for automated coding and note analysis. Evidence for billing support and physician workflow transformation remains less developed, particularly in prospective clinical environments.

Escalated patient complaints signal breakdowns in communication, care coordination, service recovery, and trust. These events can create safety, reputational, financial, and legal risk, yet they often emerge from data already generated before escalation occurs. Current complaint management workflows are largely reactive. Health systems often rely on manual triage, retrospective reports, and delayed recognition of risk after a patient’s dissatisfaction has already intensified. This article proposes a multimodal neural network for predicting whether an initial patient complaint is likely to escalate into a formal grievance, regulatory concern, legal threat, or other high-risk pathway. The model is conceptual and intended to support early intervention rather than replace human judgment. The proposed architecture integrates call center transcript embeddings, portal message language features, service recovery note representations, structured encounter metadata, and prior satisfaction survey responses. These inputs are fused into a dynamic escalation risk representation that could be updated as new interactions occur. Conceptually, the model would identify high-risk patterns such as repeated calls with increasing anger, portal messages expressing urgency, unresolved service recovery documentation, and prior low satisfaction scores. Such early detection could help patient relations teams prioritize timely outreach and targeted resolution. A multimodal neural network for complaint escalation prediction could shift patient relations from retrospective damage control toward proactive experience improvement. Its value would depend on careful validation, interpretability, privacy protection, and responsible integration into service recovery workflows.