Real-time NLP warning system for monitoring patient frustration and admission delays

Early detection of patient frustration and delays in admission is imperative for maintaining quality of care, operational efficiency, and patient safety in high-throughput hospital settings. This paper presents the design, implementation, and validation of a real-time natural language processing (NLP)-based warning system that continuously monitors unstructured textual signals and operational admission events. The system ingests live triage notes, patient communications, and admission workflow logs, and performs low-latency inference using a fine-tuned transformer-based clinical language model to identify frustration sentiment, complaint intent, urgency, and delay-related language. Linguistic indicators are fused with real-time operational metrics to generate dynamic risk scores and role-routed actionable alerts for clinical and administrative staff. Validation through historical event replay and live shadow deployment demonstrates a mean end-to-end latency of 18-25 seconds (worst case < 45 seconds), precision of 0.90-0.93 for high-severity frustration detection, and median early-warning lead times of approximately 10-15 minutes prior to formal complaints or critical delay thresholds, while maintaining a controlled alert volume below 6 alerts per 100 admissions. These findings indicate that real-time execution enables earlier intervention and improved operational responsiveness compared with conventional retrospective monitoring approaches.