Search

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.