An Ai-Driven Legal and Clinical Decision Support Framework for Mediating Nurse–Patient Aggression in Nigerian Emergency Care Settings

Vollständiger Abstract

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Aggression and conflict between nurses, patients, and patients' relatives have become increasingly significant challenges within Nigerian emergency departments, creating negative consequences for healthcare delivery, workplace safety, patient satisfaction, and institutional accountability. These conflicts are often triggered by prolonged waiting times, overcrowding, communication breakdowns, emotional distress, and resource constraints, which can escalate into verbal abuse, threats, physical confrontations, and legal disputes. Despite the growing prevalence of such incidents, there remains a limited availability of intelligent systems capable of supporting early conflict identification and mediation within healthcare environments. This study therefore aimed to develop a Natural Language Processing (NLP)-based framework for mediating nurse–patient aggression disputes in Nigerian emergency departments by identifying aggression patterns and supporting proactive conflict resolution. The study employed a machine learning and NLP-based methodology using a study population comprising 2,000 emergency department incident records. The dataset contained structured variables including waiting time, crowding level, staff availability, communication clarity, pain level, relative emotional state, and nurse response time, alongside unstructured textual incident narratives. Descriptive statistics and exploratory data analysis (EDA) were conducted to examine aggression patterns and identify factors influencing dispute escalation. NLP techniques based on Term Frequency–Inverse Document Frequency (TF-IDF) were applied to transform textual incident reports into machine-readable representations, while a Random Forest classifier was used to predict aggression severity levels. The analytical results revealed that medium aggression incidents constituted the largest proportion of cases (61.85%), followed by high aggression incidents (28.90%) and low aggression incidents (9.25%). The findings further indicated that prolonged waiting times, overcrowding, communication challenges, and staffing conditions were strongly associated with increased aggression severity. The developed model demonstrated the potential of NLP-driven conflict analysis in detecting aggression indicators from emergency department narratives and operational data. From a nursing perspective, the framework provides a proactive mechanism for improving workplace safety, reducing emotional stress among nurses, and enhancing the quality of patient-provider interactions. From a mediation law perspective, the system supports early dispute resolution by identifyingconflict risks before they escalate into formal complaints, workplace violence, negligence claims, or litigation. The study concludes that integrating NLP and machine learning into emergency healthcare environments can significantly strengthen conflict management and institutional accountability. It is therefore recommended that healthcare institutions adopt intelligent mediation-support systems, strengthen communication protocols, improve patient flow management, and incorporate alternative dispute resolution mechanisms into hospital conflict management policies to promote safer and more collaborative healthcare environments.