Natural Language Processing for Employee Relations Case Management: Analyzing Workplace Complaints, Grievances, and Investigation Narratives in Healthcare Organizations

Abstract

The increasing volume of employee complaints, grievances, and workplace investigations in healthcare organizations has created challenges for efficient and consistent case management. Natural Language Processing (NLP), a branch of artificial intelligence, offers opportunities to automate the analysis of unstructured textual data and improve decision-making in employee relations. This study examined healthcare professionals' perceptions of NLP adoption in employee relations case management, focusing on complaint analysis, grievance classification, investigation support, risk detection, decision support, organizational readiness, and ethical considerations.

A quantitative cross-sectional design was employed using a structured questionnaire completed by 380 healthcare professionals from hospitals, clinics, long-term care facilities, and public health organizations. Data were analyzed using descriptive statistics, Cronbach's alpha, chi-square tests, and independent-samples t-tests.

The findings indicate strong overall support for NLP in employee relations. Participants reported positive perceptions regarding complaint analysis, investigation efficiency, decision support, and early workplace risk identification. Decision Support and Case Management Effectiveness (M = 4.02) and Employee Relations and Organizational Outcomes (M = 3.98) received the highest ratings, while Organizational and Technological Readiness (M = 3.45) emerged as the primary implementation challenge. Ethical concerns, including fairness, privacy, and governance (M = 4.15), were also highly emphasized. Statistical analysis showed no significant relationship between organizational size and NLP adoption, while gender demonstrated a statistically significant but practically small difference in perceived benefits.

The study concludes that NLP can enhance the efficiency, consistency, and quality of employee relations case management in healthcare. However, successful implementation requires robust governance, technological readiness, ethical safeguards, and continuous human oversight to ensure responsible AI-assisted decision-making.