The Role of Artificial Intelligence in Healthcare Complaint Management: Implications for Organizational Performance, Patient Experience, and Service Governance

Abstract

The incorporation of artificial intelligence (AI) into healthcare administration is transforming the way patient complaints are received, analyzed, and resolved. This study examines the impact of AI-enabled complaint management systems on operational efficiency, accuracy, patient satisfaction, and managerial decision-making within healthcare organizations. A mixed-methods research design was adopted, combining quantitative analysis of complaint-resolution performance indicators with qualitative insights from patients and healthcare administrators. Data were collected from hospital grievance records, system-generated logs, structured surveys, and semi-structured interviews conducted across selected healthcare institutions that have implemented AI-supported grievance redressal mechanisms. Quantitative data were analyzed using descriptive and inferential statistical techniques, while qualitative data were examined through thematic analysis. The findings indicate that AI-driven tools such as natural language processing, chatbots, and predictive analytics significantly reduce response times, improve classification accuracy, and enhance transparency in grievance handling. However, challenges related to data privacy, algorithmic bias, system integration, and the preservation of human empathy remain critical. The study concludes that a hybrid human AI grievance management model, supported by robust governance and ethical safeguards, offers the most sustainable approach to improving healthcare complaint resolution and patient trust.

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