Enhancing Patient Care and Nursing Practice Through AI-Driven Analysis of Patient Complaints: A Pilot Study
Year: 2026
Type: article
Abstract: Background/Objectives: The integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare has shown promise for analyzing complex data and identifying patterns that can enhance clinical decision-making and patient care. Patient complaints represent a valuable yet underutilized data source for improving healthcare quality and patient safety. This study aimed to investigate whether an AI-driven model could effectively categorize patient complaints to extract actionable insights that support person-centered care and nursing practice to improve care quality. Method: An explorative study design was conducted as a pilot project. A total of 368 patient complaint reports collected by a Patients’ Advisory Committee were analyzed using AI-driven clustering and classification techniques. The complaints were pre-processed through text cleaning, tokenization, and vectorization before being analyzed with various clustering algorithms. Results: The AI-based classification identified four main areas that directly or indirectly influenced patients’ perceptions of care quality: (1) Care and Treatment, (2) Communication, (3) Administrative Management, and (4) Availability. These areas encompassed themes such as diagnostic delays, poor communication, fragmented administrative processes, and limited accessibility. The results highlighted that communication failures, insufficient empathy, and lack of continuity were central to patient dissatisfaction. Automated analysis enabled the identification of recurring issues and systemic weaknesses that traditional manual review methods often overlook. Conclusion: This pilot study demonstrates the feasibility and potential of using an AI-driven model to categorize patient complaints and identify patterns that may provide actionable insights for person-centered care and nursing practice to improve quality of care. The multilabel approach enabled an efficient and systematic analysis of complex complaints and may, following further validation, offer a scalable and consistent basis for understanding patient concerns. In nursing practice, integrating AI into patient feedback systems could support the earlier identification of recurring concerns and contribute to more responsive, evidence-informed, and person-centered quality improvement. However, larger datasets, independent expert validation, and careful consideration of ethical, legal, and governance requirements are needed before the model can be implemented more broadly.
Source: Nursing Reports
Language: English
Subfield: General Health Professions
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W7212250497