Construction of a hemodialysis quality assessment and feedback mechanism based on nursing behavior data

Resumo

Introduction The quality of nursing behaviors during hemodialysis procedures is pivotal for ensuring patient safety, treatment efficacy, and the optimization of clinical outcomes. This study introduces an innovative and data-driven framework for hemodialysis quality assessment and feedback enhancement, leveraging nursing behavior data to address existing limitations in traditional evaluation methods, which often lack contextual sensitivity, temporal continuity, and dynamic adaptability. Methods The proposed methodology integrates a Nursing Behavior-Informed Hemodialysis Quality Assessment Model (NB-HQAM) and an Adaptive Feedback Mechanism (AFM) to systematically evaluate nursing behavior patterns and generate structured feedback recommendations. The NB-HQAM captures temporal dependencies, contextual interactions, and behavioral sequences to model the intricate relationships between nursing actions and quality metrics, while the AFM delivers dynamic, context-aware, and actionable feedback tailored to individual nursing staff through iterative learning and adaptive reinforcement. The framework employs rigorous mathematical formalization, graph-based representations of nursing workflows, and advanced optimization techniques to ensure both reliability and interpretability. Results and discussion Experimental results demonstrate significant improvements in predictive accuracy, consistency, and robustness under an offline evaluation setting, supporting the effectiveness of the proposed framework for hemodialysis-related quality assessment and adverse event risk prediction. The AFM provides a structured mechanism for translating model outputs into context-aware nursing recommendations, while its effectiveness in changing nursing behavior or improving patient outcomes has not been established in the present retrospective evaluation. Prospective clinical studies involving practicing nursing staff are required to assess the actionability, acceptance, and clinical impact of the generated feedback.