OPPORTUNITIES TO IMPROVE THE SYSTEM FOR ASSESSING THE REHABILITATION OF CHILDREN WITH DISABILITIES

Abstract

This study explores opportunities to improve the system for assessing rehabilitation outcomes in children with disabilities through the integration of artificial intelligence (AI) into nursing practice. The research aims to evaluate the effectiveness of AI-assisted rehabilitation assessment within nursing-led rehabilitation processes and to identify key factors influencing functional outcomes. A total of 196 children aged 1–18 years participated in the study. Nurses were trained to assess children’s daily living activities using tablet-based digital forms integrated with a specially designed AI algorithm. The system automatically calculated composite rehabilitation efficiency scores across six functional domains, enabling real-time monitoring and individualized care planning. Psychometric evaluation demonstrated high internal consistency (Cronbach’s alpha = 0.92) and strong concurrent validity with standardized tools such as Pediatric Evaluation of Disability Inventory (PEDI) and WeeFIM (r = 0.85, p < 0.001). Results showed that 78% of children demonstrated functional improvement, particularly in self-care and mobility domains. The findings confirm that AI-enhanced nursing assessment tools can increase objectivity, predictive accuracy, and efficiency in pediatric rehabilitation while supporting human-centered clinical decision-making. The study highlights both the transformative potential and implementation challenges of AI technologies in rehabilitation nursing.