AI-Powered Predictive Analytics in Physiotherapy: Enhancing Patient Outcomes and Treatment Efficiency in Rehabilitation Clinics

Abstract

Artificial intelligence (AI) is increasingly being integrated into allied health disciplines to enhance clinical decision-making, predict patient outcomes, and optimize treatment workflows. This study examines the adoption of AI-powered predictive analytics in physiotherapy rehabilitation clinics in Pakistan, focusing on its effects on patient recovery outcomes, treatment efficiency, and staff workflow. Using a cross-sectional survey of 400 physiotherapists and allied rehabilitation staff, data were analyzed through partial least squares structural equation modeling (PLS-SEM). Results indicate that AI adoption is positively associated with improved patient outcomes (β = 0.52, p < 0.001), reduced treatment planning time (β = 0.48, p < 0.001), and enhanced staff workflow efficiency (β = 0.45, p < 0.001). Ethical considerations, including patient consent and professional autonomy, moderated these effects (β = -0.17, p = 0.003). The findings highlight the potential of AI to support evidence-based physiotherapy practices in low-resource settings, while emphasizing the importance of ethical governance and staff training. This study provides empirical evidence and practical insights for policymakers, clinic administrators, and allied health professionals aiming to leverage AI in rehabilitation services.