ai-driven fall risk prediction in inpatients: development, validation, and comparative evaluation

Background & aim: falls among hospitalized patients pose severe consequences, necessitating accurate risk prediction. traditional assessment tools rely on cross-sectional data and lack dynamic analysis, limiting clinical applicability. this study developed an ai-based fall risk prediction model using supervised learning techniques to enhance predictive accuracy and clinical integration.methods & materials: this study was conducted at a medical center in taiwan, excluding pediatric patients due to non-disease-related fall factors. fall cases were obtained from hospital records, and non-fall cases were stratified based on age and gender to create a balanced 1:1 dataset.a total of 52 predictive variables were identified and refined to 39 through expert review. the ai model was compared with morse, stratify, and hii-frm using supervised learning with 10-fold cross-validation. performance was evaluated based on accuracy, sensitivity, and specificity.results: the results demonstrated that the ai-based model significantly outperformed traditional fall risk assessment tools in accuracy, sensitivity, and specificity. more importantly, the model’s superior predictive power allows for real-time risk assessment and seamless integration into clinical decision support systems. this integration can enable timely interventions, optimize patient safety protocols, and ultimately reduce fall-related incidents in hospitalized settings.conclusion: by automating risk assessment, the ai model can alleviate the workload of healthcare professionals, reducing the time required for manual evaluations and minimizing subjective biases in clinical decision-making. this not only enhances operational efficiency but also allows nursing staff to allocate more time to direct patient care. these findings underscore the transformative potential of ai-driven approaches in healthcare, improving patient safety through data-driven.