AI-driven fall risk prediction in inpatients: Development, validation, and comparative evaluation

Chia-Lun Lo; Chia-En Liu; Hsiao-Yun Chang; Chiu-Hsiang Wu

doi:10.18502/npt.v12i2.18337

Chia-Lun Lo Department of Health-Business Administration, Fooyin University, Kaohsiung, Taiwan
Chia-En Liu Department of Nursing, St Joseph’s Hospital, Yunlin, Taiwan
Hsiao-Yun Chang Department of Nursing, Chang Gung University of Science and Technology, Taoyuan, Taiwan
Chiu-Hsiang Wu Department of Nursing, Kaohsiung Municipal Kai-Syuan Psychiatric Hospital, Kaohsiung, Taiwan

DOI: https://doi.org/10.18502/npt.v12i2.18337

Keywords: falls; fall risk assessment comparison; hospitalized patients; supervised learning technology; nursing assessment; decision support system

Abstract

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.