In this study we aim to better align fall risk prediction from the Johns Hopkins Fall Risk Assessment Tool (JHFRAT) with additional clinically meaningful measures via a data-driven modelling approach. We conducted a retrospective analysis of 54,209 inpatient admissions from three Johns Hopkins Health System hospitals between March 2022 and October 2023. A total of 20,208 admissions were included as high fall risk encounters, and 13,941 were included as low fall risk encounters. To incorporate clinical knowledge and maintain interpretability, we employed constrained score optimization (CSO) models on JHFRAT assessment data and additional electronic health record (EHR) variables. The model demonstrated significant improvements in predictive performance over the current JHFRAT (CSO AUC-ROC=0.91, JHFRAT AUC-ROC=0.86). The constrained score optimization models performed similarly with and without the EHR variables. Although the benchmark black-box model (XGBoost), improves upon the performance metrics of the knowledge-based constrained logistic regression (AUC-ROC=0.94), the CSO demonstrates more robustness to variations in risk labelling. This evidence-based approach provides a robust foundation for health systems to systematically enhance inpatient fall prevention protocols and patient safety using data-driven optimization techniques, contributing to improved risk assessment and resource allocation in healthcare settings.

翻译：本研究旨在通过数据驱动建模方法，将约翰霍普金斯跌倒风险评估工具（JHFRAT）的跌倒风险预测与更多具有临床意义的指标更好地结合。我们对2022年3月至2023年10月期间约翰霍普金斯医疗系统三家医院的54,209例住院患者进行了回顾性分析。其中20,208例住院被纳入高风险跌倒事件，13,941例被纳入低风险跌倒事件。为融入临床知识并保持可解释性，我们在JHFRAT评估数据及额外的电子健康记录（EHR）变量上应用了约束评分优化（CSO）模型。该模型相较于当前JHFRAT在预测性能上展现出显著提升（CSO AUC-ROC=0.91，JHFRAT AUC-ROC=0.86）。无论是否包含EHR变量，约束评分优化模型均表现出相似的性能。尽管基准黑盒模型（XGBoost）在基于知识的约束逻辑回归（AUC-ROC=0.94）的性能指标上有所改进，但CSO模型对风险标签变化的鲁棒性更强。这种基于证据的方法为医疗系统利用数据驱动优化技术系统性地加强住院患者跌倒预防方案和患者安全提供了坚实基础，有助于改善医疗环境中的风险评估与资源分配。