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 cohort 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 to reweight the JHFRAT scoring weights, while preserving its additive structure and clinical thresholds. Recalibration refers to adjusting item weights so that the resulting score can order encounters more consistently by the study's risk labels, and without changing the tool's form factor or deployment workflow. The model demonstrated significant improvements in predictive performance over the current JHFRAT (CSO AUC-ROC=0.91, JHFRAT AUC-ROC=0.86). This performance improvement translates to protecting an additional 35 high-risk patients per week across the Johns Hopkins Health System. 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 labeling. 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，使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对齐。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队列分析。其中，20,208例住院记录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，我们采用约束评分优化（CSO）模型对JHFRAT的评分权重进行重新加权，同时保留其可加性结构和临床阈值。重新校准是指调整项目权重，使得生成的评分能根据研究的风险标签更一致地对事件进行排序，且不改变工具的形式或部署工作流程。该模型在预测性能上相比当前JHFRAT显示出显著提升（CSO AUC-ROC=0.91，JHFRAT AUC-ROC=0.86）。这一性能提升意味着在约翰霍普金斯医疗系统每周可额外保护35名高风险患者。约束评分优化模型在包含与不包含电子健康记录（EHR）变量的情况下表现相似。尽管基准黑盒模型（XGBoost）在性能指标上优于基于知识的约束逻辑回归（AUC-ROC=0.94），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和资源分配。