Predicting Falls Using Electronic Health Records: A Time Series Approach

Abstract

Background and Aims: Inpatient falls are a major cause of injury within hospitals and are known to delay recovery and increase patient length of stay. The Morse Fall Scale is a commonly utilized tool to assess fall-risk and is broadly implemented, particularly within Veterans Health Administration. Yet, this scale has limited accuracy. A more precise risk assessment tool is urgently needed to identify those at risk and to ensure targeted fall-risk mitigating interventions. The goal of this work was to develop a more accurate fall prediction model within the Veterans Health Administration. Methods The cohort included Veterans admitted to a Veterans Health Administration acute care setting from July 1st, 2020, to June 30th, 2022, with a length of stay between one and seven days. Demographic and clinical data were obtained through VHA electronic health records. Veterans were identified as having a documented fall through clinical progress notes. A transformer model was used to obtain features of this data, which was then used to train a Light Gradient-Boosting Machine for classification and prediction. Area under the precision-recall curve assisted in model tuning, with geometric mean used to define an optimal classification threshold. Results Among 242,844 Veterans assessed, 5,965 (2.5%) experienced a fall during their acute inpatient stay. Employing a transformer model with a Light Gradient-Boosting Machine resulted in an area under the curve of 0.851 and an area under the precision-recall curve of 0.285. With an accuracy of 76.3%, the model resulted in a specificity of 76.2% and a sensitivity of 77.3%. Conclusion The classification model exhibited a performance greater than the Morse Fall Scale and other risk-assessment tools for predicting risk of fall. Incorporating this type of risk model, which can be automatically calculated from existing data, could provide more efficient and accurate method for identifying at-risk patients.