Predicting falls using electronic health records: a time series approach.

Abstract

Objective: To develop a more accurate fall prediction model within the Veterans Health Administration.

Materials and methods: The cohort included Veterans admitted to a Veterans Health Administration acute care setting from July 1, 2020, to June 30, 2022, with a length of stay between 1 and 7 days. Demographic and clinical data were obtained through 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, 5965 (2.5%) were documented as having a fall during their clinical stay. Employing a transformer model with a Light Gradient-Boosting Machine resulted in an area under the curve of .851 and an area under the precision-recall curve of .285. With an accuracy of 76.3%, the model resulted in a specificity of 76.2% and a sensitivity of 77.3%.

Discussion: Prior evaluations have highlighted limitations of the Morse Fall Scale (MFS) in accurately assessing fall risk. Developing a time series classification model using existing electronic health record data, our model outperformed traditional MFS-based evaluations and other fall-risk models. Future work is necessary to address limitations, including class imbalance and the need for prospective validation.

Conclusion: An improvement over the MFS, this model, automatically calculated from existing data, can provide a more efficient and accurate means for identifying patients at risk of fall.