Patient Mobility and Falls Risk Assessment Tools

This project utilizes data-driven optimization and machine learning methods to improve the quality of inpatient fall risk assessment and decision-making. Our team has developed novel optimization models to increase the sensitivity and specificity of identifying patients at high risk for falls, while maintaining interpretable model structure using the Johns Hopkins Fall Risk Assessment Tool (JHFRAT) and additional electronic health record (EHR) data. The new models account for rarity of falls and uncertainty in true underlying risk due to risk-obscuring interventions. We are continuing to work on data-driven methods for understanding the relationships between EHR measures of risk, fall prevention interventions, and fall incidence in order to provide automated clinical decision support.