Comparison of Two Different Models to Predict Fall Risk in Hospitalized Patients

Fall prevention is a patient safety and economic priority for health care organizations. An automated model within the electronic medical record (EMR) that accurately predicts risk for falling would be valuable for mitigation of inpatient falls. The aim of this study was to validate the reliability of an EMR-based computerized predictive model (ROF Model) for inpatient falls. The hypothesis was that the ROF Model would be similar to the Johns Hopkins Fall Risk Assessment Tool (JHFRAT) in predicting fall events in the inpatient setting at a large academic medical center.

This observational study compared the falls predicted by each model against actual falls over an eight-month period in a single institution. Descriptive statistics were used to compare the distribution of scores and accuracy of fall risk categorization for each model immediately preceding a fall.

For 35,709 inpatient encounters, the total fall rate was 0.92%. Of the 329 patients who fell, 60.8% were high risk by ROF Model (fall rate 1.82%), and 75.4% were high risk by JHFRAT (fall rate 1.39%). The ROF Model had a better specificity than the JHFRAT (69.7% vs. 49.2%) but a similar C-statistic (0.717 vs. 0.702) and a lower sensitivity (60.8% vs. 79.3%).

The performance of the ROF Model was similar to that of the JHFRAT in predicting inpatient falls. This comparison provides evidence to support a transition to a more automated process. Future studies will determine prospectively if implementation of the ROF Model will reduce falls in the inpatient setting.