Using Electronic Health Records to Enhance Predictions of Fall Risk in Inpatient Settings

Background: Falls are the most common adverse events of hospitalized adults. Traditional validated assessment tools have limited ability to accurately detect patients at high risk for falls. The researchers aim to develop an automated comprehensive risk score to enhance the identification of patients at high risk for falls and examine its effectiveness. Methods: The enhanced fall algorithm (EFA) was developed from 171,515 hospitalizations and 2,659 falls, in an academic medical center, using hierarchical logistic regression. Routine nursing assessments, labs, medications, demographics, and patients’ location during their hospitalization were gathered from the electronic health record (EHR). Results: The fall rate was 2.8 per 1,000 patient-days. Morse fall score was the strongest predictor of falls (odds ratio = 7.16, 95% confidence interval = 6.48–7.91), with a model discrimination c-statistic of 0.687. By adding patient demographics, chronic conditions, lab values, and medications, and controlling for patient clustering within units, predication was enhanced and model discrimination increased to 0.805. By applying the enhanced model, we observed redistribution of patient by risk: low-risk group increased from 52.8% to 66.5%, and the high-risk group decreased from 28.0% to 16.2%, with an increase of fall detection from 3.1% to 5.1%. Conclusion: The EFA redistributes and identifies patients at high risk more accurately than the Morse score alone, decreasing the population of high-risk patients without increasing the rate of falls over time. The EFA requires no addition data collection and automatically updates the patient's fall risk based on new inputs in the EHR.