Project RENEW: A Nursing Informatics Led Initiative to Reduce Documentation Burden.

Documentation burden within the United States has grown to unprecedented proportions. Poor usability of the electronic health record has been identified as one of the root causes of documentation burden. By using a structured review process, the nursing informatics team at Northwestern Medicine Healthcare identified opportunities for improved usability.

Fall Risk Prediction Models During the Initial COVID-19 Surge: Could Predictive Analytics Be Used in a Resource-Constrained Environment?

During the first COVID surge, multiple changes in nurse staffing and workflows were made to support care delivery in a resource-constrained environment. We hypothesized that there was a higher rate of inpatient falls during the COVID surge. Furthermore, we predicted that an automated predictive analytic algorithm would perform as well as the Johns Hopkins Fall Risk Assessment.

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

Background: 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.