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.

When nursing takes ownership of financial outcomes: achieving exceptional financial performance through leadership, strategy, and execution.

With nurses and unlicensed supportive personnel composing the greatest percentage of the workforce at any hospital, it is not surprising nursing leadership plays an increasing role in the attainment of financial goals. The nursing leadership team at one academic medical center reduced costs by more than $10 million over 4 years while outperforming national benchmarks on nurse-sensitive quality indicators. The most critical success factor in attaining exceptional financial performance is a personal and collective accountability to achieving outcomes.

Utility of commonly captured data from an EHR to identify hospitalized patients at risk for clinical deterioration.

Rapid Response Teams (RRTs) respond to critically ill patients in the hospital. Activation of RRTs is highly subjective and misses a proportion of at-risk patients. We created an automated scoring system for non-ICU inpatients based on readily available electronic vital signs data, age, and body mass index.