This research developed strategies to optimize CancelRx implementation and measured its impact on dispensing errors and patient outcomes.
This research will study how a safety-net hospital responds to a pandemic, specifically COVID-19, to identify how information needs are met and how decisions are made and communicated to other individuals internal and external to the institution.
This research will test and validate a machine learning predictive analytic intervention to optimize the timely and appropriate use of interpreters for hospitalized patients with language barriers and complex care needs.
This research aims to examine changes in nursing clinical documentation from 2019 through the COVID-19 pandemic timeline to observe the impact of electronic health record burden on patient care.
This research will use deep learning models to move a reactive full capacity protocol (FCP) for emergency department (ED) overcrowding interventions into a proactive FCP by predicting patient flow measures so that interventions may be activated to avoid ED overcrowding.
