Introduction
Clinical decision making in hospital environment is a multifaceted task, which often involves aggregating data across multiple sources, including the medical record and heterogeneous systems such as imaging, medication systems among others. The RADS2 team has skills in artificial intelligence and machine learning, along with the deployment expertise that is necessary to transform information to knowledge and provide insights at the point of care. Some of our work includes the development of a sepsis prediction algorithm to inform earlier detection and initiation of treatment and the deployment of real-time early warning systems to identify patients at risk of deterioration. In addition to these efforts, the team is actively developing many other clinical decision support tools for identification of patients at risk of HIV Prep discontinuation, and the automated detection of intimate partner violence.
