Adverse Events

This research will implement, evaluate, and disseminate DDInteract, a shared decision making clinical decision support tool that aims to reduce harm from oral anticoagulant drug-drug interactions.

This research will develop and validate machine learning enhanced predictive models improving the allocation of critical care pharmacists to intensive care units to reduce adverse drug events. 

This research will use artificial intelligence and machine learning to create prediction tools integrated into visualization dashboards to guide critical care pharmacists in preventing adverse drug events.

This study will develop, evaluate, and disseminate a multicomponent intervention including a mobile health application and digital navigator training to support safe care transitions for patients with multiple chronic conditions.

This research will use digital health tools leveraging patient-reported outcomes and data from electronic health records to engage individuals with multiple chronic conditions to improve understanding of individualized risk of adverse events during care transitions.

This research will examine the factors affecting cancer patients’ use of an electronic patient safety event reporting system to communicate adverse medication-related events to their care team.