Acute Care

This research will create clinical decision support artifacts for three patient-centered outcomes research guidelines around advanced diagnostic imaging using standards to allow them to be shareable, interoperable, and scalable; and implement them in different workflows and settings measuring their impact. 

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 research will create the Shared Decision Aid Navigator System to improve the implementation, integration, and scaling of patient shared decision aids into health informatics systems, aiding shared decision making and improving patient-centered outcomes.

This research evaluates an artificial intelligence risk predictive tool called CoMET that uses visual outputs of patient data to serve as an early warning system for patients at risk of cardiac decompensation to allow for earlier intervention and reduction in morbidity and mortality.

This research will refine a current health information exchange platform to improve data exchange for inter-hospital transfers, evaluate its impact, and create a dissemination toolkit so that others may adopt this model.

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 develop and test novel approaches for using electronic health record metadata to characterize and evaluate hospital discharge planning practices, with the long-term goal of providing tools to monitor and strengthen specific behaviors within discharge workflows that optimize post-acute transitions of care.

This research will adapt an existing interoperable clinical decision support (CDS) tool for prevention of venous thromboembolism (VTE) to those with traumatic brain injury, scale it using an implementation strategy called SCALED (Scaling AcceptabLE cDs), and evaluate that approach to ensure that CDS tools are based on the most current evidence.

This research will assess the validity of patient-level computable social factor phenotypes used to predict a patient’s risk of increased healthcare utilization and costs.