Hospital

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 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.

This research study will pilot test BedsideNotes—a new capability within the inpatient portal to share physicians’ admission and daily notes with parents of children hospitalized on hematology/oncology and neonatal intensive care units.

This research will develop, train, test, and evaluate a machine learning classifier to identify risk for HIV acquisition or transmission among hospitalized patients with substance misuse.

This research will study the effectiveness of a virtual in-home program designed to reduce hospital readmissions among COPD patients post-hospitalization.