Acute Care

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 will examine the acceptability and usability of a shared decision making tool that incorporates risk information and patient and caregiver preferences for Hospital at Home, an acute care alternative to traditional inpatient hospitalization.

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 and validate a COVID-19 emergency department (ED) return screening tool that will provide ED clinicians a risk assessment to guide admissions and discharges to reduce morbidity and mortality associated with acute respiratory syndrome coronavirus 2 infection.

This research aims to design and develop novel wearable technologies that can improve care coordination during prehospital encounters and, ultimately, improve patient outcomes and achieve patient-centered care delivery and coordination.

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 compare the use of predictive modeling versus traditional questionnaires to identify those with unmet social needs, use the superior method to inform the development of a clinical decision support tool, and evaluate the tool’s impact on referrals to social providers.

This research will study the implementation of Telehealth Education for Asthma Connecting Hospital and Home (TEACHH), a novel intervention designed to provide an effective asthma educational platform appropriate for all health literacy levels. The intervention includes initial instruction in the hospital and reinforcement at home using virtual visits to reduce barriers to self-management support for children who are hospitalized due to asthma.