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.
This research will study how a safety-net hospital responds to a pandemic, specifically COVID-19, to identify how information needs are met and how decisions are made and communicated to other individuals internal and external to the institution.
This research will develop and evaluate an artificial intelligence-driven clinical decision support system to detect and manage acute kidney injury in the emergency department.
