RFA Innovation Awards
Develop predictive models to identify rapidly and accurately CTS patients at high-risk (and high-cost) for postoperative clinical deterioration necessitating readmission from the SDU back to the ICU.
Patients upload cardiac tones from digital stethoscopes to develop acoustic signatures associated with LVAD complications.
Implement an RFID system that could be integrated into the OR to measure instrument usage autonomously.
Identify optimal, individualized treatment policies for insulin dosing in the context of systemic glucocorticoids.
Developing an innovative GME Trainee Primary Care eVisit Program within Duke Primary Care.
Duke Heart and DIHI are collaborating to implement the phenotype dashboard and model to support real-time identification of patients experiencing or at high risk of experiencing cardiac decompensation.
Inviting healthcare providers, staff, students, trainees, patients, and visitors to have conversations about what is meaningful in your lives, work, and relationships.
To develop and validate a computer algorithm to correctly estimate left ventricular ejection fraction (LVEF) as an initial step toward a fully automated echocardiogram evaluation.
Systematically engaging families and helps ICU clinicians deliver needs-targeted palliative care.
