High-Sensitivity Cardiac Troponin (hsTn) Decision Aid
Create a chest pain assessment tool which integrates with Epic using FHIR to rule-in or rule-out acute MI.
Projects
Machine-learning algorithm to predict ICU readmissions in the cardiothoracic surgical population
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.
Duke Health Data Pipeline
A foundational, fully-automated data curation tool enabling data liquidity which accelerates quality improvement, learning health, research and innovation projects.
Patient-directed acoustic surveillance for LVAD complications
Patients upload cardiac tones from digital stethoscopes to develop acoustic signatures associated with LVAD complications.
RFID surgical instrument tracking and optimization of the operating room
Implement an RFID system that could be integrated into the OR to measure instrument usage autonomously.
Innovative predictive model to anticipate steroid-induced hyperglycemia and guide insulin regimens
Identify optimal, individualized treatment policies for insulin dosing in the context of systemic glucocorticoids.
Improving primary care access for residents and fellows through electronic visits
Developing an innovative GME Trainee Primary Care eVisit Program within Duke Primary Care.
Early identification of cardiac decompensation and cardiogenic shock
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.
Voices of Duke Health Listening Booth
Inviting healthcare providers, staff, students, trainees, patients, and visitors to have conversations about what is meaningful in your lives, work, and relationships.