Reducing In-Hospital Mortality
A machine learning model to predict in-hospital mortality, run at the time of admission to the hospital, for all adult patients.
Projects
Evidence-based Lab Orders
Provides clinicians decision support when ordering analytes to promote high value analyte ordering.
Duke Virtual Assistant (Chatbot)
A Virtual Assistant to help patients understand whether they should go to the ER immediately rather than sending the email they are about to send to their physician.
Detection and Treatment of Patient Deterioration
Develop and implement a machine learning risk score for patient deterioration.
Using Machine Learning in Emergency Department Patient Flow
Develop a clinical dashboard built on existing DIHI databases and predictive modeling to assist in providing more efficient dispositions for emergency department patients by predicting clinical trajectory and need for admission.
Machine Learning Models to Recognize Early Clinical Deterioration in DUHS Pediatric Inpatients
Develop machine learning methods that will identify children at risk for clinical deterioration.
Telehealth examination versus clinical examination in the detection of rotator cuff tears
Simulated telehealth evaluation of shoulder pain using patient self-examination will be compared to traditional clinical examination of shoulder pain using MRI as the gold standard to determine accuracy in detecting rotator cuff tears.
Transforming Cancer Care: Bringing PCPs “Back” into Cancer Care through Onco-Primary Care
Implement a blood pressure management clinical pathway to better incorporate PCPs as active members of the cancer care team.
Understanding Underlying Health Care Costs to Deliver Value-Based Care
Develop a data repository with key clinical and administrative data with the goal of understanding the actual cost of treating ductal carcinoma of the breast within the Duke Health System.