RFA Innovation Awards
A population health solution for NAFLD with an ultimate goal to optimize health care resources by improving access for high risk patients and minimizing unnecessary referrals.
Improving dermatology access, care delivery and cost via machine learning assisted risk stratification.
A machine learning risk stratification model to improve recognition and management of high risk PE while also reducing hospital utilization for low risk patients.
Identify high risk mortality inpatients to provide them with a Transition of Care Toolkit to help with advance care planning.
A Hospital at Home program in Wake County that would allow patients to be treated for hospital-level conditions in their homes.
Identify obstetric patients at risk for clinical deterioration by using a variety of patient clinical parameters obtained from multiple data modalities and predictive modeling of pregnancy specific, patient related changes.
Augment OR case review huddles with a virtual operating room hub to facilitate communication across shifts and within shifts in the OR.
Development and implementation of a telehealth program at Duke University Hospital ED to reduce left without being seen (LWBS) and initiate patient care faster.
Sepsis Watch is a deep learning model that leverages real-time EHR data to improve detection and treatment of sepsis in the hospital. It was successfully integrated into routine clinical care in November 2018 and has reshaped how local machine learning projects are executed.
