Using a Mortality Risk Predictor Model for Advance Care Planning via a Transitions of Care Toolkit
Identify high risk mortality inpatients to provide them with a Transition of Care Toolkit to help with advance care planning.
In-Patient/Acute Care Settings
Development of a Maternal Early Warning System Using Machine Learning
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.
A Virtual Operating Room Hub to Facilitate Peri-Operative Communication
Augment OR case review huddles with a virtual operating room hub to facilitate communication across shifts and within shifts in the OR.
Emergency Department Telehealth Program
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™: the implementation of a Duke-specific early warning system for sepsis
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.
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.
Evidence-based Lab Orders
Provides clinicians decision support when ordering analytes to promote high value analyte ordering.
Detection and Treatment of Patient Deterioration
Develop and implement a machine learning risk score for patient deterioration.