Improving Chronic Disease Mgmt in DPC: Building a Virtual Medical Neighborhood
The Problem As health systems grow larger and demand for specialty care increases, it is becoming increasingly important to embrace efforts to reduce care fragmentation
Projects
Cancer Distress Coach
A randomized control trial to determine whether a distress coach mobile app usage influences symptoms of posttraumatic stress disorder and distress in cancer patients.
A Three-Pronged Approach to Reducing HIV Transmission through Pre-Exposure Prophylaxis (PrEP)
Development of a three-pronged program to decrease HIV transmission and increase awareness in the Duke and Durham community, including the creation of a dedicated Duke PrEP clinic at Duke Medical center, educational promotion, and community outreach.
Using Endocrine Dashboard and Duke PillBox to improve medication adherence to tackle uncontrolled hypertension & glucose control in patients with diabetes
Implementation of a Maestro-integrated pillbox amongst Duke patients with diabetes mellitus (DM) and blood pressure (BP) above goal to improve medication adherence and control of hypertension.
Mobile Complex Care Plans for Children with Medical Complexity
Implementing a mobile complex care plan for provider and patient’s guardians to coordinate care approaches.
Project CALM: Personalized Music for Delirium Prevention
Development and implementation of a personalized music program that targets patients who are at high-risk for developing delirium and seen in the Perioperative Optimization for Senior Health (POSH) clinic at Duke.
The GI Virtual Concierge: A Mobile Platform for Family Engagement
The GI Endoscopy Virtual Concierge leverages the patient flow tracking capability of the electronic health record to enable automated status notifications to patient caregivers.
Highly-Individualized, Dedicated Onsite Care at the Duke Outpatient Clinic
Development of a highly individualized dedicated onsite care program at the Duke Outpatient Clinic.
CALYPSO: Machine Learning for Assessing & Managing Surgical Outcomes
Building statistical models and using machine learning principles to predict the absolute and relative risks of surgical complications for specific patients.