This study will test the hypothesis that low-income, disadvantaged patients can provide high-quality patient-generated health data and patient-reported outcomes through commercial technologies, and that these data can be used to improve healthcare quality and delivery.
This project will conduct a proof-of-concept and feasibility study of aphasia telerehabilitation for stroke patients with aphasia residing in rural North Carolina.
This project will develop and test a personalized motivational text messaging intervention to improve management of diabetes and depression in low-income populations.
This project will examine health information exchange (HIE) usage patterns, the barriers and facilitators of HIE use, and the impact of HIE use by emergency department clinicians.
This project will design natural language processing algorithms to extract data from free text notes on autism spectrum disorders in electronic health records, and demonstrate the feasibility and usefulness of this approach.
This project will develop and disseminate an innovative communication system to identify and mitigate health risks for young African American women before pregnancy as a means of reducing health disparities in birth outcomes.
This project will evaluate a computerized provider order entry (CPOE)-based function—medication voiding—that can be used to prospectively identify and document medication ordering errors.
This project will evaluate the comparative effectiveness of asynchronous telepsychiatry versus synchronous telepsychiatry in a skilled nursing facility population using a 12-month randomized controlled trial.
This research will explore whether providing clinicians with contextual information at the point of care through the use of clinical decision support can reduce contextual errors, improve patient healthcare outcomes, and reduce misuse and overuse of medical services.
This project will enhance novel algorithms for matching patient health information across data sources, implement them, and evaluate their accuracy.