This research will enhance, refine, and evaluate an artificial intelligence-supported tool that collects patient-reported outcomes to inform care and improve glycemic control for patients with poorly controlled diabetes.
This research developed, tested, and evaluated visual presentations of patient-reported outcome (PRO) scores to facilitate the usability and understanding of PRO scores by patients and providers to improve shared decision making.
This research evaluated the iMatter text-messaging patient-reported outcome tool versus usual care for reducing hemoglobin A1c (HbA1c) and supporting self-care in people with Type 2 Diabetes, finding no significant difference in HbA1c reduction but an improvement in smoking status for the intervention group.
This research evaluated the implementation and effectiveness of a clinical decision support tool designed to support the delivery of recommended care to hospitalized patients with heart failure, regardless of the reason for hospitalization.
This project pilot tested and evaluated a smoking cessation clinical decision support system, finding it was feasible to implement, easy to use and helpful for patient care, and led to an increase in patient quit attempts.
This project implemented a personal health record and population-level dashboard to assist lay health workers in a church-based setting with the goal of improving blood pressure control, and succeeded in increasing the number of participant physician visits and total fruit and vegetable consumption.
