Search found 21 items
This research will support the development and testing of two electronic care plan applications and implementation guide for managing persons with chronic kidney disease and at least one additional chronic condition.
This research will use a clinical decision support tool to identify patients at high risk for type 2 diabetes and text message these patients, offering them in-clinic hemoglobin A1c testing.
This project evaluates the effectiveness, barriers, and cost of a Spanish-language electronic health intervention to treat chronic insomnia in Hispanic patients.
This project will evaluate the effects of a technology-based patient-reported outcomes system on patient management of type 2 diabetes in primary care practices.
This project will integrate the Computerized Adaptive Test for Mental Health into an electronic health record and evaluate the effectiveness of collecting depression symptoms with a patient portal.
The project will develop a patient portal intervention to increase patient activation and promote collaborative decision making for patients with depression.
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 integrated a validated anxiety-specific screening tool in an existing clinical decision support system and tested it with a randomized feasibility pilot that found the tool did not increase detection of anxiety in pediatric primary care.
This research combined the artificial intelligence technology technique Dynamic Logic with natural language processing to create a model to predict risk of death over the next 12 months and found it was better than benchmark statistical and machine learning algorithms.
This project determined care priorities for patients with multiple chronic conditions based on patient needs, preferences, and capabilities and developed a set of recommendations for patients and providers.