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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 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 develop and evaluate the impact of the Prevent Diabetes Mellitus Clinical Decision Support on clinical outcomes, healthcare process measures, and associated costs.
This project will develop and test a personalized motivational text messaging intervention to improve management of diabetes and depression in low-income populations.
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 developed a diabetes data visualization mobile application for adolescents and found that clinical and contextual data provided greater opportunity for self management and problem solving.
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.
This study found increased rates for some screening and preventative services following adoption of federally-certified electronic health records.
This study showed that there is a range of how people communicate with their social network, how this information is communicated, the types of health information communicated, and the rationales for the communication.