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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 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 study examined clinical decision task complexity to guide the design of innovative clinical decision support to for high-level reasoning in complex decision tasks.
This project designed, implemented, and assessed the effectiveness of tailored text messages for changing medication adherence in adults with uncontrolled diabetes and found that improvements were made in perceived benefits and competence.
This study examined the association between electronic health record use and care coordination and clinical care quality for patients with diabetes, and how team working relationships modify these effects.
This project used a mixed-method approach to investigate the validity of using electronic health record data for diabetes performance measures.
This dissertation work informed the creation of three conceptual frameworks related to the design of culturally-informed consumer health information technology.
This project evaluated the use and benefits of an electronic medical record shared between patients with diabetes and their health care providers.