Search found 42 items
This research aims to enhance and implement a clinical decision support tool that will support providers and underinsured patients in making personalized decisions regarding diet goal setting.
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 create patient-centered, interoperable, shareable clinical decision support tools that will support providers and patients in making patient-centered decisions about management of hypertension.
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 adapt and evaluate a mobile health application to improve patient-reported asthma outcomes in New York.
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.
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 and pilot tested an electronic after-visit summary (AVS) that incorporated evidence-based strategies for communicating printed health information to patients and determined best practices for future AVS development.
This project tested the impact of a training module that teaches clinicians how to best communicate with patients in the presence of an electronic health record and found improvements in provider communication skills, but no impact on patient outcomes.