This research will refine a current health information exchange platform to improve data exchange for inter-hospital transfers, evaluate its impact, and create a dissemination toolkit so that others may adopt this model.
This study will develop, evaluate, and disseminate a multicomponent intervention including a mobile health application and digital navigator training to support safe care transitions for patients with multiple chronic conditions.
This research will use digital health tools leveraging patient-reported outcomes and data from electronic health records to engage individuals with multiple chronic conditions to improve understanding of individualized risk of adverse events during care transitions.
The purpose of this research is to develop a standards-based, interoperable, and publicly available clinical decision support resource to aid primary care practices in instituting routine fall risk assessment and prevention care plans.
This project aims to refine and develop methods to address missing electronic health record data to improve data quality and research validity.
Researchers created a drug allergy module that detects inconsistencies in allergy information within the electronic health record and uses a dynamic picklist that puts answers in order of how important they are based on the allergen input.
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 will develop and disseminate an innovative communication system to identify and mitigate health risks for young African American women before pregnancy as a means of reducing health disparities in birth outcomes.
The research team developed and evaluated a natural language processing allergy module that was used to study different types of allergies in an electronic health record.
