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This project will redesign approaches for collecting and using allergy information with the goal of improving healthcare quality and safety, including completeness and accuracy of allergy data.
This project will implement and evaluate a “smart” pillbox given to patients in order to understand its ability to minimize discrepancies in prescribed regimens and to improve patients’ medication adherence after hospital discharge.
This project will use natural language processing and dynamic logic to create a high-fidelity model of risk of death to identify patients with low life expectancy.
This project convened stakeholder panels to inform the development of an indications-enabled computerized prescriber order entry system.
This project built an automated intervention that recognized critical imaging results that require additional testing and populated a discharge summary with recommendations, resulting in improved patient followup.
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.
The overall goal of this study was to develop and assess a natural language processing application to facilitate medication reconciliation at the point of care.
The Center for Education and Research on Therapeutics (CERTs) Program focused on translating health information technology research into improved clinical practices related to medication safety, effectiveness, and cost.
This study evaluated whether collecting risk factors to generate an electronic personalized health risk appraisal for coronary heart disease, diabetes, and breast and colorectal cancer was associated with improved patient-provider communication, risk assessment, and breast cancer screening plans in the subsequent year.
The goal of this study was to develop and evaluate electronic health record-based tools to improve diagnosis and treatment of overweight and obesity in primary care.