Brigham and Women's Hospital
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 use natural language processing and dynamic logic to create a high-fidelity model of risk of death to identify patients with low life expectancy.
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.
This project will implement and evaluate a previously developed, interactive, patient-centered discharge toolkit to improve the transition of care from the inpatient to outpatient settings.
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 study the impact of errors in medical documents on quality of care and develop innovative natural language processing methods to automatically detect errors so that physicians can correct the documents before finalizing them in the electronic health record.
This project convened stakeholder panels to inform the development of an indications-enabled computerized prescriber order entry system.
This is a questionnaire designed to be completed by administrators and clinical staff in an ambulatory setting. The tool includes questions to assess the current state of electronic health records.
This project will refine the Leapfrog Computerized Provider Order Entry (CPOE)/Electronic Health Record (EHR) test – a “flight simulator” for EHRs with CPOE which evaluates the safety performance of EHRs after deployment, with a particular focus on high impact patient safety and medication safety problems.
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.