USA, MA, Boston
This project aims to refine and develop methods to address missing electronic health record data to improve data quality and research validity.
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.
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.
This project will develop a clinical decision support tool for the perioperative setting.
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.