USA, MA, Boston
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.
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 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.
This project will develop a clinical decision support tool for the perioperative setting.
Researchers refined and implemented integrated digital healthcare enhancements to a previously developed, interactive, patient-centered discharge toolkit, finding that while patients used the toolkit, there were no significant changes in post-discharge healthcare utilization.
This research assessed the utilization of a “smart” pillbox, a prefilled electronic medication tray that sends electronic alerts and reports to patients, caregivers, and primary care providers for patients discharged from the hospital, finding an increased medication adherence among patients on five or more chronic medications.