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.
Brigham and Women's Hospital
Shareable, Interoperable Clinical Decision Support for Older Adults: Advancing Fall Assessment and Prevention Patient-Centered Outcomes Research Findings into Diverse Primary Care Practices (ASPIRE)
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 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.
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.
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.
This research studied errors in medical documents created with speech recognition software and developed natural language processing methods to detect such errors.
This project convened stakeholder panels to inform the development of an indications-enabled computerized prescriber order entry system.