Brigham and Women's Hospital

This research iteratively designed and developed 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.

Researchers created a drug allergy module that detects inconsistencies in allergy information within the electronic health record and uses a dynamic picklist that puts answers in order of how important they are based on the allergen input.

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.