Johns Hopkins University

This research will develop and evaluate an artificial intelligence-driven clinical decision support system to detect and manage acute kidney injury in the emergency department.

This research developed strategies to optimize CancelRx implementation and measured its impact on dispensing errors and patient outcomes.

This research study addressed the overuse of blood cultures to diagnose sepsis by developing an electronic health record-embedded clinical decision support tool that draws upon the strengths of analytical and naturalistic decision making.

This project developed and evaluated a clinical decision support system that effectively communicated genomic data to clinicians to improve healthcare decision making.

This project developed and pilot-tested a novel, outcomes-based emergency department triage tool and found that risk stratification and waiting times were improved for some patients.

This research used a human-centered design approach to understand the cognitive work of clinical teams involved in pediatric trauma care transitions that informed the development of two prototypes for digital healthcare solutions that support clinicians caring for pediatric trauma patients.

This project developed and validated a 30-day readmission risk prediction model that incorporated data from a health information exchange.