Firefly provides a HIPAA-compliant platform utilizing AI to optimize Competency-based Medical Education for residency and fellowship programs. The system automates case logging via EHR integration and delivers actionable, frequent feedback to trainees. This results in tailored educational plans, precise competency analytics, and improved Entrustable Professional Activity (EPA) evaluations for faculty and learners.
Funding
Funding not disclosed
Founders
Product
Problem
Residency and fellowship programs face challenges in providing frequent, actionable feedback to trainees due to the time constraints of faculty and the complexities of tracking competency development. Traditional case logging methods are often cumbersome and may not accurately reflect a trainee's progress or identify areas needing improvement.
Solution
Firefly is a HIPAA-compliant, AI-driven platform designed to streamline competency-based medical education by automating case logging, facilitating frequent feedback, and providing competency analytics. The platform integrates with EHR systems to automate case logging and offers decision support for reporting cases. Firefly provides learning curves and live analytics dashboards, enabling faculty to understand each trainee's current skill levels and tailor educational plans to individual needs. The platform supports Entrustable Professional Activities (EPA) evaluations, providing micro-assessments from the American Board of Surgery (ABS) for both General Surgery and Vascular Surgery residency programs.
Target Audience
The primary users are residency and fellowship program directors, teaching faculty, and medical trainees across various specialties in academic and community hospitals.
Features
- EHR integration for automated case logging and reporting.
- AI-driven analytics dashboards displaying learning curves and competency progression.
- EPA evaluation tools aligned with ABS standards.
- Customizable educational plans tailored to individual learner needs.
- Secure, HIPAA-compliant platform for data management and feedback.
- Automated assignment of entrustability/autonomy scores to trainees.
- Integration of OR schedules for evaluation selection.
- Statistical and deep learning models to apply skill/entrustment scores.