The right learning opportunity, for the right learner, on the right shift.
Precision medical education makes learning proactive, personalized, participatory, and predictive.
Precision medical education integrates longitudinal learner data and analytics to deliver timely, individualized educational interventions. At UF Emergency Medicine, that means identifying what a learner should practice next, what a faculty member should observe today, and when responsibility can safely expand.
The P4 Learning Model
Turn longitudinal learner data into individualized educational action.
Emergency medicine is an ideal environment for this approach: high-frequency clinical encounters, direct observation, rich electronic data, time-sensitive decisions, and repeated opportunities for focused coaching.
Proactive
Identify emerging learning needs before they become persistent gaps.
Personalized
Match coaching, exposure, simulation, and autonomy to the individual learner.
Participatory
Co-create goals and learning plans with residents, fellows, coaches, and faculty.
Predictive
Use trends to anticipate the next educational intervention and readiness decision.
step 1
Collect
Epic, evaluations, simulation
step 2
Interpret
Trends, context, AI synthesis
step 3
Plan
Individualized shift and learning goals
step 4
Coach
Direct observation and mastery practice
step 5
Assess & Adjust
Feedback, progression, next step
The program
Five connected capabilities create one precision learning system.
Each component can launch in phases, while the full model connects assessment, coaching, deliberate practice, and progressive autonomy.
Competency-based progression
Residents advance through stepwise entrustment tiers only when competence is demonstrated on defined Entrustable Professional Activities: Resuscitation leadership, Airway management, Ultrasound-guided access, Procedural sedation, and
Safe discharge
Resident • Coach • CCC • Faculty
Individualized Learning Dashboards
Private, role-based views integrate procedural experience, observed competence, case mix, EPA and milestone trajectories, quality measures, and documentation signals.
AI advises. Faculty decides.
AI-enabled Shift Intelligence
A concise pre-shift brief identifies strengths, high-value opportunities, specific behaviors to observe, and an actionable teaching plan for the shift.
Documentation coaching
Chart-grounded formative feedback helps learners strengthen clinical reasoning, risk communication, reassessment, discharge safety netting, and note quality.
AR and simulation mastery learning
Individual learning plans trigger targeted practice for rare, high-risk, or technically complex skills until mastery is demonstrated.
On-shift Teaching
Turn learner human factors data into an impactful teaching decision in seconds.
The mobile faculty view is designed as a teaching copilot—not a dense analytics screen. It answers the practical questions that matter before and during a busy ED shift.
Before shift – Identify one primary procedural target and one backup opportunity. The AI shift brief summarizes why each target matters and what behavior deserves direct observation.
During shift – Match the right clinical opportunity to the right resident. Counts, recency, adult/pediatric exposure, and competence trends help faculty allocate procedures intentionally.
At the bedside – Observe a specific skill—not simply the entire procedure. Examples include needle-tip visualization, backup-plan communication, sterile technique, or complication recognition.
Immediately after – Debrief and capture high-quality feedback with minimal friction. A structured micro-evaluation updates the longitudinal learner profile and recommends the next step.
Procedural Competence
Move beyond case logs: combine exposure, observation, performance, and safety context.
- ACGME-aligned portfolio
Track all required emergency medicine procedure categories with configurable targets and definitions.02 - Counts plus observed competence
Pair volume with direct observation ratings, entrustment, narrative feedback, and performance trends.03 - Context that matters“
Distinguish adult and pediatric, live and simulated, routine and complex, recent and remote experience.04 - Learning-oriented safety review
Interpret complications and near misses by complexity, recognition, response, and repeated pattern—not as punitive counts.05 - Actionable AI synthesis
Highlight strengths, growth areas, plateauing skills, exposure gaps, and the most useful next coaching action.
program-level view
See the cohort without losing the individual.
Education leadership can identify class-wide exposure gaps, variation in direct observation, procedure-specific coaching themes, residents who need support, and emerging readiness for greater autonomy.
- Resident procedure portfolio and ACGME completion
- Cohort heat map across required procedures
- Readiness distribution and coaching priorities
- Complication review with longitudinal context
- Program-level curriculum and scheduling signals
data to action
A secure architecture connects learner evidence to educational action.
INPUTS
Longitudinal learner data
Epic clinical and procedural data
Learner evaluation ratings and narratives
Simulation and AR performance
Resident-entered goals and reflections
INTELLIGENCE
Analytics and AI synthesis
Trend and gap detection
Narrative theme extraction
Context-aware readiness estimates
Bias, drift, and data-quality monitoring
ACTIONS
Precision interventions
On-shift teaching plans
Individualized coaching
Mastery learning assignments
Human-led entrustment decisions
benefits
Better learning decisions create value for learners, teachers, patients, and the College.
For learners
Clearer paths to mastery
Earlier identification of gaps and strengths
More specific, timely coaching
Intentional exposure to needed cases and procedures
Safer, evidence-based progression of autonomy
For educators
More precise teaching with less friction
Pre-shift priorities in seconds
Focused direct observation prompts
AI-assisted synthesis of longitudinal feedback
More defensible CCC and entrustment decisions
For patients and the system
Learning aligned with safe care
Stronger procedural preparation and rescue planning
Improved documentation and discharge communication
Targeted quality improvement with attribution safeguards
More consistent readiness for independent practice
For the College of Medicine
A scalable innovation platform
Recruitment and educational differentiation
Cross-department infrastructure
Research, scholarship, and grant opportunities
National leadership in precision education
BUILD WITH US
Help shape the future of medical education
The program welcomes engagement from educators, informaticians, data scientists, simulation experts, quality leaders, residents, fellows, and clinical departments.
