====== 📈 Learning Curve Analytics ======

===== 📌 Definition =====
> **Learning curve analytics** refers to the systematic study of how performance improves over time with repeated practice of a skill, task, or procedure.

In clinical training, it allows educators and trainees to objectively assess progress, determine when competency is achieved, and identify when additional training is needed.

===== 🧠 Key Concepts =====

<WRAP box>
**Learning curve:** A graph showing how performance metrics (e.g., time, success rate, errors) change with experience or repetition.
</WRAP>

**X-axis =** Number of procedures / cases  
**Y-axis =** Performance indicator (e.g., error rate, time, success rate)

===== 🧪 Types of Learning Curves =====

| **Shape**     | **Interpretation**                         |
|---------------|---------------------------------------------|
| Linear        | Steady improvement over time               |
| Logarithmic   | Rapid early gains, then plateau            |
| S-curve       | Slow start → rapid improvement → plateau   |
| Plateaued     | Performance stabilizes after certain point |

===== 📊 Learning Curve Analysis Methods =====

[[Learning Curve Analysis Methods]]






===== ✅ Applications in Medicine =====
  * Surgical training (e.g., [[laparoscopic_surgery]], [[ube]])
  * Procedural skills (e.g., [[lumbar_puncture]], [[intubation]])
  * Simulation-based learning
  * Credentialing and quality improvement

===== 📌 Example Scenario =====
A neurosurgery trainee is learning [[unilateral_biportal_endoscopy]] (UBE). Learning curve analytics show:
  * CUSUM curve flattens after 25 cases → baseline competence
  * Operative time decreases steadily from 120 to 60 minutes
  * Error rate (e.g., durotomy) falls below 5% after 30 cases

===== ⚠️ Limitations =====
  * Performance metrics may be subjective or hard to define
  * Learning can be non-linear due to complexity variation
  * Outcomes may be affected by supervision, case mix, or fatigue