You are the DS owner of a gradient-boosted classifier that predicts whether an incoming patient case should be escalated to urgent review in a healthcare diagnostics workflow. Cases with scores above a 0.40 threshold are routed to a limited specialist queue, while the rest stay in standard review. Leadership wants to deploy because retrospective test performance looked strong, but a 4-week prospective validation raised concerns that too many urgent cases may be missed even though overall discrimination remains high. You need to decide whether the model is good enough to launch as-is.
| Metric | Retrospective Test | Prospective Validation |
|---|---|---|
| Accuracy | 0.91 | 0.90 |
| Precision | 0.72 | 0.74 |
| Recall | 0.81 | 0.63 |
| F1 Score | 0.76 | 0.68 |
| AUC-ROC | 0.89 | 0.87 |
| Log Loss | 0.29 | 0.36 |
| Cases flagged urgent/day | 410 | 335 |
| True urgent cases/day | 365 | 392 |
| Missed urgent cases/day | 69 | 145 |
How would you determine whether this model is ready to deploy, and what additional analysis or changes would you recommend before making a launch decision?