Context

You are the lead ML scientist at MedSure Diagnostics, a healthcare SaaS vendor that provides decision-support software to hospital networks in the US and EU. Your product ingests routine lab panels (CBC, CMP), vitals, and EHR-derived features to predict sepsis risk within 6 hours and recommend whether a patient should be escalated to a rapid-response team.

The system is being prepared for deployment in a GxP environment (customers treat it as software impacting patient safety and regulated quality systems). The model is a gradient-boosted tree classifier trained on retrospective data from 18 hospitals (approx. 2.4M ED encounters over 24 months). Sepsis prevalence is ~1.2%. Labels are derived from a clinical adjudication pipeline finalized 7 days post-encounter. The model outputs a probability score and triggers an alert when the score exceeds a threshold.

Current Performance (Locked Model Candidate)

Validation was run on a temporally held-out set (last 3 months) from 6 hospitals not used in training. The clinical team is concerned because, despite strong discrimination, they observed “too many missed cases” and inconsistent alert rates across sites.

Site-to-site slice (same global threshold=0.20)

The Problem

MedSure must produce a GxP-grade validation package suitable for customer audits and internal quality review before go-live. The VP of Clinical Safety is asking for a concrete plan that demonstrates the model is fit for intended use, robust to site differences, and controlled throughout its lifecycle.

Your Task

Propose a complete validation strategy appropriate for a GxP environment. Your answer should cover:

1. Intended use & acceptance criteria: Define what “good enough” means (metrics, thresholds, and clinical/business constraints). Specify at least 3 acceptance criteria and how they map to patient safety.
2. Validation design: Describe how you would structure validation datasets and protocols (temporal validation, external validation, subgroup/slice validation, stress tests). Explain how you would avoid leakage given 7-day label finalization.
3. Metric selection & interpretation: Justify which metrics you would prioritize (e.g., recall vs precision, calibration, decision-curve / net benefit) and why AUC alone is insufficient.
4. Thresholding and calibration: Recommend an approach to threshold selection under operational constraints (rapid-response capacity) and how you would calibrate probabilities (and validate calibration) in a regulated setting.
5. Documentation & traceability: Outline what artifacts you would produce for audit readiness (data lineage, model card, validation report, change control, versioning, reproducibility).
6. Ongoing performance monitoring: Define post-deployment monitoring, drift detection, periodic review, and a GxP-compliant change management / revalidation plan.

Constraints

• Clinical capacity: Each hospital can handle at most ~60 alerts/day without degrading response quality.
• False negatives are high severity: Missing sepsis can lead to ICU transfer or death; clinical leadership wants sensitivity prioritized.
• False positives cause harm too: Alert fatigue increases clinician override rates and can reduce trust.
• Data heterogeneity: Lab ordering patterns and EHR coding differ by site; EU site has different reference ranges and units for some labs.
• Regulatory/quality: The model must be “locked” at release; any material change requires controlled evaluation and potentially revalidation.