Checklist

Predictive Analytics: Model Validation & Deployment Checklist (Interactive)

An interactive, evidence-oriented checklist to validate, document, approve, and monitor predictive models before and after clinical deployment. Collects readiness answers, artifact locations, thresholds, owners, and monitoring plans to support safe operationalization and governance.

Interactive Tool

Predictive Analytics: Model Validation & Deployment Checklist

Use this checklist to validate, document, and approve predictive models before clinical deployment. Answer each item, provide links or repository locations for artifacts where available, and record owners and approvals to support governance and post-deployment monitoring.

Confirm that datasets, preprocessing steps, random seeds, and environment are versioned and reproducible.
Where are the training/test data, code, and environment captured? Provide links or repository paths.
Include discrimination (AUC, sensitivity, specificity), predictive values, and calibration (plots, Brier score).
List the primary metrics (e.g., AUC, sensitivity at a prespecified threshold) and prespecified acceptance thresholds.
Confirm calibration across risk strata and clinically relevant subgroups.
Link or path to calibration plots or statistics.
Document subgroup performance and any mitigation applied.
List protected or clinically relevant subgroups examined (e.g., age, sex, race, language).
Describe steps taken if disparities were identified (reweighting, recalibration, exclusion, monitoring plans).
Can the model be called within required latency? Are explainability artifacts available for clinicians?
If relevant, specify the latency target for real-time or near-real-time use.
Where are SHAP plots, feature importances, or human-readable explanations stored?
Pilot should define scope, participants, duration, outcomes, and clinician feedback mechanisms.
Describe the pilot population, settings, and measurable criteria indicating success.
Estimated length of the pilot.
Specify metrics, frequency, alert thresholds, and responsible teams.
List the metrics to monitor (performance, input distributions, volume) and thresholds that trigger review or rollback.
How often will monitoring run?
Is there an automated method to detect changes in input distributions or feature importance?
Choose the primary detection method.
Immediate and longer-term actions (investigate, retrain, rollback, alert clinicians).
Confirm PHI handling, access controls, logging, and legal/regulatory approvals as needed.
Where is the privacy impact assessment, security review, or legal sign-off stored?
Name and contact information for the team responsible for the model in production.
Has the appropriate clinical, quality, or safety committee approved deployment?
Name of individual approving deployment.
Role or committee (e.g., Chief Medical Officer, AI Governance Board).
Date of final approval.
Select the most appropriate readiness status.
Anything else reviewers should know.
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