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We learn your platform, risk model types, EHR data sources, clinical use cases, and regulatory obligations in 30 minutes.
AI Predictive Analytics deserves validated accuracy
Testiva delivers specialist QA for AI Predictive Analytics, risk score accuracy, bias testing, drift monitoring, and FDA AI/ML compliance.
100+
Predictive Models Evaluated
3x
Faster Release Cycles
40%
Lower Rework Costs
Why it matters
What happens when AI Predictive Analytics
What happens when AI Predictive Analytics
platforms aren’t tested properly
Risk scores not validated against real clinical outcomes
A readmission model with strong AUC metrics can still fail calibration against actual patient outcomes.
Systematic bias across patient demographics goes undetected
A model that underestimates risk for specific groups directs interventions away from patients who need them most.
Model drift silently degrades risk score accuracy post-deploy
Patient population shifts and EHR pipeline changes degrade predictions invisibly until clinical outcomes shift.
Poor EHR data quality corrupts model inputs silently
Missing lab values, duplicate records, and stale medication lists flow into risk calculations undetected.
How Testiva protects your AI Predictive Analytics platform
- Clinical QA engineers who understand population health and risk modelling — We evaluate model outputs against clinical outcome data and population health standards, not just AUC and RMSE.
- Structured bias testing across patient demographics — We measure differential risk scores across age, gender, ethnicity, and comorbidity quantifying the equity gap.
- Clinical ground truth validation, not just model metrics — We validate risk scores against real clinical outcomes actual readmissions and deterioration events.
- Post-deploy drift monitoring configured from day one — We establish validated baselines and configure continuous monitoring so accuracy degradation is detected early.
- FDA AI/ML and MHRA AIaMD validation evidence from day one — Regulatory traceability documentation produced throughout the engagement ready before a reviewer asks for it.
What we test
Core components of an AI Predictive Analytics platform we cover
Every layer that affects risk score accuracy, population health equity, and post-deploy reliability is validated against clinical outcome data and regulatory standards.
Risk Score Accuracy & Calibration
Risk scores validated against real clinical outcome data.
Clinical AI Bias & Health Equity
Risk bias measured across patient demographics.
EHR Data Quality & Input Validation
Model behaviour tested with missing and malformed EHR data.
Post-Deploy Drift Monitoring
Risk accuracy tracked against validated baselines.
Risk Alert & Trigger Testing
Alert thresholds verified at clinical risk levels.
Model Regression & Update Testing
Accuracy re-evaluated on every model retrain.
PHI & Analytics Pipeline Security
EHR extraction and inference pipelines audited for HIPAA.
Regulatory & Compliance Validation
FDA and MHRA evidence mapped throughout the engagement.
Performance & Scalability Testing
Inference latency verified at population-scale load.
HOW IT WORKS
Up and running in 4 simple steps
From first contact to your first test report a process designed to be fast, transparent and low-friction.
QA Audit & Plan
We audit your risk score accuracy baseline, bias testing coverage, and build a clinical validation strategy with outcome-validated ground truth datasets.
Test Execution
Risk score accuracy evaluation, demographic bias analysis, EHR input validation, alert logic testing, and regulatory mapping every finding rated by clinical severity.
Report & Iterate
Clinical accuracy report with risk score calibration metrics, bias analysis, regulatory traceability, drift monitoring setup, and prioritised remediation roadmap.
What People Say
Testiva is a great team to work with. I’ve hired them multiple times and recommended them to others, all impressed by their thorough work. Highly recommended for QA.
Testiva team is highly skilled and extremely thorough. I trust them for accurate and timely delivery. They are a reliable resource for any project.
Testiva team delivered outstanding quality with great professionalism. Communication was excellent and delivery met expectations. Highly recommended.
Excellent team worked well with minimal supervision and did a great job. Their work helped us improve the robustness of the platform.
❮
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AI Predictive Analytics Testing Packages
| Feature | Analytics Check | Risk Guard | Analytics Shield | Apex Analytics Suite |
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| CORE ANALYTICS TESTING | ||||
| Risk score accuracy & calibration testing |  |
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| EHR data quality & input validation testing | |
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| Risk alert & trigger logic testing | |
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| Clinical outcome validation testing |  |
5K patients | 10K patients | Unlimited |
| Multi-model & multi-condition testing | |
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Setup only | Full build |
| BIAS, EQUITY & SAFETY | ||||
| Clinical ground truth outcome dataset build | |
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| Demographic bias & health equity testing | |
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| Subgroup performance disparity analysis | |
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| Model regression on retrain & updates | |
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| COMPLIANCE, MONITORING & SECURITY | ||||
| HIPAA & PHI analytics pipeline audit | |
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| FDA AI/ML SaMD validation evidence | |
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| MHRA AIaMD & NHS AI governance documentation | |
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| Post-deploy drift monitoring setup | |
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| Continuous outcome tracking & alerts | |
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| SUPPORT & REPORTING | ||||
| Dedicated clinical QA lead | |
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| Risk score accuracy scorecard & weekly report | |
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| 24/7 critical patient safety defect SLA | |
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Common questions
We validate predicted risk scores against actual clinical outcome data real readmissions, deterioration events, and population health outcomes rather than internal test sets or synthetic benchmarks. We evaluate calibration (whether the model’s predicted probabilities match observed event rates), discrimination (whether high-risk patients experience worse outcomes than low-risk patients), and clinical utility (whether acting on the risk score would actually improve care decisions at different threshold settings).
We run structured bias evaluation across age, gender, ethnicity, socioeconomic status, and comorbidity profile measuring calibration, discrimination, and clinical utility separately for each demographic subgroup. We identify cases where the model systematically underestimates or overestimates risk for specific groups, quantify the performance gap relative to the overall model, and help you trace the bias to training data composition, feature weighting, or outcome label quality to inform remediation.
Common drift causes include patient population shifts, changes in clinical practice patterns, EHR data pipeline updates, and feature distribution changes over time. We establish a validated risk score accuracy baseline at deployment and configure continuous monitoring against that baseline tracking calibration drift, discrimination degradation, and subgroup performance changes. We set alert thresholds that detect meaningful accuracy degradation early, rather than waiting for clinicians to notice changes in the recommendations they are acting on.
We test the full EHR data extraction and feature engineering pipeline verifying that lab values, medication lists, diagnosis codes, and clinical notes are correctly extracted, transformed, and presented to the model. We specifically test for silent data quality failures: stale medication lists, missing lab values due to integration errors, duplicate record merging issues, and incorrect date-time handling all of which cause the model to generate risk scores based on inaccurate clinical inputs without any visible error in the prediction interface.
Model retrains and EHR pipeline changes are among the most common causes of silent risk score regression. We run automated regression testing against the validated accuracy baseline after every model update, feature engineering change, or data pipeline modification measuring calibration changes, discrimination shifts, and subgroup performance impacts before updated predictions reach clinical workflows or care management tools.
Yes. Our AnalyticsShield and ApexAnalytics Suite tiers include regulatory traceability documentation covering risk score accuracy baselines, bias audit findings, EHR input validation records, and drift monitoring protocols structured to support FDA AI/ML SaMD action plan requirements and MHRA AIaMD guidance for UK deployments. This documentation is produced as a standard output of the testing engagement, not a separate audit commissioned when a submission review has already begun.
Get in touch
Start with a free AI Predictive Analytics QA audit.
Tell us about your predictive analytics platform and we’ll map out exactly what clinical testing you need no obligation, no sales pitch.
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We reply to all enquiries within 1 business day