Discovery Call
We learn your platform, knowledge base content, clinical use cases, and retrieval architecture in a 30-minute session.
Healthcare RAG apps deserve clinically verified retrieval
Testiva delivers specialist QA for Healthcare RAG applications clinical knowledge retrieval accuracy, guideline currency, hallucination detection, and HIPAA-safe pipelines tested end to end.
200+
Clinical RAG queries evaluated
3x
Faster Release Cycles
40%
Lower Rework Costs
Why it matters
What happens when Healthcare RAG
What happens when Healthcare RAG
applications aren’t tested properly
Outdated clinical guidelines surfaced as current
A superseded treatment protocol generates a confident, well-formatted answer. Standard QA sees a response. Clinical QA sees a patient safety risk.
AI generates content not grounded in retrieved sources
RAG systems hallucinate in the generation layer even when retrieval is accurate adding drug interactions or dosages absent from the retrieved documents.
Jurisdiction-incorrect guidelines returned for clinical queries
A UK-deployed RAG system retrieving US drug dosages or vice versa creates clinical risk invisible in format-based testing without clinical domain knowledge.
Retrieval quality degrades silently after knowledge base updates
A re-indexing event or embedding model change silently degrades retrieval relevance. The first signal is a clinician reporting that answers feel different.
How Testiva protects your Healthcare RAG application
- Clinical knowledge engineers, not generic QA — We evaluate retrieval outputs against current clinical standards and formulary references, not relevance scores alone.
- Two-layer testing: retrieval and generation — We test whether the right documents were retrieved AND whether the AI faithfully represented them as separate workstreams.
- Guideline currency and jurisdiction verified — We audit your knowledge base for superseded protocols and jurisdiction-incorrect content across US and UK clinical standards simultaneously.
- PHI isolation across the full retrieval pipeline — We test that patient data ingested into the knowledge base cannot be retrieved in response to unrelated clinical queries.
- Retrieval regression on every knowledge base update — Automated quality evaluation triggered on content changes, re-indexing, and embedding model updates no silent degradation.
What we test
Core components of a Healthcare RAG application we cover
Every layer of the retrieval-generation pipeline that affects clinical accuracy and patient safety is validated against current clinical standards.
Clinical Retrieval Accuracy Testing
Relevant and complete clinical documents verified for every query type.
Guideline Currency & Freshness Testing
Superseded protocols and deprecated drug recommendations identified before use.
Jurisdiction Accuracy Testing
Cross-jurisdiction guideline contamination detected across US and UK knowledge bases.
RAG Hallucination & Faithfulness Testing
AI-generated clinical content verified against retrieved source documents.
Out-of-Knowledge-Base Query Handling
System verified to decline or flag queries outside its knowledge boundary.
Source Attribution & Citation Testing
Citation links verified to correctly map to referenced content in every response.
PHI Isolation & Pipeline Security
Patient data tested across chunking, embedding, retrieval, and generation for HIPAA.
Retrieval Regression Testing
Retrieval quality re-evaluated on every knowledge base update and re-indexing event.
Retrieval Performance & Scalability
Latency and throughput verified under enterprise-scale concurrent clinical query loads.
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 knowledge base currency, retrieval pipeline, and build a two-layer testing strategy with clinical ground truth queries.
Test Execution
Retrieval accuracy evaluation, guideline currency audit, hallucination testing, PHI isolation, and regulatory mapping every finding rated by clinical severity.
Report & Iterate
Clinical accuracy report with retrieval metrics, hallucination rates, guideline currency findings, 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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Healthcare RAG Testing Packages
| Feature | Starter | Professional | Clinical | Enterprise |
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| CORE RAG TESTING | ||||
| Clinical retrieval accuracy testing |  |
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| Generation faithfulness testing | |
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| Source attribution accuracy testing | |
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| Out-of-knowledge-base query handling |  |
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| Concurrent query load testing | |
5K queries | 10K queries | Unlimited |
| Multi-specialty coverage testing | |
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Setup only | Full build |
| CLINICAL KNOWLEDGE QUALITY | ||||
| Guideline currency & freshness audit | |
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| Jurisdiction accuracy testing (US & UK) | |
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| Clinical hallucination detection | |
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| Retrieval regression on KB updates | |
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| Chunking & embedding quality testing | |
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| SECURITY, COMPLIANCE & MONITORING | ||||
| PHI isolation & pipeline security audit | |
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| HIPAA compliance testing | |
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| FDA AI/ML validation evidence | |
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| MHRA AIaMD & NHS AI governance | |
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| Post-deploy retrieval drift monitoring | |
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| SUPPORT & REPORTING | ||||
| Dedicated clinical QA lead | |
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| Retrieval accuracy scorecard & weekly report | |
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| 24/7 critical defect SLA | |
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Common questions
We build synthetic clinical query datasets designed to surface retrieval failures across condition categories, drug interactions, clinical guidelines, and specialty-specific topics. These are validated against clinical standards before use. Real patient data never enters our test environments we work with your de-identification protocols if you have existing content you wish to use under a formal data handling agreement.
Retrieval accuracy testing evaluates whether the RAG system surfaces the correct, relevant, and current clinical documents in response to a query. Faithfulness testing evaluates whether the AI-generated response accurately represents those retrieved documents without adding clinical details, drug dosages, or recommendations not present in the source. Both layers fail independently: retrieval can be accurate while generation halluccinates, and vice versa. We test them as separate workstreams.
We audit your knowledge base content against current clinical standards for each condition category comparing ingested guidelines against authoritative sources such as NICE, BNF, FDA labelling, and specialty society publications. We flag superseded protocols, deprecated drug recommendations, and outdated treatment pathways including content that is current in one jurisdiction but not in another if you serve both US and UK clinical users.
Yes this is one of the most underappreciated HIPAA risks in Healthcare RAG systems. If clinical notes or patient records are ingested into the knowledge base, they can be partially or fully retrieved in response to unrelated diagnostic or clinical queries. We specifically test PHI isolation across chunking, embedding, vector storage, retrieval, and generation verifying that patient identifiers and protected health information cannot surface through the retrieval interface.
Knowledge base updates, re-indexing events, and embedding model changes are among the most common causes of silent retrieval quality degradation in Healthcare RAG systems. We establish a clinical retrieval accuracy baseline and run automated regression testing against that baseline after every knowledge base change detecting retrieval relevance drops, citation drift, and hallucination rate changes before clinicians notice different answer quality.
Yes. Jurisdiction accuracy testing is a core part of our Healthcare RAG QA methodology for mixed US-UK deployments. We test for cross-jurisdiction guideline contamination cases where US-specific drug dosages, formulary references, or clinical pathways are returned to UK users, and vice versa. We also verify that your knowledge base metadata and retrieval filters correctly segment jurisdiction-specific content, and that regulatory documentation covers both FDA AI/ML requirements and MHRA AIaMD guidance.
Get in touch
Start with a free Healthcare RAG QA audit.
Tell us about your Healthcare RAG application and we’ll map out exactly what clinical testing you need no obligation, no sales pitch.
Email us
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We reply to all enquiries within 1 business day