Regulated Industries Served
Validation in Healthcare, Pharma and AI require industry-specific thinking, regulatory literacy and a bias for evidence, not opinions.
Why Regulated Industries Need Different Validation
Regulated industries operate under a simple rule:
no evidence – no trust
Validation is not just about catching defects. It is about:
Demonstrating to regulators that systems are fit for use.
Showing auditors that processes are controlled, repeatable and transparent.
Protecting patients and end-users from harm, bias or data misuse.
Keeping innovation moving without constant “stop-the-line” fire-fighting.
Complex Industries
Focused Validation
Targeted QA and validation for Healthcare, Pharma and AI organisations in the US and EU, aligning critical systems with strict regulatory expectations.
Healthcare
EHR, telehealth and digital health platforms validated against HIPAA, GDPR and clinical workflow risks to protect patient data and decision-making.
Life Sciences
GxP-critical lab and manufacturing systems aligned with FDA 21 CFR Part 11, EU Annex 11 and data-integrity expectations across the product lifecycle.
AI
High-risk and general-purpose AI solutions structured around EU AI Act, ISO/IEC 42001 and NIST AI RMF for controlled, explainable model behaviour.
Healthcare
Clinical, EHR & Digital Health
Why Compliance & Correct Validation Matter in Healthcare
Correct validation in Healthcare ensures that:
Systems behave as intended in real clinical workflows, not only in ideal test cases.
Privacy and security controls are aligned with HIPAA/GDPR expectations.
Changes to EHR, LIS, RIS, PACS and telehealth platforms are deployed with known risk, not guesswork.
Vendors and integrators are held to a consistent quality baseline.
What Strong Validation Looks Like in Healthcare
Clinical workflow mapping
Before test design, so important scenarios reflect reality.
Risk-based testing
Prioritising high-impact pathways such as medication orders, results reporting and consent capture.
Traceability matrices
Linking requirements, risks, tests and issues.
Structured change control
Including impact assessment and regression strategies for upgrades, patches and integrations.
Security and privacy testing
From access control to logging and auditing.
Vendor oversight
Ensuring third-party implementations align with internal policies and regulatory requirements.
Life Sciences & Pharma
GxP, Labs & Manufacturing
Why Compliance & Correct Validation Matter in Pharma
Correct validation in Pharma ensures that:
Systems comply with GxP expectations across the full lifecycle.
Data integrity principles (ALCOA+) are respected end-to-end.
21 CFR Part 11 / Annex 11 controls are defined, implemented and tested.
Audit trails, electronic signatures and user access are fit for inspections.
What Strong Validation Looks Like in Pharma
Risk-based validation planning
Aligned with GAMP 5 and GxP expectations.
Clear definition of intended use
User requirements and critical functions.
Structured test strategy
Including installation (IQ), operational (OQ) and performance (PQ) qualifications where applicable.
Data integrity checks
From user management to audit trails and reports.
Supplier and vendor assessments
Especially for SaaS and cloud-based systems.
Lifecycle governance
Covering upgrades, patches, decommissioning and archiving.
AI (US/EU)
High-Risk, General-Purpose & Regulated AI
Why Compliance & Correct Validation Matter in AI
Correct validation for AI ensures that:
AI behaviour is understood, tested and constrained in high-risk use cases.
Training, validation and test datasets are traceable and governed.
Human-in-the-loop mechanisms are designed and verified, not just claimed.
Evidence exists to satisfy regulators, partners and enterprise buyers.
An AI-focused Validation approach
Risk classification
AI use cases, aligned with EU AI Act and internal policies.
Data governance and lineage
Covering sources, preprocessing and usage constraints.
Test design for AI
With scenario-based, adversarial and stress testing.
Bias and fairness checks
Using relevant metrics for the domain.
Explainability and transparency
Appropriate to stakeholders.
Operational monitoring
Linking production behaviour back into test and improvement cycles..
Standards, Regulations & Frameworks Used
Across Healthcare, Pharma and AI, engagements typically reference and align with relevant standards and regulations such as:
Healthcare
HIPAA, HITECH, GDPR, MDR, IVDR, local health data laws, ISO 27001, OWASP and secure development practices
Pharma & Life Sciences
GxP guidelines, GAMP 5 principles, FDA 21 CFR Part 11, EU Annex 11
AI
EU AI Act requirements, ISO/IEC 42001 (AI management systems), ISO/IEC 27001, NIST AI RMF
Clarify Your Regulatory Reality
Healthcare, Pharma and AI systems in the US and EU face similar regulatory themes, but each organisation carries its own history, architecture and risk profile.
A short, structured conversation can surface the key constraints, the quickest wins and the right level of QA and validation discipline for your context.
