Validation strategy
Define scope, risk, responsibilities, deliverables, acceptance criteria, and the lifecycle evidence that will be maintained after go-live.
Computerized system validation
Evidence that fits the risk and the system.
Validation earns its cost when it concentrates on what would actually harm a patient, a subject, or a decision — and stops manufacturing paper everywhere else.
Perspective
Two failure modes are equally common. One is thin validation that would not survive a serious question. The other is exhaustive validation that documents everything, proves little, and collapses the moment the platform releases a change. The work is deciding — explicitly, and with a rationale somebody can read — where rigour belongs.
Validation principle
The validated state is a property of the operation, not the project.
A system is validated when qualified infrastructure, controlled configuration, working procedures, trained users, and live change control all hold at the same time. Any one of them lapsing ends it, however good the original package was.
Capabilities
Specialist support, connected to the whole system.
Scope is tailored to the engagement; these are the core areas in which QA4Tech can contribute.
Risk-based specification
Write requirements that can be tested and that express intended use, critical functions, and consequence of failure.
Testing & assurance activities
Select a justified mix of scripted testing, unscripted and exploratory assurance, and supplier evidence — and record why.
Independent validation review
Assess plans, requirements, risk assessments, test evidence, traceability, deviations, and reports for what they actually demonstrate.
Systems with AI components
Extend validation to statistical behaviour: evaluation evidence, oversight, monitoring, and model change within a validated system.
Remediation & periodic review
Prioritize gaps, restore control proportionately, and design periodic review that has a chance of being performed.
Levels of validation
Validate once at the right level, not repeatedly at the wrong one.
Most wasted validation effort comes from testing the same platform behaviour again for every study, product, or site — or from assuming a platform validation covers a configuration nobody looked at.
Infrastructure qualification
The qualified ground beneath everything else: compute, storage, network, hosting, and platform services. Covered once, maintained continuously.
Platform-level validation
Core product functions, standard configuration, and the controls common to every use. Performed once by the accountable owner and relied upon thereafter.
Study or product-level validation
What is specific to this use: study build, forms, edit checks, workflows, calculations, roles, and reports. Focused, and never a rerun of the platform.
Local & end-user computing
Spreadsheets, scripts, queries, and reports built outside the platform. Frequently ungoverned, and frequently where data integrity actually fails.
AI-enabled components
Where behaviour is statistical rather than specified, requiring evaluation evidence, monitoring, and defined human oversight alongside conventional testing.
Assurance method
Computer software assurance in practice.
Critical thinking is the method, not a slogan. It means deciding what could go wrong, what evidence would reveal it, and what documentation is worth producing — and then being able to justify all three.
- Intended use, critical functions, and the actual consequence of each failure mode
- Patient, subject, product, and data-integrity risk as the driver of effort
- Supplier evidence used where it is credible, challenged where it is not, and never assumed to be transferable
- A justified mix of scripted testing and unscripted or exploratory assurance
- Documentation proportionate to risk, with the rationale recorded rather than implied
- Traceability that connects requirement, risk, test, and result without a spreadsheet archaeology exercise
- Procedures, training, and access control treated as part of the validated state
Approach
Context first. Evidence throughout.
A clear sequence keeps the work rigorous while avoiding unnecessary process.
- 01
Anchor on intended use
Connect every requirement and assurance activity to the regulated purpose and the way the system will genuinely be operated.
- 02
Scale to risk
Direct effort toward the functions, data, and failure modes that carry real consequence, and record the reasoning.
- 03
Use supplier evidence well
Establish what can be relied on, what needs independent challenge, and what remains unambiguously the customer’s responsibility.
- 04
Design for change
Build release assessment, periodic review, and incident handling into the lifecycle so the validated state survives the next update.
Deliverables
What the engagement produces.
Depending on scope, an engagement produces the strategy, the executable documentation, or an independent verdict on what already exists.
- Validation master plan or system-specific validation plan
- User requirements and functional specification written to be testable
- Risk assessment with documented criticality and effort rationale
- Test strategy, protocols, scripts, and executed evidence
- Requirement-to-test traceability and deviation handling
- Validation summary report with an explicit release conclusion
- Supporting procedures, role-based training material, and periodic review design
Reference frameworks
What the validation approach is built on.
These frameworks are compatible; the work is choosing which one leads for a given system and making that choice explicit in the plan. The list below is a starting point rather than the full set.
- GAMP 5 (2nd edition)
- Risk-based lifecycle, software categories, supplier leverage, critical thinking, and its guidance on AI and machine learning.
- FDA computer software assurance
- Assurance effort concentrated on high-risk functions, with unscripted testing recognized as valid evidence.
- EU Annex 11 & 21 CFR Part 11
- Computerized systems in a regulated environment: records, signatures, audit trails, access, and data lifecycle.
- ICH E6(R3)
- Computerized systems, data governance, and proportionate validation expectations in clinical research.
- EMA guideline on computerised systems and electronic data in clinical trials
- Detailed European expectations for validation, configuration, audit trails, and cloud services used in trials.
- PIC/S PI 041
- Data integrity and management expectations applied across the computerized system lifecycle.
These are examples, not a complete list. The frameworks and criteria that apply to a particular engagement are identified and agreed as part of defining its scope.
Typical applications
Where this work can apply.
- New system implementations and platform migrations
- Cloud and SaaS validation under continuous supplier change
- Validation remediation after an audit or inspection finding
- Introducing AI components into a validated system
- Study-level build assurance in clinical platforms
- Independent review of a completed validation package
Start a conversation
Bring the right level of assurance to the next decision.
Begin with a focused discussion about context, risk, evidence, and the outcome you need.