27 periodic reviews. 650+ documents analyzed. One clear view of system health.

Periodic reviews are intended to answer a relatively simple question:

Is this GxP system or piece of computerized equipment still operating in a controlled and appropriately validated or qualified state?

Getting to that answer is rarely simple.

A reviewer may need to look across change controls, deviations, CAPAs, validation documentation, training records, maintenance history, access reviews, audit-trail reviews, backup evidence and actions from the previous periodic review.

The challenge is not just reviewing each file, but understanding what all of that information means together.

Assurea supported a mid-sized pharmaceutical manufacturer with a periodic-review program covering 27 GxP systems and computerized equipment assets.

Using our custom AI analytics solution together with Assurea’s validation and quality expertise, we analyzed 650+ documents and data exports and converted the information into structured, review-ready insights.

No integration with the client’s QMS, CMMS, LMS or other enterprise systems was required.

The Challenge: Periodic Review Information Was Everywhere

The 27 reviews covered a mix of manufacturing equipment, laboratory instruments and GxP computerized systems.

For each review, information had to be gathered from multiple sources, including:

  • Change-control exports
  • Deviation reports
  • CAPA reports
  • Maintenance and calibration records
  • Validation and lifecycle documentation
  • Training reports
  • User-access reviews
  • Audit-trail reviews
  • Backup and recovery information
  • Security information
  • Previous periodic-review reports and actions

A single export could contain dozens of underlying changes, deviations or maintenance events.

The reviewer still had to determine:

  • Is anything missing?
  • Is anything still open?
  • Is anything overdue?
  • Are similar events happening repeatedly?
  • Did several changes affect the same system function?
  • Did a CAPA actually solve the problem?
  • Are previous periodic-review actions closed?
  • Does anything require assessment of the validated or qualified state?

That is where periodic reviews become time-consuming — and where important patterns can be missed.

Practical Tip: Before starting a periodic review, define the expected evidence by system type. A laboratory instrument may require different review categories than manufacturing equipment or an enterprise application. This makes missing information much easier to identify.

The Assurea Approach: AI Analytics + Life Sciences Expertise

The client did not need to implement a new platform or integrate its GxP systems with Assurea.

Instead, the client provided the periodic-review information already available for the applicable review period. Across the 27 reviews, Assurea analyzed 650+ documents and data exports.

Our AI analytics evaluated the information through a series of practical periodic-review questions.

What’s missing?

The analytics identifies expected documentation or supporting evidence that cannot be located, helping the reviewer distinguish a complete review package from one that still requires follow-up.

Example: A change control referenced completed validation testing, but the supporting executed test evidence was not included in the review package.

What’s still open?

The analytics connects current-period information with previous actions, changes, CAPAs and investigations to identify items that may have carried forward unresolved.

Example: An action from the previous periodic review remained listed as open, with no closure evidence in the current review package.

What’s overdue?

The analytics highlights activities or actions that appear overdue or where expected evidence has not been provided so that the appropriate owner can confirm the actual status.

Example: One of the expected quarterly user-access review records was not available for the applicable review period.

What’s recurring?

The analytics looks across deviations, CAPAs, maintenance records and other information to identify similar events that may represent a recurring pattern rather than isolated incidents.

Example: Four maintenance records used different descriptions but involved repeated failures of the same temperature-control component.

What changed?

Rather than simply counting changes, the analytics examines whether multiple changes affected related functions and whether cumulative validation impact should be considered.

Example: Three configuration changes during the same review period affected the same system function.

What needs human assessment?

The analytics surfaces the relationship, provides the supporting evidence and identifies where Quality, Validation, Engineering or the System Owner needs to make the final assessment.

Once the analysis was complete, Assurea’s validation and quality specialists:

  • Reviewed the AI-generated observations
  • Verified supporting evidence
  • Investigated ambiguous findings
  • Eliminated false or irrelevant relationships
  • Identified items requiring client follow-up
  • Prepared the information needed to support completion of the periodic review

The technology accelerated the analysis and human expertise remained responsible for the judgment.

Example: Several deviations and maintenance events appeared to involve the same system function, but the available evidence was not sufficient to determine whether the validated state had been affected.

What We Found

Most systems were operating without significant unresolved concerns.

Across the 27 systems:

  • 18 systems had no significant unresolved issue requiring escalation.
  • 6 systems required follow-up actions but had no indication of loss of validated or qualified state.
  • 3 systems contained observations requiring formal client assessment.

That distinction mattered.

The goal was not to generate more red flags. It was to help reviewers quickly differentiate between systems that were healthy and systems that genuinely needed attention.

Top 3 Findings
Finding 1: Missing Evidence That Could Easily Be Overlooked

The analysis identified 14 instances of missing or incomplete expected evidence across nine systems.

Examples included:

  • A required user-access review not included in the review package
  • Backup records available, but no restore-verification evidence
  • A lifecycle document referenced as current when a newer version existed
  • Validation evidence referenced in a change but not included with the review
  • Previous periodic-review actions without clear closure evidence

Missing evidence did not automatically mean that a control had failed. It meant something more specific. The review package could not yet demonstrate that the control had been completed.

This allowed the client to locate the missing evidence or initiate follow up before approving the periodic review.

Example: A dashboard showing “Backup: Complete” could be misleading if routine backups were performed but restore verification could not be demonstrated. The reviewer needs to see exactly what evidence is present and what is not.

Finding 2: Previous Actions Had Quietly Carried Forward

Periodic reviews may be performed annually, every two years or according to another risk based frequency. That creates an opportunity for old actions to disappear between review cycles.

Across the portfolio, the analytics identified seven previous periodic review actions without clear closure evidence in the current review package.

After follow up:

  • 5 were confirmed as completed, but closure documentation had not been clearly linked.
  • 2 required additional action.

Without explicitly connecting the previous review to the current one, those items could easily have remained buried in historical documentation.

Finding 3: Patterns Were Hidden Across Individual Records

One of the strongest benefits came from trend analysis.

Assurea identified six recurring patterns across deviations, CAPAs and maintenance information that warranted additional review.

Example: Repeated Equipment Failure

For one manufacturing system, four corrective-maintenance records used slightly different descriptions but involved the same temperature-control component.

Individually:

  • Each work order had been completed.
  • Each repair appeared acceptable.
  • No single event looked significant.

Together:

The records showed a recurring component-failure pattern.

In another system, several quality events occurring months apart used different terminology but shared a similar failure mode.

The AI surfaced the potential relationship. Assurea’s specialists then determined whether it represented a meaningful trend.

This is an important difference between counting records and analyzing records.

A human reviewer may easily identify a trend when three records have the same title. The harder problem is finding the relationship when the descriptions look like this:

  • “Temperature instability”
  • “Probe replacement”
  • “Chamber temperature alarm”
  • “Sensor reading deviation”

The words are different.The underlying problem may be the same.

The Operational Impact: Nearly 500 Hours of Estimated Effort Saved

Before using the AI-assisted approach, the client estimated that a typical periodic review required approximately 38 hours per system of combined effort across preparation, document review, analysis, stakeholder follow-up and completion.

Across 27 systems, that represented more than 1,000 hours of periodic-review effort.

With Assurea’s AI analytics supporting evidence review and synthesis, average effort decreased to approximately 20 hours per periodic review.

Estimated Impact
  • ~486 hours saved
  • ~47% reduction in periodic-review effort
  • 27 periodic reviews completed within the planned review cycle

The efficiency did not come from removing human review.

It came from reducing the amount of experienced people’s time spent:

  • Searching for information
  • Sorting exports
  • Reading repetitive records
  • Manually comparing events
  • Rebuilding summaries
  • Looking for relationships across multiple sources

That allowed Quality, Validation, Engineering and System Owners to spend more time where their expertise mattered most:

Reviewing the findings. Assessing the risk. Making the decision.

More Than Faster Review

Speed was valuable, but it was not the only benefit. The reviews also became more systematic and easier to understand. For each system, stakeholders could see:

  • Reviewed: What information was analyzed
  • Missing: What expected evidence could not be found
  • Open: What remained unresolved
  • Overdue: What should already have been completed
  • Recurring: What patterns appeared across multiple events
  • Changed: What significant changes occurred
  • Potential Impact: What might require validated/qualified-state assessment
  • Actions: What still needed to happen

Instead of navigating multiple spreadsheets, PDFs, reports and lifecycle documents, stakeholders received a consolidated picture of what had happened to the system during the review period.

Where AI Stops and Human Expertise Begins

Periodic review is a strong use case for AI because the work combines large volumes of information with expert judgment.

AI is particularly useful for:

  • Reviewing large document sets
  • Comparing information across sources
  • Finding missing information
  • Connecting similar events
  • Recognizing recurring patterns
  • Organizing findings for review

But those capabilities do not replace Quality or validation judgment.

For example, the analytics may identify four similar temperature-probe failures during the review period and show:

  • The related maintenance records
  • The affected system function
  • When each failure occurred
  • Whether similar deviations were reported
  • Whether an associated CAPA exists

That is valuable analysis.

The appropriate client stakeholders still determine what that evidence means for the system’s validated or qualified state.

AI provides scale and pattern recognition. Assurea provides the CSV and Quality expertise to determine what those patterns actually mean.

The Outcome: From Fragmented Evidence to 360° System Intelligence

For this pharmaceutical manufacturer, periodic review shifted from a largely manual document-review exercise to a structured assessment of system health.

Across the Program
  • 27 GxP periodic reviews completed
  • 650+ documents and data exports analyzed
  • 14 missing or incomplete evidence items identified
  • 7 previous review actions requiring closure confirmation or follow-up
  • 6 recurring patterns surfaced across Quality and maintenance information
  • 3 systems requiring formal validated/qualified-state assessment
  • ~47% estimated reduction in periodic-review effort
  • ~486 hours of estimated effort saved

More importantly, the organization gained a clearer picture of what had actually happened across its GxP systems during the review period.

Systems with complete, well-controlled evidence could be recognized faster. Systems requiring attention could be identified earlier. And experienced Quality and Validation professionals could spend less time searching for information and more time making the decisions that require their expertise.

The periodic reviews did not become less rigorous. They became more focused, more systematic and easier to act on.