Batch Review by Exception (BRBE) – Focusing QA on What Really Matters

This topic is part of the SG Systems Global regulatory & operations glossary.

Updated November 2025 • eBR, MES, Data Integrity, Release • Pharma, Biotech, Food, Devices, Cosmetics

Batch Review by Exception (BRBE) is a risk‑based approach to batch record review where validated electronic systems automatically check every step, signature, parameter and material movement, and humans only review the exceptions the system flags. Instead of QA reading every line of every batch record, attention is focused on deviations, warnings and unusual patterns that actually matter for product quality and compliance.

“The point is not to read faster; it is to stop reading what the system can check better than humans.”

1) What Batch Review by Exception Really Is

BRBE is not “skipping batch review”; it is automating the repetitive parts of review under controlled, validated conditions. In a traditional process, QA manually checks every calculation, signature, parameter and entry in the batch record. In BRBE, the eBR/MES system runs those checks automatically using predefined rules and highlights only exceptions—missing data, out‑of‑range values, sequence violations, incomplete steps or unresolved deviations.

The human reviewer still makes the release decision, but their time is spent on assessing risk and context rather than mechanically ticking boxes. If your BRBE design simply hides problems instead of surfacing them, you do not have BRBE; you have a latent data‑integrity issue waiting to be found in inspection.

2) Regulatory Perspective & Acceptance

Regulators do not require QA to re‑read every line if a validated system has already done the checking. What they care about is whether the review process is effective, risk‑based and documented. 21 CFR 211 and global GMPs require thorough review of production and control records; 21 CFR Part 11 and Annex 11 define expectations for electronic records and signatures.

Inspectors will ask how exceptions are defined, how rules are validated, how changes are controlled, and how the QP or QA person ensures the ruleset remains appropriate over time. If you cannot explain and document this, they will assume the system is under‑controlled, regardless of how modern the UI looks.

3) Prerequisites – Structured eBR, Not Scanned Paper

BRBE only works when batch data is structured and machine‑checkable. If your “eBR” is just scanned paper or unstructured PDFs, there is nothing for rules to operate on. True BRBE requires a data‑centric electronic batch record where materials, weights, parameters, signatures and statuses are stored as discrete fields with metadata.

This in turn relies on clear URS, configuration standards and a robust Validation Master Plan (VMP). If you skip the design work and simply “parameterise” paper forms, you lock in manual review pain and eliminate most of the value BRBE can offer.

4) What the System Checks vs What Humans Still Must Review

Systems are good at checking rules; humans are better at judging context. BRBE typically delegates to the system checks such as step completion, mandatory fields, state transitions, material identity and status, in‑range parameters, equation correctness and barcode matches. These checks can be defined once and run consistently for every batch.

Humans remain responsible for reviewing complex deviations, trends across batches, scientific plausibility, unusual operator comments and any residual manual attachments. A BRBE design that tries to automate judgement decisions usually fails; a design that avoids any system checks is just wasteful. The sweet spot is clearly documented division of labour between automated checks and human review.

5) Defining Exception Rules & Risk Classes

At the heart of BRBE is the exception ruleset: the conditions under which the system flags something for human attention. These should be derived from Quality Risk Management (QRM), PFMEA, control plans and product‑specific knowledge. Typical rules cover critical process parameters, material identities, status checks, setpoint/limit violations and missing approvals.

Exceptions should be risk‑graded (critical, major, minor) to help reviewers triage. Too few rules and you miss real issues. Too many low‑value rules and you generate alert fatigue, encouraging reviewers to bulk‑close exceptions without reading them. Getting this balance right takes iteration and ruthless focus on what truly affects patient safety and product quality.

6) Data Integrity, Audit Trails & ALCOA

BRBE stands or falls on data integrity. If the underlying data is not complete, consistent and trustworthy, automating checks simply produces bad answers faster. Systems must comply with ALCOA+ principles and maintain robust audit trails for all critical data elements.

During inspections, authorities routinely compare BRBE narratives with audit trails, user access rights and change histories. If the system allows silent overwriting of values, back‑dating or undocumented rule changes, your BRBE programme will be viewed as a risk amplifier, not a control improvement.

7) Integration with Deviations, CAPA & OOS

In a mature design, BRBE is tightly linked to deviation, OOS and CAPA workflows. Certain exception types (e.g. critical limit breaches, use of rejected materials, missing key signatures) should automatically require a deviation or investigation record. Others may be documented and justified directly in the eBR with appropriate comments and attachments.

What inspectors want to see is consistency: similar exceptions produce similar responses, and trends in exceptions drive systemic CAPA and improvements. If the same rule fires batch after batch with the same weak justification, your BRBE data is quietly telling you where your next observation will land.

8) Impact on Release Time & QA Workload

Done properly, BRBE can materially reduce release lead times and QA workload per batch, especially in high‑volume operations. Reviewers spend less time scanning pages and more time solving real problems. Metrics often improve for on‑time release, queue length and “right‑first‑time” batch documentation.

However, the primary justification should not be headcount reduction. BRBE shifts effort from repetitive checking to rule design, validation, monitoring and continuous improvement. If management treats BRBE purely as a cost‑cutting tool, corners will be cut in design and validation—and regulators will eventually notice.

9) Implementation Roadmap – Start Narrow, Prove, Then Scale

Pragmatic implementations start small: one product, one line, one region of the batch record. Define a clear URS for BRBE, configure rules in the eBR/MES platform, and qualify them under a structured CSV approach (often aligned to GAMP 5). Run shadow mode for a period, comparing traditional full review with BRBE outputs.

Once confidence is built—and data shows equivalent or better defect detection—you can extend BRBE scope to more products, sites and processes. Skipping this “prove it” phase and flipping everything overnight is how you end up with unvalidated rulesets and panicked back‑pedalling in front of inspectors.

10) Change Control & Governance of Rules

Exception rules are part of your control strategy and must live under formal change control. Changes should be risk‑assessed, tested and documented, with clear rationales linking rules to QRM, PFMEA and specifications. QA should have visibility and veto rights over rule changes that affect release decisions.

Sites should maintain an inventory of active BRBE rules, including scope, criticality, test coverage and history. Periodic review—e.g. tied to PQR/APR or validation maintenance—checks that rules are still relevant and that exception patterns match design intent.

11) Hybrids, Attachments & Legacy Processes

Most organisations live for years in hybrid mode: part of the batch is fully electronic; part is still paper, PDF or external system outputs. BRBE should be honest about these limits. You cannot “review by exception” what the system cannot see or interpret.

Policies should clearly state which sections of the record are eligible for BRBE and which still require traditional review. Attachments (e.g. external lab certificates, manually loaded trend plots) often demand human scrutiny until they are integrated as structured data. Pretending everything is covered when it is not is a fast route to data‑integrity findings.

12) BRBE Across Sites, CMOs & Quality Agreements

In multi‑site networks and CMO relationships, BRBE has to align with quality agreements. Sponsors need transparency on which checks are performed automatically, what exceptions look like, and how frequently they occur. CMOs need clarity on which exceptions must be escalated before batch release.

Standardising BRBE design principles across sites improves comparability and allows shared analytics on exceptions, CAPA and yield. If each site invents its own rule taxonomy without coordination, network‑level governance becomes nearly impossible and inspection responses become inconsistent.

13) Common Failure Modes & How Inspectors Spot Them

Common BRBE failure patterns include weak or incomplete rulesets, poor documentation of rule rationales, inconsistent handling of the same exception type, and lack of evidence that rules are tested after changes or upgrades. Another red flag is an almost empty exception log despite a complex process—usually meaning rules are too loose or not implemented where people think they are.

Inspectors will compare exception statistics with deviation records, CAPA themes and manual observations on the shop floor. If the BRBE log says everything is perfect while reality clearly is not, confidence in your electronic controls drops sharply, and so does the inspector’s willingness to accept streamlined review practices.

14) KPIs, Dashboards & Continuous Improvement

Typical BRBE metrics include batch review cycle time, percentage of batches released on time, number of exceptions per batch (by type and risk class), proportion of exceptions leading to deviations, and recurrence rates. These metrics should feed into PQR/APR, QMS dashboards and management review.

There is no “ideal” number of exceptions, but extreme values are informative. Very high counts indicate poor basic control or noise; very low counts in a complex process usually mean under‑specification. The goal is a stable, interpretable exception profile that correlates with actual risk and drives targeted improvements over time.

15) FAQ

Q1. Is Batch Review by Exception acceptable to regulators?
Yes—if it is implemented in a validated system, based on documented risk assessments, with clear procedures and evidence that rules are effective. Regulators object to weak design and poor documentation, not to the concept itself.

Q2. Does BRBE mean we can reduce QA headcount?
Not automatically. BRBE can free QA from low‑value checking and reduce overtime, but it also creates new work in rule design, validation, monitoring and improvement. Treat any headcount reduction as a by‑product of maturity, not the primary goal.

Q3. How many exceptions per batch is “normal”?
There is no universal number. The right level depends on process complexity, ruleset design and product risk. What matters is that exceptions are meaningful, manageable and aligned with observed process performance and quality outcomes.

Q4. How do we justify BRBE in validation and SOPs?
Describe which checks the system performs, how rules are derived from QRM and specifications, how they are tested (IQ/OQ/PQ), how changes are controlled and how reviewers handle and document exceptions. Then reflect that in URS, risk assessments, validation reports and SOPs.

Q5. What is a practical first step if we are still doing full manual batch review?
Start by mapping which checks QA performs today and which of those could safely be automated in the eBR/MES. Implement a small set of high‑value rules for one product, run in parallel with full review, and use the results to refine your BRBE approach before scaling.

Related Reading
• Batch Records & Electronic Systems: BMR | eBR | MES | Automated Batch Records | CSV
• Release & Quality Oversight: Batch Release | QP Release | PQR/APR | QMS
• Data Integrity & Risk: Data Integrity | Audit Trail | ALCOA+ | QRM | Deviation/NCR | CAPA | GxP