Continuous eTMF Inspection Readiness

Inspection readiness is often summarized with a red, amber, or green dashboard. The summary is useful for attention, but it can become misleading when the denominator, source freshness, rule coverage, and severity logic are hidden. The IntuitionLabs model keeps the composite deliberately decomposable. A user can see every input, its current measure, configured weight, source coverage, blocking findings, and the actions that would improve it.

The prototype uses five inputs: TMF completeness, document quality, cross-system agreement, timeliness, and expiring documents. Those categories cover different questions. Completeness asks whether expected evidence exists. Quality asks whether the artifact that exists is adequate. Cross-system agreement asks whether the document record agrees with clinical operations and data systems. Timeliness asks whether evidence arrived within the approved window. Expiry looks forward to evidence that will cease to be current.

A composite is calculated only from controlled definitions. The calculation itself should be deterministic and reproducible. Model-assisted components feed structured findings into those inputs, but the arithmetic, weights, thresholds, exclusions, and missing-data behavior are explicit. The system records the readiness-profile version with each result so a score from one date can be interpreted after the configuration changes.

The score is not a regulator’s assessment, a probability of inspection success, or proof that the file is complete. It is a management indicator for the purpose defined by the customer. A study can have a high composite and still contain a blocking item. The interface therefore keeps blocking findings visible and can cap or qualify the aggregate when a critical condition exists.

Scenario analysis is separated from controlled reporting. A user can explore what happens if cross-system agreement receives more weight or if a remediation closes a finding. That helps teams understand sensitivity and allocate effort. It does not rewrite the approved production profile. Publishing a new weight set follows review, testing, approval, and an effective date.

The design supports study and portfolio views. At study level, every input drills into documents, records, and findings. At portfolio level, leaders can compare trends while seeing which readiness profile and rule coverage apply to each study. Non-comparable studies are labelled instead of being forced into a single ranking.

Document quality is not one universal score. It is a collection of checks with different evidence, consequence, and ownership. Signature completeness, date consistency, currency, expiry, legibility, pagination, identifier presence, and duplicate filing each require their own definition and validation. The product preserves that detail and can derive a normalized quality input only after the individual checks are evaluated.

A running check has an approved source rule, scope, method, tolerance, exceptions, severity mapping, and allowed disposition. A candidate check is visible as design work but does not affect readiness. This prevents a demonstration idea from becoming a production control without review. The boundary is especially important when a model recognizes a possible issue but customer policy determines whether it matters.

Findings cite the page, section, field, or visual region. An empty signature block is linked to the relevant page. A date conflict preserves both candidate dates and their locations. A stale CV cites the signature or effective date and the customer rule used to assess currency. A poor scan records the OCR or visual-quality evidence and abstains from automatic filing when the threshold is not met.

The quality input distinguishes blocking and non-blocking conditions. A missing signature required for an effective record may prevent progression. An approaching expiry creates a forward-looking task but may not reduce current quality in the same way. A potential duplicate can remain a watch item until confirmed. The score reflects the customer’s consequence model, while the interface retains the original count and severity distribution.

Document populations matter. A quality rate over twelve clean documents does not represent a study with thousands of artifacts. Production reporting shows coverage: which document types were read, what percentage of the applicable file was evaluated, which documents could not be processed, and which checks apply. Missing coverage reduces confidence or creates a separate data-quality condition rather than being treated as a pass.

Reviewer dispositions feed monitoring. Confirmed-positive rate, false-positive rate, missed-finding rate from quality sampling, amendment rate, abstention rate, and time to evidence can be tracked by document type and rule version. A check that creates noise is revised or retired. A check that finds recurring issues can inform process improvement beyond the individual documents.