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A recent MedCity News opinion piece made a useful point about AI in drug development: the constraint is often not the quantity of data, but whether that data can be understood as evidence. The question is also relevant well beyond drug development.
AI can analyse large volumes of information, find patterns and bring together inputs that a person could never review manually. Its output still depends on the information foundation beneath it. When a model produces a signal, a recommendation or a summary, teams need to answer basic questions: Which source records support it? What did those records mean at the time? Were they complete? Who changed them? Can the result be reproduced or challenged later?
Data becomes usable information when its meaning, provenance, relationships and controls remain available. Without that context, it may still be data, but it is no longer reliable evidence.
This matters because data can survive while evidence disappears. A clinical document can be copied into a data lake without its original identifiers, approval history or relationship to the protocol it supported. A laboratory result can be exported without the context of the instrument, method, version and review that made it interpretable. A completed trial can outlive the systems that held its eTMF, quality records and structured data, leaving files behind but weakening the ability to explain the whole record.
The point is not that every AI use case needs a single, perfect enterprise repository. It is that high-value decisions need a traceable route back to the information and controls that support them. That route has to be designed before the data is reused, migrated or fed into a model.
Start with an evidence package, not a dataset
For each record class that may support research, quality, safety or regulatory decisions, define what must travel with the content.
The package usually includes more than the document or data extract. It may need the authoritative source identifier, business and technical metadata, relevant timestamps, the version in force at the time, linked records, approvals, signature or review evidence, and the retention basis. The exact list will differ between a batch record, clinical-trial artefact, quality event and submission file. What matters is that the organisation can explain why a given item is what it claims to be and how it relates to the wider process.
This should be a functional requirement, agreed by quality, regulatory, data and business owners. Leaving it to an export script or a future migration team is how the evidence chain becomes incomplete.
Preserve provenance through every transformation
Most loss of evidential value happens in the hand-offs. A system export is cleaned, mapped, enriched, converted and loaded into another platform. Each step may be technically sound while making the result harder to interpret later.
Maintain a record of the source, extraction date, transformation rules, schema version and validation performed. When AI contributes to a process, capture the model or service version, the inputs made available to it, the output retained, and the human review or decision that followed. This does not require preserving every ephemeral processing detail forever. It does require a proportionate trail for outputs that could influence a regulated or material decision.
Treat the AI output as a derived record with its own provenance. A reviewer should be able to move from that output to its supporting evidence without relying on institutional memory or a retired application.
Keep time and relationships intact
Life sciences information is rarely meaningful in isolation. A result may only make sense in relation to a protocol amendment, a sample, a device configuration, an investigator approval or an earlier finding. Time matters too: event time, capture time, correction time and approval time can each answer a different question.
An evidence-ready design preserves these links rather than flattening them into a folder of files or a disconnected set of rows. It distinguishes the original record from a rendition, a corrected version from the prior version, and a cross-reference from a duplicate. It should also make clear which system was authoritative for a given stage of the lifecycle.
That does not mean every relationship needs to be modelled in one application. It means the connections that are needed to understand and defend a record must remain available, documented and retrievable.
Make verifiability an operating capability
Evidence is tested over time, often after the operational system has changed or disappeared. Storage alone cannot answer whether a record remained intact, readable and properly governed throughout that period.
The practical controls are familiar, but they need to operate together: integrity checks, controlled access, audit trails, retention and legal-hold governance, format and migration planning, and the ability to export the record with the context required for inspection. For digitally signed material, the preservation approach also needs to account for the validation evidence that will be needed after certificates, algorithms and services have evolved.
The real test is simple. Could an authorised reviewer understand what this record is, how it was handled, and why it can be relied upon, without reopening the original system or chasing former project members?
Build the governance around the use case
There is no single evidence architecture for every life sciences workload. A discovery dataset, a completed clinical-trial archive, a pharmacovigilance case and a retired quality system have different retention periods, access expectations and inspection risks.
Teams should therefore begin with a small set of priority use cases and work backwards from the questions they may need to answer later. Identify the authoritative sources, the evidence that must remain linked, the transformations that require traceability, and the controls that must survive a system change. Then make those requirements testable during implementation and migration.
This is a cross-functional job. Data science can define what is useful for analysis. Quality and regulatory teams can define what is defensible. IT and information governance can make sure the result remains accessible and controlled for as long as it is needed. AI becomes more useful when those disciplines meet early, rather than after a model has already produced results that nobody can fully explain.
The practical response is to preserve the records, context, relationships and proof that make information understandable over time.
Where Docbyte Vault fits
Docbyte Vault is relevant when these evidence requirements must outlive the source systems that created the information. Explore life sciences and GxP archiving, the practical discipline of application retirement, and the integration and preservation options in Docbyte Vault.
This post was inspired by AI in Drug Development is Not a Data Problem — It’s An Evidence Problem on MedCity News.
