Reconciliation is the quality control activity that confirms data held in two different systems agrees before a clinical trial database is locked. Throughout a trial, data about the same participant is collected and stored in more than one place — the clinical database, a separate safety database, a central laboratory system, and various external vendor systems. Reconciliation is the disciplined comparison that makes sure these independent records tell the same story.
It is one of the most important safeguards against a locked database that looks complete but contains contradictions. This guide explains what reconciliation involves, why it matters, and how the three most common types — serious adverse event (SAE), laboratory, and external vendor reconciliation — are carried out in practice.
What is reconciliation in clinical data management?
Reconciliation is the process of comparing two datasets that should contain the same information and identifying, investigating, and resolving any differences between them. In clinical data management it almost always means comparing data in the clinical database — the electronic data capture (EDC) system where site-entered data lives — against data held in another system that received or generated the same information independently.
The reason this is necessary is structural. A serious adverse event, for example, is recorded by the site in the clinical database and also reported into a separate pharmacovigilance safety database, often through different routes and by different people. Both records describe the same event, but nothing guarantees they match. Reconciliation closes that gap before the data is used for analysis.
Why reconciliation matters
The integrity of a trial's conclusions depends on internal consistency. If the safety database lists an event as fatal while the clinical database records the participant as recovered, that contradiction must be found and resolved — not discovered by a regulator after submission. Reconciliation directly supports the ALCOA+ principles of accurate and consistent data, and unresolved discrepancies between systems are a recurring source of inspection findings.
Reconciliation is also a prerequisite for database lock. Declaring a clean file means confirming that data collection, cleaning, consolidation, and reconciliation are complete and that any residual issues are documented and accounted for. A database cannot be credibly locked while known cross-system discrepancies remain open.
SAE reconciliation
SAE reconciliation compares serious adverse event data recorded in the clinical database against the same events in the safety (pharmacovigilance) database. Because the two databases are populated independently — the clinical side through the eCRF, the safety side through expedited reporting workflows — differences are common and expected.
The comparison focuses on the fields that define the event and its clinical significance: the event term, onset and resolution dates, seriousness criteria, severity, outcome, causality assessment, and the action taken with the study drug. A reconciliation listing pairs the records and highlights where they disagree. Each discrepancy is then investigated with the data management, safety, and medical teams, and corrected in whichever system holds the error — though, because the investigator owns the trial data, any correction on the clinical side is raised as a query for the site rather than made by the sponsor directly. SAE reconciliation runs periodically through the trial and is completed in full before database lock, because safety data carries the highest consequence of any data in the study.
Laboratory data reconciliation
Most trials use a central laboratory that transfers results electronically into the clinical data environment on an agreed schedule. Laboratory reconciliation confirms that what the lab sent and what the clinical database holds are aligned — that every expected sample is present, that none are duplicated, and that key identifiers match.
The comparison typically checks participant and visit identifiers, sample collection dates, the presence of expected results, and units of measure. A frequent issue is a mismatch between the sample date or visit recorded at the site and the one supplied by the lab, which must be investigated rather than assumed away. Where a trial also uses local laboratories whose results are entered at the site, those entries are checked against source documentation as part of the same quality effort.
External and vendor data reconciliation
Modern trials draw data from a growing number of external vendors: ePRO and eCOA providers, interactive response technology (IRT/RTSM) for randomisation and drug supply, imaging and ECG core labs, and specialist biomarker providers. Each delivers a dataset that must agree with the clinical database on the data points they share.
External data reconciliation confirms that these independent datasets line up — that the participants, visits, and key shared variables match across systems, and that no records are missing, duplicated, or orphaned. The foundation for this is laid during set-up: a data transfer agreement and transfer specification define exactly what each vendor will send, in what format, and how often. Reconciliation then verifies, throughout the trial, that the data actually received conforms to that specification and agrees with the clinical record.
How reconciliation works in practice
Although the systems differ, the method is consistent across all three types. The expected datasets and the fields to be compared are defined in advance, usually in the data management plan or a dedicated reconciliation plan. The two datasets are then compared, most often programmatically, to produce a listing of matches and discrepancies. Each discrepancy is investigated to find its cause, resolved through a correction or a documented explanation — raising a query to the site where site data is the source of the error — and the resolution is recorded.
Two principles separate reconciliation that works from reconciliation that creates a last-minute crisis. First, it is continuous: running comparisons at regular intervals throughout the trial keeps the discrepancy count manageable, whereas leaving it all until close-out guarantees a bottleneck. Second, it is documented: the comparison, the discrepancies found, and how each was resolved form part of the evidence that the database was clean at lock.
Frequently asked questions
What is reconciliation in clinical data management?
Reconciliation is the comparison of two datasets that should contain the same information — typically the clinical database against a safety, laboratory, or vendor system — to identify and resolve any differences between them. It confirms that independently held records of the same data agree, and it is completed before database lock as part of declaring a clean file.
What is SAE reconciliation?
SAE reconciliation compares serious adverse event data in the clinical database against the same events in the safety (pharmacovigilance) database. It checks fields such as event term, dates, seriousness, outcome, and causality, because the two databases are populated independently. Discrepancies are investigated with the data management, safety, and medical teams and resolved before lock, given the high consequence of safety data.
What is the difference between lab and vendor reconciliation?
Laboratory reconciliation confirms that data from a central or local laboratory agrees with the clinical database, checking identifiers, sample dates, results, and units. Vendor reconciliation applies the same logic to other external providers — ePRO, IRT, imaging, ECG, or biomarker vendors — confirming that each external dataset matches the clinical record on the variables they share.
When does reconciliation happen in a trial?
Reconciliation is an ongoing activity during the conduct stage rather than a single task at the end. Running comparisons at regular intervals keeps the number of open discrepancies manageable. A final, complete reconciliation is then performed before database lock, because no database can be credibly locked while known cross-system discrepancies remain unresolved.
Why is reconciliation important?
Reconciliation prevents a locked database that appears complete but contains contradictions between systems — for example a safety database and clinical database that disagree on an event outcome. It supports the ALCOA principles of accurate and consistent data, and unresolved cross-system discrepancies are a recurring source of regulatory inspection findings.
Build the full picture with TriTiCon
Reconciliation sits within the wider conduct and close-out stages of clinical data management, alongside data cleaning, consolidation, and the declaration of a clean file. TriTiCon's training covers it in that context: The Clinical Data Management Conduct Process covers data cleaning and consolidation, and The Clinical Data Management Close-out Process covers completing data handling and declaring the clean file. Each module defines the key concepts, highlights regulatory requirements, and shows you how to apply the fundamentals across different trial and company settings.
