Source Data Verification (SDV) confirms that clinical trial data matches its source. Modern SDV can be configured around critical data and study risk, performed remotely where appropriate, and supported by AI to extract, compare, and prioritize information for human review.

Source Data Verification, or SDV, is the process of comparing data reported in a clinical trial system with the source information from which that data was derived. Its purpose is to confirm that information entered into an electronic case report form (eCRF) or Electronic Data Capture (EDC) system accurately reflects the supporting source record.

Source information may come from electronic health records, laboratory reports, clinical notes, medical charts, medication records, imaging reports, paper source documents, and other records generated during participant care or trial conduct.

For example, if a laboratory result is entered into the EDC, SDV may involve comparing that entry with the original laboratory report to confirm that the value, date, unit, and other relevant details were entered correctly.

SDV is one component of clinical trial monitoring. Monitoring more broadly also considers participant safety, protocol compliance, study conduct, and the reliability of the resulting data.

SDV vs Source Data Review

Source Data Verification and Source Data Review are related, but they answer different questions.

A procedure date, for example, may have been entered correctly into the EDC and therefore pass SDV. Source Data Review may still identify that the procedure occurred outside the protocol-defined visit window.

This distinction matters because accurate transcription alone does not address every risk within a clinical trial.

Traditional SDV often applied broad verification across large volumes of trial data. A configurable approach lets sponsors define where SDV is required, rather than applying the same level of verification everywhere.

The starting point is usually the protocol and study risk assessment. Data that are important to participant safety or study conclusions may require greater verification than lower-risk supporting data.

For example, an SDV plan may place greater emphasis on eligibility criteria, critical safety information, primary endpoints, or treatment-related data. Verification requirements can also differ by site, participant, visit, form, or individual field.

The plan does not necessarily have to remain fixed throughout the trial. Monitoring findings may justify changing verification intensity.

For example, repeated eligibility errors, unusual missing-data patterns, protocol deviations, delayed safety reporting, or recurring discrepancies may indicate that a site or data area requires additional attention. Conversely, consistently acceptable performance may support maintaining the planned level of verification without unnecessary expansion.

This is the practical role of risk-based SDV: using criticality and emerging study risk to determine where verification effort should be concentrated.

The important point is that a risk signal should lead to a defined assessment rather than automatically changing the SDV plan.

Depending on the issue, the appropriate response may be additional SDV, broader Source Data Review, site communication, retraining, centralized review, or an onsite visit.

In this model, Risk-Based Monitoring is broader than risk-based SDV. SDV is one monitoring control alongside centralized monitoring, data analytics, SDR, site communication, and targeted onsite activities.

Where Remote SDV Fits

Once the study has defined what requires verification, the next consideration is how that verification will be performed.

Some SDV can be completed remotely when the site, technology, privacy requirements, and monitoring plan support it. This may involve appropriately restricted read-only access to electronic source records, guided remote review with site personnel, or controlled source-document exchange.

Remote SDV therefore does not represent a separate verification strategy. It is an execution method for performing the SDV already defined by the monitoring plan.

A risk-based study may use both remote and onsite SDV depending on the source information being reviewed and the nature of the monitoring need.

The next step is determining where technology can make that process more efficient.

The strongest use case for AI in SDV is not to independently decide whether clinical trial data is correct. It is to help monitors process source information, find possible discrepancies, and focus attention on records that require closer review.

AI can support several stages of this workflow.

Extracting Data From Source Records

Much of the information used for SDV is not available in a clean structured format. It may appear in progress notes, laboratory reports, scanned records, discharge summaries, or other clinical documents.

Technologies such as Optical Character Recognition and Natural Language Processing can help extract relevant information such as laboratory values, dates, medications, diagnoses, procedures, and clinical observations.

The extracted information can then be prepared for comparison with trial data.

Normalizing and Linking Source Information

Source records and EDC data may describe the same information differently. Units, terminology, dates, or medication names may need to be standardized before two values can be meaningfully compared.

The information must also be associated with the correct participant, visit, event, form, and EDC field.

This step is particularly important. An accurately extracted value linked to the wrong participant or visit can create a more serious problem than failing to extract the value at all.

Ambiguous associations should therefore be routed for human review rather than accepted automatically.

Comparing Source and EDC Data

Once the relevant source information has been identified and linked, automated logic can compare it with the corresponding EDC entry and highlight potential mismatches.

The system should surface the relevant evidence rather than simply label every difference as an error. Differences may result from timing, unit conversion, clinical interpretation, or other legitimate reasons that require context.

Prioritizing Records for Review

AI and analytics can also identify patterns across larger volumes of trial data.

Repeated discrepancies, unusual missingness, unexpected data-entry timing, or abnormal site-level patterns may indicate records or sites that warrant additional attention.

AI assistance does not remove the need for human oversight.

Information extracted or interpreted by AI should not automatically be treated as the original source. When a system identifies a possible discrepancy, the reviewer should be able to examine the supporting source information and understand what was compared.

This means an authorized reviewer should be able to determine where the information originated, what the system identified, and what action was ultimately taken.

The same principle applies to AI uncertainty. If the system cannot confidently associate a source value with the correct participant or visit, that uncertainty should be visible rather than hidden.

Human review becomes especially important for data related to eligibility, safety, treatment, endpoints, or other critical study decisions.

Remote access also introduces its own controls. Users should only be able to access source information appropriate to their role, and blinded or unrelated patient information should remain protected.

For AI-assisted SDV, efficiency therefore cannot be separated from traceability. A workflow is only useful if it makes review faster without making the underlying decision harder to reconstruct.

When sponsors and CROs evaluate technology for SDV, broad labels such as “AI-powered” or “remote monitoring” reveal relatively little about how the system actually works.

A more useful evaluation focuses on the workflow.

The objective should not be to automate the highest possible percentage of SDV.

The more meaningful question is whether the technology helps monitors identify and review important information efficiently while maintaining transparency and control.

Source Data Verification continues to serve the same fundamental purpose: confirming that reported clinical trial data is supported by its source.

What is changing is how precisely that verification can be planned and how efficiently it can be performed.

Configurable SDV allows the monitoring plan to specify where verification is required. Risk-based approaches allow those requirements to respond to data criticality and emerging study findings. Remote access can provide another way to perform the required review, while AI can help process source information, identify potential inconsistencies, and prioritize records for human attention.

These capabilities should work together rather than operate as separate strategies.

The aim is not simply to reduce SDV or replace monitors with automation. It is to make verification more focused, responsive, and efficient while preserving the source evidence, human judgment, and traceability required for reliable clinical trial oversight.

Clinion rSDV 

Clinion EDC supports remote Source Data Verification (rSDV) with AI-assisted review capabilities that help streamline the comparison of source information with data recorded in the EDC. AI can assist by extracting relevant information from source records, aligning it with the corresponding study data, and highlighting potential mismatches for monitor review. This helps reduce repetitive manual comparison while keeping the final verification and interpretation with the authorized reviewer.