Non-eCRF Data Reconciliation in Clinical Trials: What It Is and Why It Matters

As clinical research evolves, the amount and variety of clinical trial data continue to grow. Much of this information now comes from multiple data sources beyond traditional forms and systems. This expansion adds value but also makes managing and reviewing the data more complex.

Some of the data collected in a clinical trial does not go through the usual electronic case report form (eCRF) or EDC systems. These alternative sources require special attention to ensure the data stays consistent, complete, and compliant.

That’s where data reconciliation comes in. The reconciliation process compares different datasets, flags inconsistencies, and helps maintain data integrity. It is an essential part of clinical data management, especially when dealing with information gathered outside of standard platforms.

What Is Non-eCRF Data?

Non-eCRF data refers to any clinical trial data that is not collected through a case report form (CRF) or an EDC (electronic data capture) system. Instead of being entered directly into structured forms, this information often comes from data sources outside the trial site’s main platform.

Common types of non-eCRF data include:

• Lab data from the central and safety laboratory data systems
  
• Imaging such as MRI and X-ray scans
  
• ePROs (electronic patient-reported outcomes)
  
• Wearables like smartwatches and fitness trackers
  
• ECOA, biomarkers, and genetic data
  

These data points offer a more comprehensive view of how patients respond to treatment. However, collecting non-eCRF data introduces additional complexity, as it is often managed by third-party systems, captured in varying formats, and stored outside the primary database.

While CRF data is usually entered by site staff through structured workflows, non-eCRF data is less standardized. This leads to more data exchange and standardization challenges. Because of this, there is a greater need for proper review and reconciliation to protect data integrity.

Why Reconcile Non-eCRF Data?

As trials grow more digital, the volume of non-eCRF data in clinical trials is rising. Information now comes from many vendors, platforms, and tools. This variety increases the chances of inconsistency and error, making effective processes like data reconciliation in clinical data management more important than ever.

Reconciling non-eCRF data is essential because:

• Conflicting values between data sources can reduce data quality.
  
• Missing or incorrect entries may cause problems with regulatory compliance.
  
• Unmatched records can lead to risks for patient safety.
  

A straightforward reconciliation process is needed to match and confirm this information. We do reconciliation to avoid an excessive process of resolving queries, which can slow down data review and compromise quality. This helps ensure that all clinical data aligns and teams can rely on one version of the truth throughout the study.

How Non-eCRF Data Reconciliation Works

Non-eCRF data can be reconciled in two ways: within the EDC system or outside it. Each approach depends on the data collection method, the number of different sources, and how the clinical team manages trial data across systems.

Within the EDC System

Some EDC platforms support direct data transfer from third-party vendors. In this setup, the vendor sends data based on a data transfer agreement, and the system loads it into the same space as eCRF data.

This method makes it easier to align datasets quickly and helps to streamline the reconciliation process. It reduces delays and supports real-time review. However, it may involve a complex setup. The incoming data must follow the exact format and validation requirements. If not, it can lead to errors or delays in data entry.

Outside the EDC System

In other cases, non-eCRF data is managed outside the EDC platform. Here, eCRFs and external sources are gathered into separate datasets. These are compared against each other using validation rules defined in the study plan.

The data management team reviews any mismatches. They check which data point is correct and decide how to resolve the issue. This method allows more flexibility, especially when working with multiple vendors and data sources. However, it can take more time and may increase the chance of making errors, especially if the process is manual.

Key Challenges in Reconciling Non-eCRF Data

Reconciling non-eCRF data is not always simple. Because this data comes from different sources and systems, it often introduces new obstacles for the clinical team. Below are some of the most common challenges faced during the reconciliation process.

1. Data Format Inconsistencies

When multiple vendors are involved, each may use a different format or structure for their files. This makes it hard to compare or combine information. Without standardization, the data may not match or fit the expected layout, leading to delays in review and extra work for data management.

2. Lack of Interoperability

Not all systems are designed to work together. If platforms cannot exchange or align information properly, data transfer problems and duplication are created. This slows the timeline and increases the chance of errors, especially when datasets are being moved across platforms.

3. Security and Privacy Concerns

Some types of non-eCRF data, such as genomic data ¹ and ePROs, are sensitive. They require extra protection. If data protection steps are not in place, it could lead to a loss of patient trust or even a breach. Strong data security and clear data transfer agreements are essential for safe handling.

4. Time-Consuming Manual Processes

In many cases, teams still rely on manual reconciliation. They check records by hand and send corrections back to the vendor. These feedback loops take time and may delay trial results. If the study involves many files or datasets, the process can become too slow to support real-time decisions.

5. Data Quality and Integrity Risks

Without proper checks, there’s a risk of missing out on errors, such as wrong dates, missing fields, or duplicate records. These issues can affect the safety review and the reliability of trial data. In worst cases, poor data quality can impact safety and efficacy assessments and even lead to trial delays or incorrect outcomes.

Final Thoughts on Non-eCRF Data Reconciliation

As clinical trials become more complex, reconciling non-eCRF data has never been more critical. This data type plays a growing role in understanding treatment outcomes, but its variety and structure introduce new risks to data quality, regulatory compliance, and patient safety.

To manage these challenges, the reconciliation process must evolve. It’s no longer enough to rely on manual reviews or disconnected tools. Today’s clinical studies require more intelligent, more connected systems that can handle data transfer, validation, and data security across many different sources, all while protecting participant privacy and ensuring accurate trial results.

That’s where platforms like CDConnect make a difference. Designed for modern research needs, CDConnect helps clinical teams streamline workflows, unify patient data from wearables and remote devices, and maintain strong data integrity from start to finish. 

With real-time monitoring, seamless integration, and reliable compliance features, it supports data management and study success, especially in complex or decentralized trials.

FAQs About  Non-eCRF Reconciliation

What Types of Studies Benefit Most From Non-eCRF Data Reconciliation?

Non-eCRF data reconciliation is especially important in decentralized trials, hybrid studies, and those involving real-world evidence. These studies often rely on data from wearables, home monitoring devices, and external labs, where ensuring data consistency is critical to support accurate analysis and decision-making.

How Often Should Non-eCRF Data Be Reconciled During a Clinical Trial?

The frequency of reconciliation depends on the study design, data volume, and regulatory requirements. In most cases, ongoing or scheduled reconciliation is recommended to avoid backlogs and reduce the risk of inconsistencies affecting interim analyses or safety monitoring.

Who Is Responsible for Reconciling Non-eCRF Data?

Data management teams typically oversee non-eCRF data reconciliation. However, this task may involve collaboration with vendors, clinical operations, and site teams to confirm discrepancies, resolve issues, and ensure data integrity across all platforms.

Can Automation Replace Manual Non-eCRF Data Reconciliation Completely?

While automation tools can significantly reduce manual effort and speed up the process, full automation is rarely enough. Human oversight is still needed to interpret complex discrepancies, confirm source accuracy, and apply clinical judgment, especially when dealing with unstructured or sensitive patient data.

What Happens if Non-eCRF Data Is Not Properly Reconciled?

Failure to reconcile non-eCRF data can lead to regulatory delays, incorrect conclusions about safety and efficacy, or missed signals in trial results. It also increases the risk of submitting incomplete or inconsistent data, which can impact trial approvals and patient safety.

Sources:

1. https://www.genome.gov/about-genomics/fact-sheets/Genomic-Data-Science 

CDConnect Team

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