Quick Answer
Clinical research depends on accurate, consistent data, but manual documentation and data entry can introduce errors that lead to avoidable sponsoring EDC queries, rework, and delays. Our Bright Idea initiative at the SCRS Global Site Solutions Summit 2026 focused on addressing these challenges earlier by integrating an AI-powered eSource quality-checking tool into our Clinical Trial Management System (CTMS) workflow. By flagging potential errors, inconsistencies, and missing information while Clinical Research Coordinators (CRCs) are documenting participant information, the approach helps teams correct issues before data reaches the sponsor’s Electronic Data Capture (EDC) system.
Key Takeaways
- Catch issues earlier: Identify potential documentation errors and inconsistencies while the CRC is completing eSource.
- Reduce avoidable EDC queries: Address preventable discrepancies before they move downstream into sponsor review.
- Improve collaboration: Create a smoother handoff between CRCs and the data entry team.
- Strengthen data quality: Support more accurate EDC submissions and consistent documentation.
- Put people first: Reduce avoidable rework so research teams can dedicate more time to participant safety, protocol compliance, and study execution.
- Make AI practical: Integrate intelligent quality checks into an existing workflow rather than creating an entirely new process for staff to manage.
A Small Error Can Create a Much Bigger Challenge
In clinical research, data quality is rarely determined by a single task. It depends on a chain of connected activities, from documenting participant information and reviewing source records to entering data into sponsor systems and responding to queries.
A small inconsistency at the beginning of this chain can create a surprisingly large amount of work later. A missing detail, an incorrect entry, or a mismatch between source documentation and the sponsor’s EDC system may trigger a query that requires investigation, clarification, correction, and follow-up. Each step consumes time that could otherwise support study conduct and participant care.
For clinical research sites, the challenge is not simply to resolve these queries efficiently. It is to prevent avoidable errors from progressing through the workflow in the first place.
That question inspired our Bright Idea initiative at the SCRS Global Site Solutions Summit 2026: What if quality checks happened earlier, while the information was being documented; rather than after it had already reached the next stage?
Our answer was to bring AI-powered quality checking directly into the existing CTMS workflow, helping the study team move from reactive query resolution toward proactive data quality management.
The Challenge: When Manual Processes Create More Work
Before implementing the AI-powered tool, our workflow involved two closely connected responsibilities.
First, our CRCs documented participant information in eSource. Next, our data entry team transferred that information into the sponsor’s EDC system.
Although each team had a defined role, the handoff between source documentation and EDC entry created opportunities for discrepancies. Manual data entry, inconsistent documentation, missing information, and questionable values could result in differences between what was recorded in the source and what appeared in the sponsor’s system.
These issues often became visible only after the data had moved further downstream.
When sponsors identified discrepancies, the resulting EDC queries required staff to revisit records, verify information, communicate across teams, make corrections when appropriate, and ensure that the issue was properly resolved. Even when an error was relatively small, the resolution process could involve several people and multiple steps.
The impact extended beyond the query itself.
- Additional administrative work: CRCs and data entry staff had to revisit previously completed tasks.
- Longer resolution cycles: Teams needed time to investigate discrepancies and communicate the correct information.
- Interrupted workflows: Query management competed with other study responsibilities.
- Avoidable operational pressure: Repeated corrections made it harder to devote attention to higher-value activities.
- Potential delays: Outstanding queries could complicate data review and other study milestones.
The problem was not a lack of effort from our teams. It was that important quality checks often occurred too late in the process.
We needed a way to identify potential problems closer to their source.
Our Bright Idea: Bringing AI-Powered Quality Checks into the Workflow
Our solution was to implement an AI-powered eSource quality-checking tool within our CTMS workflow. Rather than waiting for discrepancies to appear during sponsor review, the tool supports earlier identification of potential errors while the CRC is completing eSource documentation.
It is designed to flag information that may be incorrect, inconsistent, missing, or questionable, giving staff an opportunity to review and address potential issues before the information reaches the EDC data entry stage.
This approach changes where quality control happens.
Instead of relying primarily on downstream queries to reveal problems, the workflow introduces an additional opportunity to review information at the point of documentation.
The aim is straightforward: identify potential issues earlier, correct them through appropriate human review, and reduce the number of preventable discrepancies that travel through the clinical data process.
Importantly, AI is being used as a practical support tool for research staff—not as a replacement for their clinical judgment, accountability, or established study procedures.
How the AI-Enabled Workflow Makes a Difference
1. Identifying Potential Errors Before They Travel Downstream:
The earlier a discrepancy is identified, the easier it may be to investigate and address.
When the CRC is completing eSource, the AI-powered tool can flag potential problems such as missing information, inconsistencies, or entries that may require a closer look. Instead of discovering every issue after EDC entry or sponsor review, staff have an opportunity to assess flagged information closer to where it originated.
This creates an additional layer of quality oversight before the next workflow stage begins.
2. Reducing Avoidable EDC Queries:
Sponsor EDC queries are an important part of clinical data review. They help identify information that needs clarification and support the integrity of trial data. The goal is not to eliminate legitimate queries or discourage sponsors from raising important questions.
The opportunity lies in reducing queries that arise from preventable documentation or data transfer discrepancies.
By addressing potential issues earlier, the site can help reduce unnecessary back-and-forth, limit repeated work, and improve the quality of information submitted to the sponsor.
Fewer avoidable discrepancies also mean that staff can spend more time addressing substantive data questions rather than repeatedly correcting issues that could have been caught earlier.
3. Improving Collaboration Between CRCs and the Data Entry Team:
Accurate clinical data depends on effective collaboration.
CRCs understand the participants’ visit, source documentation, and study-specific requirements. The data entry team depends on clear, complete, and consistent source information to populate the sponsor’s EDC system accurately.
When potential issues are identified before handoff, both teams benefit from a more reliable starting point.
The result is an opportunity for clearer communication, fewer preventable corrections, and a smoother transition from documentation to data entry.
Rather than treating source documentation and EDC entry as separate tasks, the process reinforces their shared responsibility for data quality.
4. Supporting More Efficient Trial Execution:
Every avoidable query creates work that must be accommodated alongside existing study responsibilities.
By moving quality checks earlier, our initiative aims to reduce unnecessary rework and help staff use their time more effectively. The operational benefit is not limited to faster query resolution. It also involves protecting time for participant-facing responsibilities, protocol adherence, study coordination, and other activities essential to successful trial delivery.
This is particularly important in busy research environments, where teams balance multiple studies, deadlines, documentation requirements, and participant needs.
Reactive Query Resolution to Proactive Data Quality
The most important aspect of this initiative is the shift in mindset.
A reactive process identifies an issue after it has progressed through the workflow. A proactive process looks for opportunities to identify and address potential issues before they create downstream consequences.
Our AI-powered quality-checking tool supports this transition by bringing an additional review opportunity into the documentation stage.
It also reflects an important principle in clinical research technology: innovation does not always require replacing an entire system. Sometimes, meaningful improvements come from introducing the right capability into a workflow that teams already understand and use.
By integrating AI into the CTMS workflow, we are applying technology to a clearly defined operational challenge. The focus remains on practical value, better documentation, more consistent handoffs, fewer avoidable discrepancies, and a more efficient process for managing clinical trial data.
As implementation continues, monitoring outcomes such as query volume, query resolution time, recurring discrepancy types, and staff feedback can help evaluate the tool’s impact and identify further opportunities for improvement.
Our Site Spark Award Nomination
Did You Know?
Revival Research Institute’s work with AI extends to another challenge faced by research sites: building protocol-specific eSource documentation.
As highlighted in the 2026 SCRS Site Spark Award material, Revival developed ProtoScribe, a tool designed to generate eSource from study protocols and electronic case report form (eCRF) guidelines in minutes. Previously, the process could take two to three weeks from protocol receipt to an approved build.
ProtoScribe uses saved templates to create visit schedules, forms, and edit checks. A second, independent AI then reviews the build and flags items for the clinical team to assess. Human review and approval remain part of the process, with approved builds exportable for CTMS setup.
The workflow is designed to reduce the build timeline from two to three weeks to approximately two business days. It also applies rules across company, site, and therapeutic-area levels to promote consistency. According to the submitted material, ProtoScribe runs on a local large language model (LLM), so data is not passed to external AI.
This initiative highlights a related principle: thoughtful use of AI can help research teams improve consistency and reduce avoidable work while retaining human oversight.
Looking Ahead
Clinical research innovation is most meaningful when it addresses real challenges faced by the people conducting studies every day.
Our Bright Idea initiative began with a familiar problem: manual documentation and data entry could allow preventable discrepancies to progress downstream, creating additional sponsor queries and unnecessary rework. By introducing AI-powered quality checks earlier in the workflow, we are working toward a more proactive, consistent, and efficient approach to data quality.
The broader goal is not simply to use artificial intelligence because technology is available. It is to apply AI where it can support people, strengthen processes, and create measurable operational value.
For research sites, that means using technology to improve the quality of information moving through the clinical trial process while keeping human oversight and participant safety central to the work.
A better query process starts before the query exists. By identifying potential issues earlier, supporting collaboration, and making quality checks part of the workflow, our Bright Idea represents a practical step toward more efficient clinical research, and more time for the work that matters most: delivering high-quality studies with participants at the center.
