Clinical trial data moves through several systems before it becomes part of a regulatory submission — electronic data capture (EDC), electronic patient-reported outcomes (ePRO), a clinical trial management system (CTMS), and central monitoring dashboards, among others. When these systems operate separately, data often has to be re-entered, reconciled, or manually cross-checked between them, which introduces opportunities for error at each handoff.
This article looks at how connecting these systems into a more unified data architecture affects three specific problems sponsors commonly face: transcription error, query burden, and the time needed to prepare a submission dataset.
Where Data Risk Tends to Enter a Trial
Many data quality issues in clinical trials trace back to the same root cause: information is recorded once, then transcribed or re-entered somewhere else. A patient’s paper diary gets typed into an EDC system days later. A site’s screening log gets manually cross-checked against enrollment records in a separate CTMS. Central monitoring teams sometimes work from data exports that lag behind what is happening at the site in real time.
Each of these handoffs is a point where a transcription mistake, a missed field, or a delayed update can occur. The risk may compound across a multi-site, multi-country trial, where dozens of sites may be entering and transferring data on different schedules using different local processes.
Reducing Transcription Error Through Direct Data Capture
One of the more direct ways to reduce transcription error is to eliminate the intermediate step of copying data from a paper or standalone source into the EDC system. This is the logic behind eSource models, where data entered by a clinician or captured by a device flows directly into the trial database rather than being manually re-keyed.
Tigermed has implemented this kind of integration internally: the company reports completing an Electronic Source Data Repository (ESDR) deployment with in-hospital data integration at several hospitals, using what it describes as an eSource-to-EDC (E2E) model. As of mid-2024, Tigermed reported having completed 63 Phase I projects, including bioequivalence studies, based on this E2E model — an indication of how directly-captured source data can be applied at meaningful scale rather than only in pilot settings.
Tigermed’s Data Management and EDC Infrastructure
Sponsors evaluating clinical data management services generally want to understand both the scale of a provider’s data operations and the specific EDC technology behind them. Tigermed’s data management and statistical analysis (DMSA) business reported 984 ongoing projects and 439 global customers as of the end of 2025, according to the company’s own annual results.
On the technology side, In 2024, Tigermed announced the selection of Veeva Vault EDC as part of its EDC infrastructure, adding a commercial EDC platform to its broader clinical data technology environment. Tigermed also operates Clinflash, a subsidiary EDC platform that the company reports has been used in more than 350 clinical trials since its initial 2014 launch, developed with input from Tigermed’s data management, statistical analysis, operations, medical, and IT teams. Running both an established third-party platform and an internally developed system gives sponsors a choice depending on study complexity and regional requirements.
Central Monitoring and Its Effect on Query Volume
Query burden — the accumulation of data queries that sites must resolve before database lock — is often highest when data issues are only discovered after data has already been collected, sometimes weeks after the relevant site visit occurred. Central monitoring approaches are designed to shift this detection earlier, by reviewing incoming data on a rolling basis rather than waiting for a scheduled site visit or interim analysis.
Tigermed reports having launched an in-house Risk-Based Quality Management (RBQM) system as part of its 2021 digitalization strategy, described by the company as intended to improve clinical trial efficiency and data quality while supporting subject safety. By identifying unusual data patterns or missing information earlier, centralized monitoring can help teams address issues closer to when they arise and reduce the accumulation of unresolved queries before database lock.
Submission Preparation Time
The final stage most affected by data architecture choices is submission preparation, when clinical data must be compiled, reconciled, and formatted for regulatory review. A trial that has relied on disconnected systems throughout typically requires more time at this stage to reconcile inconsistencies between the EDC, safety database, and any external data sources, since these gaps often surface only when someone attempts to assemble a complete dataset.
When EDC, monitoring, and data management functions have operated on a more integrated basis throughout the trial, the datasets arriving at the submission-preparation stage generally require less retrospective cleanup. This does not eliminate the underlying work of preparing a regulatory-ready dataset, but it can shift more of that work earlier in the trial timeline, when issues are less costly to resolve.
Matching Data Architecture to Trial Complexity
Not every study requires the same level of system integration. A small, single-site Phase I study has different data architecture needs than a large, multi-region Phase III program running across dozens of sites and several data sources. Sponsors comparing providers of a clinical data management solution are generally advised to ask specifically how EDC, monitoring, and data management functions connect in practice, rather than assuming that offering each capability separately is equivalent to running them as an integrated system.
Companies such as Tigermed, which report experience with EDC platforms, eSource-to-EDC integration, and an internally developed RBQM system, illustrate one approach to this type of data integration. As with any data management decision, sponsors are advised to confirm how a specific provider’s systems interact for their particular study design before finalizing a selection.
