The Case for Early Data Management Input in eCOA

Jul 11, 2025 | Michelle Marlborough

The life science industry stands at a critical point in its approach to electronic clinical outcome assessment (eCOA) data. Despite the technology’s maturity, eCOA adoption lags behind other clinical trial systems. While the shift away from paper-based methods has brought advancements, there is scope to improve eCOA data capture and management strategies. These limitations contribute to restricted scalability, escalating costs, and protracted timelines in clinical trials. A significant factor in these challenges is the belated involvement of data managers in the eCOA process.

Traditionally, data management’s role has been largely back ended, working with the data once collected; their strategic input earlier in the process holds the key to better upfront decisions and streamlined downstream processes. A fundamental shift in our view of eCOA data management and a re-evaluation of when and where each stakeholder should contribute their expertise is required.

Rethinking the current approach

The clinical data landscape has changed considerably over the last decade. Historically, the majority of data came from site-based entry and all data was handled in a similar way, regardless of source.

As technology evolves, the amount of data collected through electronic data capture systems (EDCs) is reducing while the eCOA proportion of the data set is increasing. This is in part due to the move away from paper, but also because the industry is realizing the value of the patient voice in clinical research. Therefore, the design, capture, and management of eCOA is becoming more important. However, eCOA systems and processes haven’t kept pace with tools like EDC. Why?

The current model for eCOA implementation means systems are adopted on a study-by-study basis, impacting standardization across studies. A lack of data flow from siloed systems leads to poor data access, and delayed data manager involvement often leads to sub-optimal data structure, increasing work later in the process. Together with a heavy reliance on eCOA vendors, these challenges result in minimal scalability, longer timelines, increased cost, and greater burden on sites and patients.

Changing mindsets about how eCOA is designed, delivered, and managed can be daunting. Taking a new approach that enables eCOA standardization across studies overcomes the traditional barriers of slow builds, limited data access, and onerous data management processes.

Standardizing the three pillars of eCOA data management

Building a better eCOA that reduces the burden on study teams and improves patient experience relies on standardizing the three pillars of eCOA data management:

• Data capture
• Data structure
• Data flow

Data capture

Data managers are often not included in eCOA planning because it usually focuses on the protocol science — which instruments to use, their frequency, etc. Involving data managers early provides the opportunity to optimize system configuration. They can also recommend pragmatic approaches to improve the capture of study-critical data by building flexibility into the system design, balancing patient and site burden against scientific need. For example, setting realistic and effective notification schedules to prevent notifications from becoming background noise for patients and creating more lenient availability windows that prioritize late data over no data at all.

Data structure

Traditionally, study teams focus on how the patient experiences the questionnaire, often overlooking the underlying data structure. While the presentation layer is a crucial, clinically validated component, it is not the only aspect that matters. By starting with the end in mind, data managers can apply their expertise to the data structure, field naming, and extraction processes, removing the need for burdensome data mapping and transformation downstream.

Data flow

Given that eCOA is source data and, in most cases, can’t be changed, an ‘audit trail style’ review may be more appropriate to make sure there are no systemic issues like errors in design or patterns that indicate fraudulent activity. Early and frequent access to data is therefore crucial so individual discrepancies can be raised quickly with the data submitter to clarify the response within a reasonable recall period.

“The value is the data flow, the connections, the efficiencies, the workflows, and capturing end-to-end data and insights into the whole process.” Head of Data Office, global biopharma company

Moving to an upfront mindset

Data management skills and mindset should be capitalized on and involved in eCOA in a way they haven’t historically. Switching from a study-by-study approach towards cross-study standardization will enable teams to introduce consistency across studies, regardless of the system. Reusing validated instruments not only reduces build times, it also enables consistency across data standards.

The fundamental principles of data management — ensuring data is complete and clean — are important for all types of data. It is essential to consider the eCOA data flow, access, and structure from the start, just as we do for other systems.