From Structured Capture to Unstructured Insight: 10 Questions on the Next Phase of eSource

Electronic source data (eSource) has evolved from a paper-replacement strategy into a broader modernization effort aimed at integrating clinical care and research workflows.

As electronic health record (EHR)-to-electronic data capture (EDC) connections mature, attention is shifting toward activating unstructured data, implementing artificial intelligence (AI) responsibly, and building validation frameworks that can withstand regulatory scrutiny.

Based on recent discussions across sponsors, sites, and technology providers, here are 10 questions shaping the next phase of eSource.

1. What does eSource encompass today?

eSource refers to the direct digital capture of clinical data at its origin. This includes structured data from electronic health records (EHRs), laboratory systems, electronic patient-reported outcome (ePRO) tools, wearable devices, and digital consent platforms.

The goal is to reduce duplicate entry, improve accuracy, and accelerate the flow of high-quality data into clinical trials. Rather than maintaining separate documentation systems, eSource seeks to align research capture with routine care workflows wherever feasible.

2. Why has EHR-to-EDC become the leading model?

EHR-to-EDC integration addresses one of the most persistent operational pain points: manual transcription. By securely transferring structured data into trial databases, sponsors and sites can reduce administrative burden and minimize transcription errors.

These efficiencies can shorten timelines to database lock and improve compliance, making this pathway an attractive and measurable starting point for eSource implementation.

3. Why are different “flavors” of eSource necessary?

Implementation varies depending on site infrastructure. Large academic health systems often have mature EHR environments and embedded research capabilities.

Independent research sites, however, may rely on protocol-specific eSource tools tailored to study workflows. Recognizing these differences is essential. A flexible approach ensures that eSource strategies accommodate both integrated health systems and standalone research sites without imposing unrealistic technical demands.

4. Where does AI enter the picture?

AI becomes relevant once structured data pipelines are established. It can support automated quality checks, flag inconsistencies, and help prioritize monitoring activities. Importantly, AI is positioned as an augmentation tool rather than a replacement for human oversight.

Its effectiveness depends on stable integrations and clearly defined governance processes.

5. What governance concerns accompany AI use?

AI integration raises questions around validation, transparency, and accountability. Stakeholders emphasize the need for human-in-the-loop review, documented quality assurance procedures, and risk assessments to ensure data integrity.

Without formal oversight structures and traceable workflows, AI-enabled systems may struggle to gain regulatory trust or broad institutional adoption.

6. Why is unstructured data considered the next frontier?

A significant portion of healthcare data—often cited as more than 80%—exists in unstructured formats such as clinical notes and imaging reports. In therapeutic areas such as oncology and rare disease, key trial variables may reside exclusively in narrative documentation.

If these sources remain untapped, eSource efforts capture only part of the available evidence, limiting the potential for comprehensive digital workflows.

7. How can unstructured data become usable for trials?

Technologies such as natural language processing and multimodal AI can extract relevant elements from narrative text and convert them into structured formats. Interoperability standards, including HL7 FHIR and common data models, support mapping and normalization.

However, extraction must be paired with validation layers, audit trails, and provenance tracking to ensure outputs are reliable and reproducible.

8. Can AI-derived data satisfy regulatory expectations?

Regulators emphasize traceability, auditability, and reproducibility. While AI holds promise for improving efficiency, formal validation frameworks are still developing.

Demonstrating that AI-generated outputs can be reviewed, explained, and consistently reproduced will be central to regulatory confidence. Continuous monitoring and transparent documentation are likely to remain core requirements.

9. Are all institutions equally prepared for advanced eSource?

Readiness varies widely. Academic centers may have dedicated informatics teams capable of managing integrations and AI validation.

Smaller hospitals and independent sites may lack similar resources. To avoid widening disparities in trial participation, sponsor-supported services and vendor-enabled validation solutions may be necessary to reduce implementation burden.

10. What will determine whether eSource scales sustainably?

Long-term scale will depend on collaboration among sponsors, vendors, sites, and regulators. Shared standards, neutral governance models, and consistent documentation practices can transform isolated pilots into repeatable systems.

Without alignment, adoption may remain fragmented. With coordinated effort, eSource has the potential to evolve from digitization initiative to interoperable research infrastructure.

As structured integrations mature and unstructured data becomes increasingly accessible through AI-assisted tools, the industry’s focus is shifting from simple digitization to validation and trust. The next phase of eSource will be defined not only by technological capability, but by the governance and collaboration required to scale it responsibly.

Editor’s note: This FAQ article was generated based on two previous pieces of Applied Clinical Trials content—Unlocking Unstructured Health Data: Scaling eSource-Enabled Clinical Trials, SCOPE Summit 2026: Aligning AI Oversight With Strong eSource and EHR Foundations, and SCOPE Summit 2026: Understanding the Different Flavors of eSource.