# Accelerating eSource scale-up in oncology clinical trials: The i~HD Task Force initiative

## TL;DR

This paper introduces a collaborative initiative to overcome barriers in using electronic health records for clinical trials, aiming to improve efficiency and data quality in oncology research.

## Contribution

The paper presents a consensus-driven roadmap and foundational resources for scaling eSource in oncology clinical trials.

## Key findings

- The i~HD Task Force developed readiness criteria, a performance indicator framework, and an operational playbook for eSource adoption.
- The initiative emphasizes interoperability, regulatory alignment, and real-world validation for successful implementation.
- The framework is adaptable to other therapeutic areas with complex data workflows.

## Abstract

eSource – particularly EHR-to-EDC – is an emerging paradigm in clinical research that enables automated transfer of electronic health record (EHR) data into electronic data capture (EDC) systems, with the potential to reduce site burden, improve data quality and accelerate oncology clinical trial workflows. However, widespread implementation remains limited due to technical, regulatory and operational barriers. To address these challenges, the European Institute for Innovation through Health Data (i~HD) launched the eSource Scale-Up Task Force in 2024. This multi-stakeholder initiative brings together leading oncology centres and pharmaceutical sponsors to establish a consensus-driven roadmap for eSource adoption. Central to this effort are three foundational resources: readiness criteria for early adopters, a performance indicator framework for monitoring success and an operational playbook to guide implementation. This article provides a structured overview of the Task Force’s objectives, collaborative model and outputs, with specific attention to its focus on interoperability, regulatory alignment and real-world validation. While initially developed for oncology, the Task Force’s framework is applicable across therapeutic areas characterized by data-intensive workflows.