JMIR Publications

ABSTRACT

Background:

Data transfer between electronic health records (EHR) at the point-of-care and EDC systems (electronic data capture) for clinical research is still mainly carried out manually which is error-prone as well as cost- and time-intensive. Automated digital transfer from EHR to EDC (EHR2EDC) would enable more accurate and efficient data capture, but has so far encountered technological barriers primarily related to data format and technological environment: In Germany, health care data are collected at the point-of-care in a variety of often individualized practice management systems (PMS), most of them not interoperable. At the same time, data quality for research purposes within EDC systems must meet the requirements of regulatory authorities for standardized submission of clinical trial data and safety reports.

Objective:

To develop, as part of the observational FINE-REAL study, a model for automated data transfer that allows data of sufficient quality to be captured at the point of care, extracted from various PMS systems, and automatically transferred to electronic case report forms of EDC systems. This required addressing aspects of data security as well as lack of compatibility between EHR health care data and the data quality required in EDC systems for clinical research.

Methods:

The SaniQ software platform (Qurasoft GmbH, Koblenz, Germany) is already used to extract and harmonize predefined variables from electronic medical records of different Compu Group Medical-hosted PMS. From there, data are automatically transferred to the validated AlcedisTRIAL EDC system for data collection and management. Synchronization from EHR2EDC occurs automatically overnight, also real-time update can be initiated manually following each data entry in the EHR. The CRF contains 13 forms with n=274 variables. Thereof, 5 forms with n=185 variables contain n=67 automatically transferable variables (24% of all and 36% of eligible variables).

Results:

This model for automated data transfer bridges the current gap between clinical practice data capture at the point-of-care and the datasets required by regulatory agencies and enables an automated data transfer from EHR2EDC in compliance with General Data Protection Regulation. It addresses feasibility, connectivity, and system compatibility of currently used PMS in health care and clinical research and is therefore directly applicable.

Conclusions:

This use case demonstrates that secure, consistent and automated end-to-end data transmission from the treating physician to the regulatory authority is feasible and can contribute to a more efficient use of health data for clinical research. Automated data transmission can be expected to reduce effort and save resources and costs while ensuring high data quality. This may facilitate the conduct of studies for both study sites and sponsors, thereby accelerating the development of new drugs. Nevertheless, the industry wide implementation of EHR2EDC requires policy decisions that set the framework for the use of research data based on routine PMS data.