TransFAIR study: a European multicentre experimental comparison of EHR2EDC technology to the usual manual method for eCRF data collection.

PURPOSE: Regulatory authorities including the Food and Drug Administration and the European Medicines Agency are encouraging to conduct clinical trials using routinely collected data. The aim of the TransFAIR experimental comparison was to evaluate, within real-life conditions, the ability of the Electronic Health Records to Electronic Data Capture (EHR2EDC) module to accurately transfer from EHRs to EDC systems patients' data of clinical studies in various therapeutic areas. METHODS: A prospective study including six clinical trials from three different sponsors running in three hospitals across Europe has been conducted. The same data from the six studies were collected using both traditional manual data entry and the EHR2EDC module. The outcome variable was the percentage of data accurately transferred using the EHR2EDC technology. This percentage was calculated considering all collected data and the data in four domains: demographics (DM), vital signs (VS), laboratories (LB) and concomitant medications (CM). RESULTS: Overall, 6143 data points (39.6% of the data in the scope of the TransFAIR study and 16.9% when considering all data) were accurately transferred using the platform. LB data represented 65.4% of the data transferred; VS data, 30.8%; DM data, 0.7% and CM data, 3.1%. CONCLUSIONS: The objective of accurately transferring at least 15% of the manually entered trial datapoints using the EHR2EDC module was achieved. Collaboration and codesign by hospitals, industry, technology company, supported by the Institute of Innovation through Health Data was a success factor in accomplishing these results. Further work should focus on the harmonisation of data standards and improved interoperability to extend the scope of transferable EHR data.

Performances of a Solution to Semi-Automatically Fill eCRF with Data from the Electronic Health Record: Protocol for a Prospective Individual Participant Data Meta-Analysis.

Clinical trial data collection still relies on a manual entry from information available in the medical record. This process introduces delay and error risk. Automating data transfer from Electronic Health Record (EHR) to Electronic Data Capture (EDC) system, under investigators' supervision, would gracefully solve these issues. The present paper describes the design of the evaluation of a technology allowing EHR to act as eSource for clinical trials. As part of the EHR2EDC project, for 6 ongoing clinical trials, running at 3 hospitals, a parallel semi-automated data collection using such technology will be conducted focusing on a limited scope of data (demographic data, local laboratory results, concomitant medication and vital signs). The evaluation protocol consists in an individual participant data prospective meta-analysis comparing regular clinical trial data collection to the semi-automated one. The main outcome is the proportion of data correctly entered. Data quality and associated workload for hospital staff will be compared as secondary outcomes. Results should be available in 2020.