Portal de Periódicos da CAPES

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

2020; IOS Press; Linguagem: Inglês

10.3233/shti200184

1879-8365

Nicolas Griffon, Héléna Pereira, Juliette Djadi‐Prat, María Teresa García, Sara Testoni, Manon Cariou, Jacques Hilbey, Aurèle N’dja, Grégory Navarro, Nicola Gentili, Oriana Nanni, Massimo Raineri, Gilles Châtellier, Agustı́n Gómez de la Cámara, Martine Lewi, Mats Sundgren, Christel Daniel, Almenia Garvey, Marija Todorović, Nadir Ammour,

Machine Learning in Healthcare

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.