JMIR Publications

ABSTRACT

Background:

An eSource generally includes the direct capturing, collecting, and storing of electronic data to simplify clinical research. It can improve data quality, improve patient safety, and reduce clinical trial costs. There has been some eSource-related research progress in relatively large projects. However, most of these studies focused on technical explorations for how to improve interoperability among systems to reuse retrospective data for research. Few studies have explored source data collection and quality control during prospective data collection from a methodological perspective.

Objective:

This study aims to design a clinical source data collection method that is suitable for real-world studies (RWSs) and meets the data quality standards for clinical research and to improve efficiency when writing electronic medical records (EMRs).

Methods:

Based on our group’s previous research experience, TransCelerate BioPharm Inc. electronic source (eSource) logical architecture and relevant regulations and guidelines, we designed a source data collection method and invited relevant stakeholders to optimize it. Based on this method, we proposed the eSource record (ESR) system as a solution and invited experts with different roles in the contract research organization (CRO) company to discuss and design a flow chart for the data connection between the ESR and electronic data capture (EDC).

Results:

The ESR method included five steps: research project preparation, initial survey collection, in-hospital medical record writing, out-of-hospital follow-up, and electronic case report form (eCRF) traceability. The data connection between the ESR and EDC covered the clinical research process from creating the eCRF to collecting data for the analysis. The intelligent data acquisition function of the ESR will automatically complete the empty eCRF to create an eCRF with value. When the clinical research associate (CRA) and data manager (DM) conduct data verification , they can query the certified copy database through interface traceability and can send data queries. The data queries are transmitted to the ESR through the EDC interface. EDC and EMR systems interoperate through the ESR. The EMR and EDC systems transmit data to the ESR system through the data standards of the Health Level Seven (HL7) Clinical Document Architecture and the Clinical Data Interchange Standards Consortium (CDISC) operational data model (ODM), respectively. When implemented data standards for a given system are not consistent, the ESR will approach the problem by first automating mappings between standards and then handling extensions or corrections to a given data format through human evaluation.

Conclusions:

The source data collection method proposed in this work will help realize eSource's new strategy. The ESR solution is standardized and sustainable. It aims to ensure that research data meet the ALCOA+ standards for clinical research data quality and to provide a new model for prospective data collection in RWSs.