However, the lessons learned from the adoption of web-based EDC may be helpful in the design, implementation, and evaluation of other technological innovations involving data collection and processing in clinical studies. The systematic literature review supporting the revision of the Good Clinical Data Management Practices (GCDMP) EDC Chapters identified many articles of historical importance but of limited value to inform present EDC practices. Companies also reported use of EDC systems to process Patient Reported Outcomes (ePRO) data (34.2%), Pharmacokinetic data (33.9%), and Biomarker data (28%). 4 The most common types of data managed in EDC systems included eCRF data (100%), local lab data (59.5%), and Quality of Life data (59.5%). 4 Responding companies reported managing 77.5% of their data volume in EDC systems. Since EDC has largely replaced collection of structured data on paper forms, it is no surprise that EDC led the ranked-list of implemented data systems in the recent eClinical Landscape Survey in which all of the 257 eligible respondents reported use of web-based Electronic Data Capture. Support for specialized data collection and management have also been reported and include centralized image interpretation, classification of clinical events, management of serious adverse events in studies, and management of source data when special requirements 2, 3 are met. Some systems have additional functionality supporting randomization of study participants, assigning controlled terminology to data, and collection of data through patient-completed questionnaires. Core functionality available in most web-based EDC systems today includes the ability for a data manager to (1) design and maintain screens for data entry via the internet (2) add and maintain univariate and complex multivariate rules to check for discrepant data (3) develop rules for alerts and conditional form behavior such as adding fields or forms based on user entered data (4) import and export data (5) store and retrieve data (6) implement and maintain role-based privileges and (7) track and report status of data entry and processing. The fundamental shift enabled with web-based EDC was decentralized entry and centralized organization of data processing, updating, and storage. EDC enables these functions at geographically distributed sites using web-based software (i.e., an EDC system). BackgroundĪlthough more broad in literal meaning, the label Electronic Data Capture within the therapeutic development industry has historically referred to manual key entry, automated discrepancy identification, and manual discrepancy resolution. Learning from EDC adoption experience will help EHR-to-eCRF adoption proceed with less risk and return value more quickly. While varying workflows and data flows are being pursued, EHR-to-eCRF and EDC adoption are similar in that both involve significant technology, process, and behavior changes at clinical sites and study sponsors alike. 1 The interoperability with clinical site EHRs required for direct data collection from EHRs is likely the next major advance in clinical research data collection and management. Data collection such as this requires the ability to identify study data in EHRs, request the data from the EHRs, reformat the data for the study, and transfer the data into the study database. This new data acquisition option has been variously referred to as EHR eSource, EHR2EDC, and EHR-to-eCRF data collection. Today, we stand at the cusp of the adoption of direct data acquisition from site EHRs in multicenter, prospective, longitudinal clinical studies. However, neither the history of this important evolution in clinical research nor the reasons for the apparent protracted adoption have been systematically synthesized and reported. Multiple reasons have been articulated for the slow evolutionary development of data collection and management methods. The progression to web-based EDC has taken 40 years. Web-based Electronic Data Capture (EDC) was arguably the third. Use of computers to organize, process, and store data was the second. The first revolutionizing development in clinical research data collection and management was the use of structured paper data collection forms to facilitate consistent data collection across multiple clinical sites.
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