These trends will continue. Along with the increase in volume and complexity, there is increasing pressure to reduce cycle times and acquire and deliver data more rapidly, contain costs, and ensure data integrity. It means there’s a unique opportunity and urgency for data management to learn and use new tools and develop new skills, which will make a huge impact on how data managers conduct trials and support patients.
Electronic source data, commonly referred to as eSource, is data initially recorded in electronic format.
Increasing adoption of eSource technologies is essential to collecting data rapidly and dramatically increasing quality. Technologies to implement eSource are widely available and their use in clinical trials is growing. Electronic Clinical Outcome Assessment, or eCOA, which has been available for many years, is commonly used and increasingly being utilized to collect efficacy endpoints and safety data. And we are now seeing more sponsors piloting and starting to adopt technologies to integrate site electronic health record (EHR) systems directly with electronic data capture (EDC), often referred to as “EHR to EDC.”2
Standards, such as FHIR, developed by Health Level Seven International (HL7), which stands for Fast Healthcare Interoperability Resources, facilitates the exchange of electronic healthcare information and has helped enable this technology. And advances in artificial intelligence (AI) and natural language processing (NLP) offer the opportunity to identify and process relevant information from the unstructured notes within EHR systems. Unfortunately, critical data collected by other means outside of the electronic case report form (eCRF), such as safety labs, pharmacokinetics (PK) data and biomarkers are regularly collected through antiquated file transfer processes, often only delivered monthly or quarterly. This data needs to be collected more regularly and much more efficiently.
As the volume and speed of data collection increases, the practice of clinical data management must evolve.
Data management must adopt more efficient methods to review and clean data. Manual review practices will not suffice. Solutions that offer data management automated means of reviewing and reconciling data need to be used. Many of these solutions also allow other functions to access the data quickly, as soon as it received – eliminating the need for data management technologists to spend days or weeks to process or transform the data before it can be distributed, where the data is often shared through email or web storage. Delivering high quality data more quickly for analysis and decision making is essential.
The use of AI and machine learning (ML) is slowly being adopted for data review and cleaning. These technologies facilitate rapid review of enormous amounts of data at speeds that humans could never match.
Analysis has shown that only a very small percentage of the data is ever changed throughout the data cleaning process. Consequently, this led to adoption of Risk Based Quality Management. For example, where all EDC data used to be verified against the source, the accepted approach is to verify a subset of critical data. We are on the cusp of broad adoption of similar approaches for Data Management.
1 Tufts Center for the Study of Drug Development. January 12, 2021. “Rising Protocol Design Complexity Is Driving Rapid Growth in Clinical Trial Data Volume, According to Tufts Center for the Study of Drug Development”
2 Guidance for Industry Electronic Source Data in Clinical Investigations, September 2013
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