Electronic data collection (EDC) tools have been proven to streamline clinical trials in several ways. These systems create faster data recording, which shortens trial periods, and provide better data as well. When clinical researchers present their findings and bring new treatments to market, they can feel confident that their studies are accurate and reliable.
However, not all EDC systems are of the same quality or have the same key features. Here are three aspects of your EDC system to keep in mind as you select a provider.
Easy Data Entry
One of the main benefits of EDC tools is the ease with which information can be collected by contract research organizations (CROs) and sponsors. Researcher organizations can obtain more data and collect it faster because the information is immediately cleaned and added to a larger database. This is a significant benefit over past clinical data management systems.
“In the days of paper-based data collection, how the data were entered into electronic format, typically in a Clinical Database Management System (CDMS), was not the most important consideration,” David Eade, et. al write in an article published by the Journal of the Society for Clinical Data Management.
“The CDMS partially automated the workflow of data entry, integration of external data, cleaning and coding, and provided automation for tracking data entry, discrepancy identification, and discrepancy resolution. However, the benefits of these advances were largely limited to in-house data management groups in Contract Research Organizations (CRO) or Sponsor organizations.”
By the time the data is reviewed by a human researcher, it has likely gone through multiple checks and logic steps with the help of EDC technology to make sure it is accurate and within reasonable bounds.
Quality Management and Data Cleanliness
One of the main benefits of switching to an EDC system is improvements in data quality. Poor data is a waste of time for both researchers and patients. A high number of errors means it takes longer to complete the analysis of the data overall, plus any information with errors must be discarded. As you evaluate EDC tools, make sure they have the data management capabilities you expect.
“Data quality is a multi-dimensional concept with five key dimensions, viz. completeness, uniqueness, timeliness, accuracy, validity, and consistency,” write Santam Chakraborty, et al. researchers at Tata Medical Center in Kolkata, India, in a preliminary report. “Ensuring data quality in randomized controlled trials (RCT) is essential to ensure the validity of results.”
A system that only checks for completeness won’t be as useful as a tool that also filters results for inaccurate or valid numbers. These systems also need to work quickly to ensure the data is collected in a timely manner.
Even if your data is collected accurately from the start, there may be changes to how you collect and report on it over time. These changes aren’t common, but there is one clear example of researchers making last-minute accommodations to their studies: the COVID-19 pandemic.
Mary Stanfill, Kathy Giannangelo and Susan Fenton, vice president of United Audit Systems, president of Kathy Giannangelo Consulting, and associate dean for academic and curricular affairs at The University of Texas Health Science Center School of Biomedical Informatics, respectively, published a paper in Spring 2020 that guided researchers on data capture during the COVID-19 pandemic. At the start of the pandemic, researchers weren’t sure how to incorporate notes related to COVID-19 into their documentation, which led to confusion. However, there are now universal codes related to the coronavirus and its symptoms, which make it easier for research documentation.
The authors encourage researchers to reconcile their data to ensure the correct COVID-19 codes were used and the right data was collected. Of course, now that the pandemic has aged a year, many teams have already implemented questions related to this disease into their patient evaluations.
A good EDC system shouldn’t create more work for your data management teams. Instead, it should help teams across all levels of the clinical trial process save time and money. Look for these key features in order to streamline your operations and move more treatments to market.