Clinical trials vary significantly in their research areas, the questions they explore, and the details in between. Yet all clinical trials share one overarching common goal: quicker data collection.
Electronic data capture (EDC) systems allow teams to collect data more efficiently. Systems that focus on interoperable data collection also improve the process of analyzing data sets, both during a clinical trial and in future research.
By building certain efficiencies into the trial itself, EDCs, like Anju’s TrialMaster, can help teams reach the data collection stage more quickly and at lower cost.
Using Automation and Templates in the Trial Build Process
There are two ways to create a clinical trial build that reduces time to data collection: incorporating automation and using templates in the clinical trial process.
TrialMaster supports efficient trial builds in several ways. Its interface allows users to easily build simple or complex studies in Phase I–IV of clinical trials. Automated tools, like edit check generation and built-in medical coding, flag errors and streamline routine, often repetitive tasks that can otherwise lead to the incorporation of transcription errors.
These automated tools help flag potential issues, preventing typos or mistakes from slipping under the radar, thereby undermining the quality of collected data. Clinical trial teams can then give their full attention to tasks that lead to faster, more thorough data collection.
The Custom Data Export Utility allows users to define export data domains, generate SAS datasets, and ensure data complies with industry standards like CDISC SDTM. Standard templates included in TrialMaster conform to SDTM by default, guiding users to provide the necessary information in the proper formats for SDTM compliance.
Flexible tools for data capture and analysis also let clinical trial teams standardize data collection expectations during the build phase. By using these templates, clinical trial teams can focus on collecting thorough, standardized data rather than on how that data should be expressed or formatted.
Getting Teams to the Quicker Data Collection Stage
Efficient clinical trial builds based on an EDC’s use of automation and templates can help teams reach the data collection stage more quickly and with fewer errors.
In a 2022 study, Cynthia M. Senerchia and fellow researchers used electronic data sources, including an EDC system, to analyze data collected from patients with diabetes who were being treated with metformin. The researchers found that using electronic data offered several advantages over paper-based clinical trials, including:
- No transcription errors, as no data was transcribed from paper to electronic capture.
- The systems flagged “medication changes, healthcare encounters and lab results” that deviated from standard clinical practice, allowing teams to spot discrepancies quickly.
- Using largely interoperable data minimized the amount of effort required for electronic systems to share information.
The researchers predicted their use of EDC and similar electronic systems “could be expanded for larger trials and will significantly reduce staff effort.”
Interest in using EDC systems has increased exponentially since the beginning of the COVID-19 pandemic. The recently issued U.S. National Biodefense Strategy, for instance, includes a call for a U.S. clinical trials infrastructure “ready to administer candidate countermeasures to participants within 14 days after the identification of a viable countermeasure.”
In a post at HealthITbuzz, Office of Science and Technology Policy (OSTP) officials Micky Tripathi, Jennifer Roberts, and Grail Sipes write, “A key component in building U.S. capacity for clinical research – both during a public health threat and at other times – is ensuring that trial data can be captured as a set of consistent data elements across separate trial sites under a coordinated clinical trial protocol.”
EDC systems contribute to this goal when they focus on streamlining the collection of interoperable data in a secure environment. Anju’s TrialMaster offers a flexible, robust digital platform with the automation and template-based tools needed to speed clinical trial teams toward more complete and more accurate data collection.
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