Ways to Advance Clinical Data Quality in the Digital Era

The transition from paper to electronic data capture (EDC) in the clinical trial setting led to a shift in measuring the quality of clinical data management (CDM) activities. The paper world recognized that the quality of clinical data acquired was simply a function of transcription teams’ ability to transform paper data into a database. Everything was smaller, as well as a thorough QC of critical factors. For acceptable error rates, the industry settled on tolerance of 0.5%. These levels were no longer required when EDC enabled locations to input data directly, and transcribing was no longer needed. Pubrica data management teams, on the other hand, must be involved in a variety of tasks to prepare data for effective analysis and submission.

The quality of the efforts that result in data-collection technologies and data cleansing may directly impact data quality. As a result, organizations must evaluate how to monitor the quality of workstreams in which their teams participate, particularly as more data is received from multiple sources such as eSource, ePRO/eCOA, EMR/EHR, wearables, mHealth, and AI-based adherence tracking systems, among others. Traditional error rate thinking is no longer an appropriate method to manage quality standards; rather, quality must be fostered as a habit or culture inside data-handling teams. When conducting quality control sampling, teams must also assess quality rather than quantitatively.

Clinical trials are just an expensive method of “collecting data.” If we don’t develop the tool appropriately to collect data, we create a gap that can’t be closed, leading to patches piling up and teams putting in extra effort to ensure data quality.

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