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Medidata Detect and eCOA in Decentralized Clinical Trials

Sep 20, 2021 - 2 min read
Medidata Detect and eCOA in Decentralized Clinical Trials

While decentralized clinical trials (DCTs) have provided benefits for participants such as fewer trips to trial sites and for sponsors, a huge increase in the amount and speed of data collected, they bring new risks due to the amount of data collected, digital endpoints, and the need to adjust oversight of compliance and data integrity.

“We need better tools to account for the quality of this new and more substantial data that's coming in so that we ensure proper oversight and patient safety,” says Ken Hamill, Director of Product Marketing for Clinical Operations at Medidata Solutions. “I need to have complete visibility with what’s going on with the data.”

One of the tools is Medidata Detect, a centralized statistical monitoring tool based on a robust RBQM approach that provides complete oversight into patient, study, and site risks. It uses statistical algorithms and machine learning to identify trends, errors, and anomalies across all the data within a trial.

“It looks at data trends over time, inconsistencies in data, outliers, skewed distributions,” Hamill explains. “And then it learns what acceptable ranges are and gets better over time. It surfaces that information up to users in a very visual, easy-to-digest format.” When it comes to decentralized clinical trials, Sas Maheswaran, Principal Engagement Consultant, Strategic Consulting Services at Medidata, says that many sponsors are focused on compliance and don’t spend enough effort on ensuring data quality. For example, a participant in a trial may record their temperature into an eDiary daily, but they may enter the number incorrectly or a remote sensor might not have been calibrated properly. That can lead to poor data quality.

“Tools like Medidata Detect can identify patterns within and across participants or sites in near real-time,” Maheswaran says. “Detect is able to analyze extremely large volumes of data quickly so that we can take meaningful action to ensure data integrity and participants' safety. Issues surrounding eCOA and Detect should be discussed during a decentralized trial’s protocol optimization conversations," according to Brian Barnes, Senior Director, Product Development - RBQM and Digital Oversight at Medidata. Sponsors should continue to plan mitigation strategies to control risks that cannot be eliminated. And those strategies should not only focus on data collection compliance, but also the reliability and consistency of data.

Centralized statistical monitoring should be used to proactively identify data trends, Barnes notes. “Medidata Detect holistically reviews subject data with on-demand data ingestion for detection of errors, trends, and anomalies in study data through automated statistical algorithms to enhance study data quality and ensure patient safety.”

Barnes adds that the COVID-19 pandemic accelerated remote and risk-based approaches for clinical trial execution to ensure the safety of trial participants and minimize risks to trial integrity. Regulators have also shown increased interest and support for innovation and remote capabilities as evident by ICH E6 (R3) Draft and its alignment with ICH E8(R1) to Quality by Design (QbD) principles, and the inclusion of technology to support enhanced efficiency in clinical development activities.

As the industry continues to advance, centralized and remote monitoring is evolving to improve data surveillance across data sources including operational and audit data. Better risk monitoring of known and novel risks—including risks that are unique to decentralized data collection stemming from data collection issues during home visits, user technology interface challenges, and patient-centric fraud—will serve to improve remote data collection in the future.

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