US-based clinical trial technology provider Medable has introduced new automation technology that reduces the amount of time it takes to set up standard clinical trial procedures.
The artificial intelligence (AI)-powered platform is applied in electronic clinical outcomes assessment (eCOA) deployments, which then streamlines manual tasks and saves time. By automating manual tasks such as testing, Medable speeds up trial initiation and minimises delays.
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Medable updated its eCOA features to incorporate an auto-configuration tool generating standard configurations, such as assessment schedules, anchor dates, and patient flags quickly. The auto-validate tool eliminates the need for extensive testing by automatically providing a downloadable Configuration Validation Report (CVR) to validate the study build’s quality.
In the announcement accompanying the launch, CEO and co-founder of Medable Michelle Longmire said: “We are reimagining the way clinical trials are deployed by eliminating many of the biggest process bottlenecks. Starting with eCOA, we are accelerating clinical trials while helping to improve data quality – ultimately enabling our vision of a one-day study start-up to help deliver effective treatments and cures to patients faster.”
According to the 8 January press release, Medable aims to cut standard trial build timelines by at least half.
The company recently partnered with care coordination company Pluto Technology to improve and optimise access to clinical trials, with Medable also teaming up with the Multi-Regional Clinical Trials Center of Brigham and Women’s Hospital and Harvard (MRCT Center) to release a toolkit for the DCT ethics review process.
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By GlobalDataWashington, US-headquartered technology provider Suvoda has also recently launched a configuration-based toolkit, which can streamline and expedite the setting up and delivery of eCOAs.
AI is a growing tool in the clinical trials landscape, which is being used to target various challenges. For example, AI is being used to connect and match patients to clinical trials, overcoming the time-consuming process of recruiting suitable patients.
According to an analysis by Clinical Trials Arena, low accrual rates are the most common reason for a clinical trial termination. Additionally, AI may be used to replace animal models in drug development, using cell-based assays and computer models instead.
