Decentralized clinical trial pioneer Medable unveiled an intelligent automation technology at the JP Morgan Health Care Conference, claiming it can slash standard trial build timelines by at least 50%. This development hinges around electronic clinical outcomes assessment (eCOA) — a common bottleneck in trial startups.
“We’ve used AI to essentially take a protocol and translate that into a mobile application that is running on a patient’s phone,” said Dr. Michelle Longmire, CEO of the company in a recent interview. “The app can partner with patients in their health journey, asking them the right questions at the right times to understand how they’re doing with their clinical trial intervention.”
The AI in question involves the use of large language models for complex task automation and workflow orchestrations.
Medable notes that prominent Big Pharma companies are using the automation technology to cut weeks off build times. “AI has solved for the complexity of translating that protocol into a digital application,” Longmire said.
Simplifying eCOA complexities through automation
Dr. Michelle Longmire
Emerging roughly a decade ago, eCOA provided a way to add clinical trial data to electronic case report forms (eCRFs) or a clinical database. Although the technology (eCOA) enabled the transition of clinical trial data from paper to digital formats, its adoption can involve operational complexities. Such challenges can involve the need for staff training, new equipment provisioning, management logistics, and process and cultural changes at trial sites.
Medable has applied this AI-powered automation across its entire clinical trials platform, not only addressing eCOA complexities but also simplifying other critical aspects of clinical trial operations.
Medable prioritized helping automate labor-intensive, manual tasks involved in eCOA deployments. The company’s auto-configuration tool, for instance, rapidly generates standard configurations such as schedules of assessments, anchor dates, and patient flags. In addition, its auto-validate tool streamlines the testing process by automatically producing a downloadable Configuration Validation Report (CVR), which validates the quality of study builds.
AI as key to transforming trials
The new automation capabilities represent just “the tip of the iceberg” for how artificial intelligence can “collapse clinical trial timelines,” Longmire said. She explains that complex processes like configuring eCOA data collection tools leave “a lot of room for human error.” By applying AI to “standardize, streamline, accelerate,” these complex configuration tasks, Medable aims to develop solutions with “a higher level of reliability” than manual approaches plagued by potential quality issues. Longmire envisions immense untapped potential for intelligent algorithms to remove inefficiencies and inconsistencies caused by manual work, driving faster and more accurate trial deployments. “This is a particular area of interest for us,” she says, as reducing these errors through automation promises to both speed up workflows and improve overall data quality.
“What we’ve done is we’ve used AI to ensure that every single time you’re launching a clinical trial, you’re not waiting on your eCOA solution to get those patients enrolled,” Longmire said.
Filed Under: clinical trials, Drug Discovery, machine learning and AI, Women in Pharma and Biotech
