Icon says artificial intelligence helped it increase patient access to clinical trials over the past 12 months.
The Dublin-based trials contractor made the comments in a report last week, explaining it released a major artificial intelligence enhancement to its tech that identifies the best sites for a trial opportunity.
According to the firm use of AI resulted in significantly improved study start-up and site cycle times.
Icon also cited a partnership with health information tech firm Veradigm – gained through its acquisition of PRA – as key to efforts to improve access to clinical trials over the past year.
Through the collaboration Icon and Veradigm created an electronic health records-based clinical research network that positions clinical research as a care option, which can help reach more diverse populations, increase recruitment rates and accelerate time-to-market for new therapies.
The firm said it “has focused on creating a strong foundation that enables the execution of new and innovative methods for meeting patient needs and advancing public health.
“It is focusing its innovation efforts in three critical areas - improving clinical trial design and execution, enabling faster and more predictable patient recruitment, and evolving clinical trials to be more patient-centric.”
Icon also highlighted its recently launched “Cares” program - which brings together its sustainability, diversity, inclusion and belonging (DIB) and CSR initiatives – as part of its efforts to improve both access to trials and diversity.
AI
Icon has talked about the potential impact AI could have on trials for some time. In 2018, for example, the CRO predicted the approach would aid protocol design, patient enrolment and retention, and study start-up.
More recently the firm partnered to establish a service known as CiteLine Study Feasibility, which uses machine learning – a type of AI - to help sponsors assess the feasibility of a planned clinical trial, accelerate proposed timelines and – when the program is running – identify and reduce non- and low-enrolling sites.
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