Fulshear, TX – June 9, 2022 – Sieve, a B2B SaaS platform leveraging artificial intelligence technology to enhance operational efficiencies for multiple Clinical Research Stakeholders by automating clinical trial recruitment workflows, has launched an equity crowdfunding campaign on Wefunder.
Founded by Arti Bhosale and Sameer Bhoite, Sieve aims to address long-standing issues plaguing traditional clinical trial enrollment processes and address the problem at its core. Being an experienced clinical research executive, Bhosale herself is aware firsthand of the operational hurdles causing low enrollments. After multiple discussions, both Bhosale and Bhoite realized that today's advancements in Artificial Intelligence could be leveraged to address the issue of low trial enrollments. As a result of those discussions and research among the founders, they concluded that Natural Language Processing could be utilized to automate the manual trial enrollment process, allowing for a faster and more efficient process with reduced effort.
“Sieve provides automated clinical trial matching and recruitment management for physicians, research sites, and study sponsors, leading to enhanced efficiency and faster trial enrollments. Sieve's platform mimics yet automates the traditional trial enrollment processes allowing for optimization with minimal adoption efforts,” says CEO and Co-Founder Arti Bhosale.“Sieve's mission is to expand access to clinical trials and accelerate trial execution,” Bhosale adds.
Sieve's approach differentiates them from their competitors. In addition to providing the technology, Sieve also joins forces with the groups executing the trials at the ground level. Sieve's SaaS platform eliminates one of the most significant and critical bottlenecks affecting trial success by accelerating and optimizing clinical trial processes. Using Sieve's platform, users can identify and enroll patients with ease. Sieve's approach will also reduce administrative burdens and eliminate the process inefficiencies causing lower clinical trial enrollments by leveraging technology as a tool for study success. Sieve currently partners with various groups in North America.
“We want to accelerate new treatment developments because many patients and their caregivers are waiting for those advances in medicine. Everyone, including us, has family, friends, colleagues, or someone else we love who we have lost or know is suffering because a new or advanced treatment option was buried under administrative overload or delayed in development,” says Co-Founder and CTO Sameer Bhoite.
Sieve is proven to significantly reduce administrative burden and matches trials with patients in a fraction of the time it takes to perform the process manually. The Natural Language Processing technology embedded in Sieve's platform mimics the process of reviewing records manually. With the combination of automated trial matching and a recruitment management platform, Sieve provides an end-to-end solution that helps research sites, healthcare providers, CROs, and Pharma keep on top of their studies. Using Sieve's validated algorithm and process, the clinical trial matching process could be accelerated without compromising match quality. In the end, this means developing cutting-edge treatments faster for the people who need them most.
For more information about Sieve, and their innovative B2B SaaS platform, visit their Wefunder page: About Sieve:
Sieve is a B2B SaaS platform leveraging artificial intelligence technology to enhance operational efficiencies for multiple Clinical Research Stakeholders by automating clinical trial recruitment workflows. Sieve aims to reduce the administrative burden involved in the process of trial matching and enrollments to enable accelerated trial execution. Sieve provides automated clinical trial matching and recruitment management for physicians, research sites, and study sponsors, leading to enhanced efficiency and faster trial enrollments. Sieve's platform mimics yet automates the traditional trial enrollment processes allowing for optimization with minimal adoption efforts.
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