The ClinSite tool launched by Phesi is a self-service, artificial intelligence (AI) powered Software as a Service (SaaS) solution, which searches and selects investigator trial sites for biopharmaceutical companies using data collected by the clinical development analytics provider.
Phesi’s technology platform taps into data from 4.2 million physicians and 600,000 investigator sites identified from 80,000 sources and 330,000 clinical trials in 240 countries.
Jonathan Peachey, CEO of Phesi told us, “Our primary strategic goal is to partner with trial sponsors to remove barriers to conducting effective clinical trials, and to get novel therapies to patients faster.”
“Smarter trials equal faster cures,” he added.
The intention behind the development of ClinSite is that it can be used directly by client teams under a SaaS, cloud-based license, “leaving Phesi to support the more difficult programs as a service, either directly or via formal alliance partners,” said Peachey.
According to the company, 250 clinical trials have used the solution to select investigator sites and those trials have enrolled patients 40% faster than other sites.
The solution uses an automated analytical approach and a proprietary data analytics platform, an approach, which Peachey said enables Phesi to conduct activities traditionally conducted in series and by different functional groups, simultaneously, by running ‘what if’ analyses’ in real time.
Peachey told us that the use of the service recently saved “millions of dollars by advising on the design of a gastroenterology protocol and determining the optimum number of sites requires and the best investigators.” The client estimated that it saved up to $5m by using the AI-based analysis.
The company expects to introduce an additional integrated module to optimize design of study protocols in 2020.
“We have seen a strong and growing demand for high quality, specialized providers in the niche we occupy, and we confidently expect dramatic further growth as the pharmaceutical industry faces increasing R&D productivity and cost pressures,” Peachey said.
