Health tech company Inato developed an AI-powered patient prescreening tool to make it easier and faster for research sites to assess patient eligibility for clinical trial opportunities.
Launched in 2016, Paris- and New York-based Inato built a platform to help expand the pool of patients for clinical trials and involve more diverse patient populations in drug trials.
Patient enrollment is a major bottleneck for trials. The patient prescreening process takes 25% longer today compared to five years ago—and growing trial complexity, larger trials and increasing competition for participants threaten to exacerbate this problem.
Ongoing developments in generative AI unlock new opportunities to overcome this challenge, alleviate site burden and accelerate trial timelines.
Research sites across the U.S. can use Inato’s AI-enabled patient prescreening feature to significantly streamline patient identification and review—with no electronic medical record or clinical trial management system integration required.
Kourosh Davarpanah, co-founder and CEO of Inato, said the company's latest technology helps tackle of the biggest hurdles in clinical trials. "Recent advances in AI reasoning enable sites to identify and review patients at scale–fundamentally changing how they approach this challenge. Sites can now focus their efforts on offering more trial options to more patients, accelerating enrollment timelines while enhancing access to research," he said.
Prescreening patients to trials has been a time-consuming and cumbersome process. Evaluating just one patient record against one trial’s inclusion and exclusion criteria could take hours.
"if you go back historically to how sites have been working once they have been selected in the trial, they have a really long protocol with typically 20 to 40 inclusion and exclusion criteria. Those can be insanely complicated. Historically, the way they do it is, they look at the patient records, which can be five to 500 pages, and manually, they're going to check, OK, is the patient meeting this criteria, this criteria, this criteria, and this takes them, typically, between half an hour for the simplest trials, up to four hours per patient. And by per patient, it's not per patient enrolled, it's for every patient prescreened," Davarpanah said in an interview.
The company's new AI agent enables research sites to de-identify patient records, quickly determine which trials are relevant to each patient and evaluate patients against inclusion and exclusion criteria to assess eligibility—accurately, at scale and in compliance with HIPAA guidelines.
Inato says its AI-powered tool can effectively assess patients in just minutes and at a 95% accuracy rate so site staff can make quick, informed decisions about who is eligible. Early users of the AI tool reported that it reduced their prescreening times by more than 50% and up to 90%, according to Inato executives.
The technology's blend of models is capable of sophisticated medical reasoning and deduction, time-bound assessments and understanding handwriting, according to the company. For example, if a trial requires patients with a history of seizures, with 10 in the last six months, with no more than a month in between incidences, the AI leverages a combination of mathematical analysis and medical understanding to create a simple, easily understandable assessment for sites in minutes.
"It's an important way for us to enable the researchers and the communities to get access to more patients that they could bring into trials and still have all the details they need and confidence that the data that they need to move forward is provided," he said.
Inato was founded with the aim of expanding access to more drug trials by matching community sites with the right trials for both them and their patients, Davarpanah said.
The company has grown steadily, and its platform has 5,000-plus community research sites in more than 70 countries, and it partners with 20 of the top 40 pharma companies, according to Davarpanah.
"As we got more sites and more trials and more pharma on board, we started getting more and more requests for supporting sites beyond the selection process. Where we got really excited was specifically on helping sites with identifying and enrolling in patients, which obviously, at the end of the day, is really what matters. From the site's perspective, very consistently, no matter the country, no matter the type of site, the biggest issue they were sharing is it takes absolutely forever to manually try to identify eligible patients across all the trials that were running," he said.
Inato worked closely with both trial sponsors and research sites to build and pilot the new capabilities.
"This new AI tool that we launched a few months ago is really what we're seeing now as the biggest boost for our growth and the biggest value for sites and sponsors," Davarpanah said.
He added, "Obviously, this is valuable for sites and sponsors, but what we're really most excited about is this fundamentally changes the experience for patients. As a patient, instead of being prescreened for one trial when the site is trying to enroll patients specifically for this trial, what the tool allows is, once you're uploaded into the tool, you will be prescreened across all the trials that are available at the site. If you're not eligible for this one, you might actually be eligible for a much better one for you. It's actually a big unlock for patients in terms of potential options of care."
Inato continues to collaborate with organizations like Pantheon Clinical Research to expand its AI capabilities into areas like feasibility.
In 2023, the company picked up $20 million in series A2 funding backed by Cathay Innovation, Obvious Ventures, La Maison and Top Harvest Capital.
Davarpanah says he is excited about the opportunities to use AI technology in the clinical trial space. At the recent J. P. Morgan Healthcare Conference in San Francisco, the conversations around AI in healthcare have shifted, he noted.
"Historically, most of the AI discussions have really been focused on drug discovery, and we're finally starting to see the excitement really peak on the drug development piece. I think if you go back one year when people were already excited about AI, even though everyone recognized that the biggest blocker to getting more drugs to market were trials, no one was very clear about what would be the impact of AI on trials, and this has really changed this year," he told Fierce Healthcare.