Clinical trial recruitment has always been challenging. It is especially challenging for therapies being tested for rare diseases, an already small population. Clinical trials for rare diseases are small out of necessity, but they’re even smaller due to an inability to recruit. Many of these trials don’t reach more than 100 participants.
To understand just how small this is for a medical trial, Pfizer’s clinical trial for the COVID-19 vaccine included 40,000 participants. Further, 90% of rare disease trials never reach their goal for recruitment, and 81% of patients who want to participate are not deemed eligible.
Further, rare disease trials typically take 68% longer to complete than other trials. This number is so high only because of the time it takes to initially recruit patients.
As a result of recruitment struggle, many rare disease trials create 6 times more trial locations than typical studies. When you don’t know where to look, you need to expand your search. Of course, this adds to the financial cost of implementing a trial.
A new upgraded AI recruitment system by Real Chemistry IPM.ai should improve the ease of recruitment for these challenging subject pools.
The New AI
It’s not just about finding individual patients for these trials. It should be about finding pockets of patients. This would allow researchers to effectively place clinical trial sites where the people are.
Thankfully, this can be done with artificial intelligence, most specifically, machine learning.
IPM.ai has access to a plethora of anonymized health data. This includes information regarding which physicians patients go to and which medical institutions those physicians are affiliated with. This allows the research team to find geographic areas patients are located, and ultimately improve recruitment.
For instance, when IPM.ai worked with X4 Pharmaceuticals, they combined their data together, overlaying WHIM syndrome’s genetic marker with their geographic database. Together, they were able to find where patients were located. Then, they used machine learning to find commonalities and patterns. Ultimately, AI technology is able to predict every individual in the United States likelihood of being diagnosed with WHIM Syndrome.
You can read more about this AI here.
