The community setting refers to community cancer care clinics or community oncology clinics that offer diagnosis, treatment, and recovery programs for various cancer stages. More often than not, these clinics are affiliated to a hospital or university, closer to town, and offer personal care. These clinics are also usually more cost-effective, making them a preferred choice among many oncology patients.
Therefore, these centers have flourished and remain a great place to find suitable recruits for ongoing and future clinical trials. Yet, many patients aren't making it to relevant phase 2 and 3 trials due to various bottlenecks involving these oncology centers.
Challenges in patient recruitment
Stringent inclusion and exclusion criteria aren't the only challenges. In the current state of affairs, a clinical research coordinator, an individual from the care team, must manually look through patients' medical records to ascertain whether a patient is suitable for a specific pharma-sponsored trial. This process is labor-intensive and can result in errors.
During the pre-screening process, many trials need to know if the patient carries specific molecular markers or not, and these results can take time. There's no communication line between the clinical research coordinator and the pathology teams to speed up the decision-making on whether a patient can be enrolled or not.
In the case of oncology trials, time is of the essence for the study to progress appropriately.
Can communication be established?
Artificial Intelligence, machine learning, and robotic process automation technology have the potential to make sense out of large and disconnected sources of information. This means that, instead of the clinical research coordinator manually combing through medical records or waiting for pathology reports to return, AI technology, such as Deep Lens' VIPER, can analyse the information to find potential recruits.
The coordinator can then focus on aspects that need personal attention and contact possible recruits to take the next steps.
How does VIPER solve these problems?
VIPER uses artificial intelligence to read through huge amounts of data to match patients despite the complexity in study protocols within narrow windows of availability.
The technology connects with existing EMR (Electronic Medical Records), LIS (Laboratory Information System), and genomic testing to match patients to relevant precision trials while also presenting all available therapy options.
As this technology does the heavy lifting, care professionals can spend less time with spreadsheets and more time with patients.
Why isn't the situation changing?
Dr. Harish Dave, CMO, AUM Biosciences, narrowed it down to a lack of willingness to move to a new paradigm:
"If you've been doing something for the last 40 years, even though it's a broken model, there's a great deal of comfort in this model, and asking people to change is hard."
Dr. Kashyap Patel, CEO of Carolina Blood and Cancer Care and President, Community Oncology Alliance (DC), endorsed Dr. Dave's views, saying:
"I would not be surprised if eventually, the FDA NIH comes down very hard on the industry and puts a piece of legislation in place to ensure they meet enough recruitment numbers from various populations, including minorities."