Clinical trials face more than their fair share of struggles due to low enrollment, delays in patient recruitment, and very few patients qualifying for the clinical trial.
Also, CRO staff, oncologists, and even sponsors continue to plow through patient databases in silos, resulting in a tremendous strain on staff and a longer time to reach an outcome.
Technology has been focussed on the last mile of trials — after a patient is enrolled. Pre-screening processes need streamlining to avoid missing patients, screen patients faster and keep overall staff morale high.
Artificial Intelligence, machine learning, and robotic process automation can free up a lot of time within the CRO/CRC framework and reduce staff burden.
Clinical research coordinators (CRCs) are spending a lot of time identifying patients by collating information from disparate sources such as pathology reports, genomic testing, unstructured notes, messages, and expert analysis (from a doctor) to realize that the patient isn’t qualified. This is a cause for delays in the overall process and disappointment and frustration and is the number one reason why CRCs don’t want to pre-screen too many patients.
AI-based technology, such as VIPER, can take the frustration out of these repetitive processes. VIPER can assimilate disparate information into a single dashboard to vet patients based on inclusion and exclusion criteria.
VIPER’s AI technology can identify patients at the diagnosis stage to suggest which trials would be best-suited for the patient and match them to the trials, reducing staff burden.
Deep Lens’ Chief Scientific Officer, T J Bowen discussed clinical trial-specific bottlenecks with Deborah Fridman, director of Clinical Research at Southern California Cancer Center, and Jim Langford, Vice President of Clinical Operations at AIVITA Biomedical Inc. and got their views as CRC and sponsor and how VIPER makes the process fast and powerful.
Click on the link below to watch the webinar.