AI is Changing Clinical Trial Enrollment

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Clinical Trial Enrollment

Clinical trials represent the forefront of medicine bringing new treatment options to patients and caregivers. Unfortunately, close to 90% of clinical trials fail to meet recruitment goals. This results in expensive delays that often result in early trial termination, or simply the inability to gather sufficient data to draw efficacy conclusions. These clinical trials failures slow down research, delay patient access to life-saving treatments, and contribute to rising drug costs.

Enrollment Challenges

  • Most patients are unaware of which clinical trials are being conducted and if they qualify to participate.
  • Matching potential candidates can be painstakingly arduous as manual reviews are required of patients’ medical records because over 90% of patient history is unstructured data – doctor’s notes, pathology reports, etc. – to match trial criteria.
  • Multiple trials are often conducted simultaneously creating competition for a limited pool of potential candidates.

According to a Harris Interactive Survey of cancer patients, 85% of patients were either unaware or unsure that participation in a clinical trial was an option at the time of diagnosis, but 75% of these patients said they would have been willing to enroll had they known it was possible.

Extending a clinical trial timeline by as little as a month can result in significant budget overruns, as well as, the loss of potential revenue from delayed drug commercialization. The number of qualified available patients willing to participate in a trial is a crucial factor that impacts the likelihood of a timely and successful trial.

Pharmaceutical companies are challenged to find enough qualifying patients within the time constraints of the enrollment period. This requires greater awareness of the trial amongst caregivers and the prospective candidates. AI and Machine Learning can help.

In order for pharmaceutical companies to stay ahead of their competitors, they must embrace new technologies, such as Machine Learning, to integrate and manage data more efficiently. Further, organisations must break down existing data silos and integrate disparate healthcare data to discover new prospects for upcoming trials. This will require greater collaboration with data partners.


IntelliClinical is a comprehensive cloud-based clinical trial analytics solution that provides faster actionable insight into clinical trial programs to make more – informed business decision and improve your bottom line with timely and fact-based help. IntelliClinical platform adds agility to clinical trial management that lowers risk while improving quality and compliance.

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