CASE STUDY
How Telisina used AI to optimize clinical trial recruitment
This case study illustrates how AI can help drive clinical trial recruitment efforts in rare diseases. It also highlights how critical having a personalized data strategy is in enabling a data-driven, evidence-based approach to clinical trial recruitment, leading to increases patient recruitment rates, improved patient selection and reduced costs.
CATEGORY
Advanced Analytics/AI
Clinical Trials
CLIENT TYPE
BioPharma
IMPACT
4500+
Clinical Trial Patients
Defined and Identified
IMPACT
26
Shared Patient Profile
Attributes Identified
THE STORY
Addressing clinical trial recruitment
challenges with AI
Set against the backdrop of COVID-19, a BioPharma company was looking to utilize a data-driven analytic approach to define, identify and recruitment patients for a phase 3 clinical trial of a promising gene therapy candidate.
Amid the difficulties of conducting trials during the COVID-19 pandemic, a BioPharma company was in search of an efficient, novel, data-oriented strategy to improve clinical trial recruitment efforts. With traditional methods adding to the complexity of targeting a rare disease population during a global health crisis, the need for a fresh, innovative approach was required.
Responding to this, we formed a partnership with the client with a clear vision: to transform clinical trial recruitment using AI. We recognized the potential of AI to revolutionize recruitment by delivering highly accurate, predictive insights, especially when dealing with a niche patient population during a global pandemic. Our mission was to leverage AI to optimize the recruitment process and maintain the pace of clinical trial activation.
THE GOAL
Identify the ideal patient profile for improving clinical trial recruitment efforts.
This project aimed to identify utilize a personalized data-driven strategy to identify and reach potential patients, and overcome the challenges of recruiting patients in rare disease.
THE SOLUTION
Integrating AI and data strategy for enhanced results
Custom AI/ML algorithms transform clinical trial site activation. AI-driven profiling and predictive modeling revolutionize decision-making.
Our initial step was to develop a robust and targeted data strategy to define
ideal provider profiles and map them to ideal patient recruitment targets. The strategy included the following solutions:
The strategy was successful in mapping providers who had patients likely to register and participate in clinical trials for the rare disease.
THE RESULTS
Highly precise and efficient clinical trial recruitment processes
The use of an AI-driven data strategy provided quick and accurate results, and allowed our client to increase their patient recruitment rate, improve patient registration and reduce costs.
- Highly Predictive Patient Attributes: By defining ideal profiles we were able to identify highly predictive signals/attributes we were able to quickly and easily reach and engage patients with clinical trial information.
- Increased patient recruitment rates: by comparing recruitment efforts to similar trials, we were able reduce the amount of time spent recruiting by identifying and reaching patients.
- Improved patient selection. We were able to identify providers with patients with the right predictors and criteria to meet clinical trial recruitment eligibility.
- Reduced costs. Through our personalized data strategy, the client was able to reduce the costs of clinical trial recruitment by identify potential patients likely to register, and to contact them directly about the opportunity to participate.
6
Shared Patient Profile
Attributes Identified
36%
Increase in clinical
trial recruitment rate
4500+
Clinical Trial Patients
Defined and Identified
27%
Reduction in clinical trial
recruitment costs