Optimize clinical trial screening process with AI

23%
reduced analyses flow and additional validation tests
70%
faster identification of qualified patients
3x
increase in eligible patient pool
Optimize clinical trial screening process with AI | Crunch-IS Case Study
Our team leveraged cutting-edge AI technologies to revolutionize clinical trial screening and accelerate patient selection
Industry:

Pharma

Location:

Europe

Team Size:

1 Project Manager, 2 AI Engineers, 1 Full-stack Developer, 1 SME

Duration:

4 months

Technologies
ScispaCy
BERT-based model
Scikit-learn
TensorFlow
FHIR
Kubernetes / Docker
01

About the Client

Collaborating with a leading pharmaceutical company, our team leveraged cutting-edge AI technologies to revolutionize clinical trial screening. This initiative was aimed to streamline and accelerate patient selection for trials while ensuring precise matching of candidates with study criteria.

Optimize clinical trial screening process with AI | Crunch-IS Case Study
02

Project Overview

The goal of this project was to enhance the clinical trial screening process by utilizing AI technologies. To achieve our goal, we implemented advanced AI solutions:

Natural Language Processing (NLP)

We implemented advanced NLP algorithms to analyze vast volumes of unstructured medical records, extracting relevant information such as patient demographics, medical history, and eligibility criteria.  

Predictive Modeling

Our AI models employ predictive analytics to assess patient characteristics against trial requirements. This involved the development of a robust predictive model using machine learning algorithms, allowing for real-time predictions of patient eligibility.   

Semantic Interoperability

To overcome data silos and ensure seamless integration with various healthcare systems, we implemented semantic interoperability. This enabled the AI system to understand and interpret diverse data formats and standards, fostering efficient data exchange.

03

Client’s Results

The screening process automation led to cost savings, minimizing manual efforts. Also, the integration of advanced technical solutions optimized the patient recruitment process.

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