Improving Speed and Accuracy in the Analysis of Clinical Data with C3 Generative AI

Challenge

The company allocated nearly 50% of its R&D budget to documentation, covering everything from product research and risk assessments to manufacturing procedures and post-market surveillance. Among these, complaint mapping was one of the most critical and complex tasks, essential for ensuring product quality, driving improvements, and meeting global regulatory standards.

Despite significant investment, the documentation process remained slow and fragmented. Quality engineers (QEs) spent years mastering company-specific terminology and regulatory requirements, while struggling to locate disjointed information across various sources such as Instructions for Use, historical risk documents, compliance guidelines, and customer complaints.

The process often required weeks of work, multiple SME reviews, and still had inconsistencies in documentation quality. This inefficiency increased the risk of variability in output, jeopardizing product quality and raising the potential for patient harm.

Solution

A joint team of developers, data scientists, and quality engineering subject matter experts identified the corpus of documents required for complaint analysis and mapping, the desired output, various input formats, user interfaces, and the most efficient workflows. The team designed the approach that incorporated various innovative techniques, such as advanced document processing and chunking to handle non-standard formats and deep learning to inject domain-specific, contextual information. These techniques enabled minimal pre-processing work from quality engineers and reliable information retrieval and content mapping capabilities that mimic human logics with zero hallucination.

C3 AI also configured complaint mapping output formats and enabled access controls to tailor to the strict requirements of the company in format compliance and security.