Automation of Clinical Study Reports (CSRs)

Narrativa’s Answer to Regulatory Affairs Automation

Narrativa uses Generative AI to transform clinical trial data into CSRs. Generative AI has many applications and can be used for information extraction, answering questions, automatic translation, speech recognition or the generation of natural language. Generative AI should play a big role in the design of clinical trials and the management of reports.

Narrativa Knowledge Graph® combines several characteristics of data management paradigms, such as databases where entity descriptions are interlinked to one another and knowledge bases bearing formal semantics, which can be used to interpret data and infer new facts. Together Narrativa Knowledge Graph® and deep learning algorithms can process huge amounts of data to extract and edit valuable information while interpreting contextual information.

Narrativa’s CSR Automation solution takes into account statistical tests to reject the null hypothesis, confidence interval calculations, risk measurements (relative risk, relative risk ratios), and frequentist approaches, among other data augmentation techniques.

Automation of Clinical Report Writing

The automation of clinical study reports helps pharma companies to create more efficient processes and scale down costs. At the same time, they can better adhere to guidelines and reduce the number of mistakes and, as a result, also the number of reviews. For example, the automation of TLFs and patient safety narratives has proven to be of significant value to our partners in terms of reducing the time, effort, and cost needed by their medical writing teams to create CSRs.

More Features: Variability and Rich Language

Narativa’s CSR Automation Solution is specifically engineered to create quality content in a simple and effortless way. We require a corpus to train our system and adapt it to the appropriate or desired style so that; the final result will have variability and rich language if needed.

We use different deep learning algorithms to extract all the valuable information and ingest large amounts of medical trial data to generate the text. To achieve this in a more accurate way, we extract topics, synonyms, and similar sentences. The goal is to mimic the writing style used in the corpus.

In addition, companies maintain full control of the output. If the final result does not meet the expectations, text adjustments can be quickly made as the process is reiterated.

It is part of Narrativa’s mission to use technology for good, and transforming medical trial data into documentation is certainly a way that artificial intelligence can be democratized for the benefit of humanity.