Breaking Down the Benefits of Intelligent Report Automation for Pharma
Regulatory compliance reporting is a heavy lift for the healthcare industry. Thankfully, it’s also one of the most promising areas for the use of technological automation, especially in new drug development. It can take several years for drugs to pass through testing and reach government approval, and compliance reporting is a key component of clinical trials for drug development, particularly during Phase 3 testing.
Phase 3 studies are designed to prove a drug offers a treatment benefit to a specific population and typically involve between 300 and 3,000 participants. The study provides most of the safety data (long-term or rare side effects, etc.) for the drug. To complete the study, medical writers compile a series of documents known as clinical study reports (CSRs) that require adherence to regulatory guidelines in terms of structure, content, and format. The production of CSRs takes a team of highly educated medical writers weeks to sometimes months to complete. CSRs contain data and narrative that seek to prove the efficacy and safety of the drug. So while these reports are an absolute critical step in drug development, their data heavy nature makes them the perfect candidate for intelligent report automation (IRA).
What is IRA?
It is a system that can leverage various forms of artificial intelligence (AI), including machine learning, deep learning, natural language understanding (NLU), and most importantly, natural language generation (NLG). IRA tools support data sourcing, data interpretation, data analytics, and narrative and semantic commentary. IRA can deliver reports exponentially faster with more accurate results than manually produced reports, as well as allow medical writers to shift their resources to other high value, non-automated work.
By automating the repetitive parts of CSR reporting and implementing NLG, IRA can reduce report writing time by an average of 30%.
Yseop’s platform, Augmented Analyst, is powered by “intentions”—pre-packaged libraries of reporting typologies and text structures addressing specific medical reporting use cases. Intentions enable multiple narrative versions to be generated from one rule, marking a step forward from traditional template-based systems, which are much less flexible in terms of narrative output. The platform combines different AI technologies, including machine learning, NLG, and NLU, allowing users to customize reporting narratives and feedback to the system. This enables the platform to learn a user’s specific style to improve future text generation.
In our whitepaper, NLG is the future of intelligent report automation, we dive into all this much further. We’re breaking down the benefits of IRA, how to launch a successful IRA project, how IRA will evolve in the future, and so much more. Get your copy here.