From 2017 to 2021 I served on the board of PRA Health Sciences, a public (NASDAQ:PRAH) clinical research organization that ran clinical trials all over the world with 20,000 employees based in 80 countries (PRA was acquired by ICON plc in 2021). I learned a decent amount about the industry while I was there and I was particularly intrigued with how labor intensive the clinical trial process is. So when foundational LLMs become readily available early this year they seemed a great technology to apply to the CRO world.
CROs are instrumental in driving medical advancements. However, they face hurdles due to labor-intensive tasks and stringent regulatory oversight. A stunning 95.7% of direct costs in CRO operations are attributed to labor. This dependence on human labor for regulatory compliance, document preparation, and data management suggests a significant opportunity for efficiency improvement through AI automation.
Our new company Ryght steps in to address these challenges with GenAI solutions that streamline regulatory compliance processes. Automating the creation and management of regulatory documents, Ryght's AI not only ensures adherence to industry standards but also alleviates the manual labor burden.
Ryght leverages an array of innovations to deliver a unique set of capabilities:
- LLM Orchestration. We don't lock customers into a single flavor of LLM but rather orchestrate the response to user prompts across best in breed across models selected based on their expertise.
- Our exclusive access to a constantly updated database based on the anonymized health records of 200M patients.
- Retrieval-Augmented Generation (RAG), which combines the retrieval of relevant documents with content generation, is ideal for handling extensive datasets and documents typical in clinical trials.
Deep diving into RAG, this method works by retrieving information from a large corpus of documents, subsequently generating accurate and context-aware responses or documents. Ideal for CROs with extensive archives of past trial data, protocols, and regulatory submissions, Ryght's AI can automate complex tasks like drafting trial protocols, summarizing research findings, and preparing regulatory submissions, ensuring high accuracy and compliance.
The application of AI in automating repetitive and complex tasks not only expedites processes but also significantly reduces the potential for human error, a critical factor in a field where accuracy and adherence to regulatory standards are paramount. This increased efficiency is crucial in a sector where timely and precise data processing is key.
Financially, the use of Ryght's AI solutions presents CROs with substantial savings in labor costs, a major operational expense. These savings can be strategically reinvested in research and development, fostering innovation and keeping CROs competitive.
