We aim to efficiently extract information from a vast pharmaceutical Standard Operating Procedures (SOPs) collection. This involves quickly summarising the content and creating new SOPs as required. To assimilate all relevant information effectively, we are working with AWS Kendra on a large corpus of documents. At the heart of our strategy lies a sophisticated large language model, which drives our powerful question-and-answer platform. Despite the enormity of the data, our foremost priority is to provide accurate and trustworthy answers. We ensure clients receive comprehensive and reliable information from our extensive database with our dedication to precision.
About the US Health Insurance Industry
Traditional OCR Systems
Traditional insurance processing presents significant process challenges - requiring more time and resources. This is further compounded by the staggering amount of denied health insurance claims in the United States of America, totalling over $262 billion annually. On top of that, there is a 27% error rate in patient registration and insurance processing, costing an additional $71 billion. These erroneous transactions comprise 1/60th of US healthcare spending and a third of hospital administrative costs.
SOPs must include all requirements for drug identity, strength, quality, and purity.
We used a blend of vision AI and Deep Learning to solve the customer's challenge. Here is a breakdown of the steps we used:
Step 1: Unlocking Valuable Insights through LLMs
Our innovative solution offers a highly scalable system that can efficiently process and analyse documents using the advanced capabilities of the AWS Kendra Platform. By utilizing powerful large language models (LLM), we can extract valuable insights through a natural question-and-answer interface. One of the key features of our solution is its ability to automatically update data from various sources, including websites and enterprise knowledge bases. This ensures that our document store is always up-to-date, allowing for real-time insight generation.
Step 2: Providing intuitive and concise explanations for user queries
Our sophisticated reasoning engine plays a crucial role in delivering accurate results. By considering the context of the conversation, it intelligently queries relevant documents to provide intuitive and concise explanations for user queries.
Step 3: Providing Intuitive Interface for Effortless Exploration
In addition, we provide an intuitive interface that allows users to easily navigate specific sections of documents and browse content related to their queries. This feature is particularly beneficial for pharmaceutical manufacturing standard operating procedures (SOPs), as our reasoning engine constructs an ontology/catalogue of SOPs for effortless exploration of similar documents.
The workflow speed has been enhanced, resulting in improved efficiency.
Detailed processes for specific tasks have been simplified, ensuring clear expectations and promoting consistent and efficient operations.
The ability to replicate processes has facilitated growth, allowing the team to dedicate more time to improvement and skill development.
Process standardization has increased efficiency by identifying areas for optimisation and automation while ensuring that all necessary procedures are properly documented.
By maintaining high-quality standards, consistent excellence in customer service is achieved. This also supports team training efforts and enables them to take breaks without compromising the integrity of the business.
Our platform revolutionises manufacturing workflows by providing tailored Standard Operating Procedures (SOPs) and flexible data solutions. SOPs play an important role in managing complex customer needs and navigating the ever-changing drug development landscape. Our approach lets us quickly identify the most suitable SOP for each unique use case.
What distinguishes our solution is its exceptional flexibility. It seamlessly accommodates various data formats, including documents, tables, URLs, and enterprise databases. This adaptability ensures that our solution remains effective and efficient regardless of the data type.
We take great pride in harnessing the power of open source Large Language Models (LLMs) to drive our innovation. One notable advantage of this approach is data isolation, which ensures the confidentiality of your information while delivering accurate and insightful results.
The Akaike Edge
Inbuilt libraries, DL models with transfer learning capabilities