Book Image

Intelligent Document Processing with AWS AI/ML

By : Sonali Sahu
Book Image

Intelligent Document Processing with AWS AI/ML

By: Sonali Sahu

Overview of this book

With the volume of data growing exponentially in this digital era, it has become paramount for professionals to process this data in an accelerated and cost-effective manner to get value out of it. Data that organizations receive is usually in raw document format, and being able to process these documents is critical to meeting growing business needs. This book is a comprehensive guide to helping you get to grips with AI/ML fundamentals and their application in document processing use cases. You’ll begin by understanding the challenges faced in legacy document processing and discover how you can build end-to-end document processing pipelines with AWS AI services. As you advance, you'll get hands-on experience with popular Python libraries to process and extract insights from documents. This book starts with the basics, taking you through real industry use cases for document processing to deliver value-based care in the healthcare industry and accelerate loan application processing in the financial industry. Throughout the chapters, you'll find out how to apply your skillset to solve practical problems. By the end of this AWS book, you’ll have mastered the fundamentals of document processing with machine learning through practical implementation.
Table of Contents (16 chapters)
Part 1: Accurate Extraction of Documents and Categorization
Part 2: Enrichment of Data and Post-Processing of Data
Part 3: Intelligent Document Processing in Industry Use Cases


In this chapter, we discussed the IDP pipeline and how this can apply to healthcare industry use cases, such as healthcare prior authorization, prescription automation, and healthcare claims processing with health risk adjustment.

We then dove deep into healthcare prior authorization use cases with a coverage requirement request and its reference architecture on AWS. We also discussed how to automate filling in a prior authorization form from a clinical data store. Moreover, we discussed how we can automate drug fill information by automating extraction from a prescription document. Finally, we looked at how to process a document for healthcare claims processing with a risk adjustment use case.

In the next chapter, we will extend IDP to additional industry use cases, such as insurance. Moreover, we will dive deep into insurance claims processing use cases and see how IDP can help to automate claims processing.