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

Introducing Fast Healthcare Interoperability Resources (FHIR)

Despite the widespread usage and adoption of Electronic Health Records (EHRs), one-third of providers, payers, and care teams struggle to exchange healthcare data. A patient may go through multiple doctor visits, routine check-ups, and lab tests over time, and we should treat all this data as essential. But this data is siloed and doesn’t provide a central patient view, which thus makes the goal to catalog the entire patient’s medical journey.

Healthcare data interoperability is a step toward combining health data across various disparate systems and sites to help healthcare professionals to spend more time with their patients rather than performing healthcare data collection.

Although healthcare organizations define interoperability standards, it is not enough. While data standards have been available in the past, they have not been sufficient to achieve full interoperability. For interoperability to...