AI EOB Document Conversion System
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Solution Overview
Problem
The manual processing of Explanation of Benefits (EOB) documents into EDI 835 transaction sets is time-consuming and error-prone due to non-standardized formats, requiring complex initial setup and continuous manual interventions, and existing automated solutions are not computationally efficient or easy to configure.
Innovation Solution
The use of artificial intelligence techniques, including autoencoders, Latent Dirichlet Allocation, and modified Non-max Suppression algorithms, to classify and extract data from scanned EOB documents, automate the conversion process, and generate EDI 835 records, reducing computational complexity and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processing methods are used to convert EOB documents into EDI 835 transaction sets, then flexibility in handling non-standardized formats is maintained, but processing time increases and error rates rise
Solution Approach 1:
The patent replaces manual mechanical processing with an automated system combining OCR technology and machine learning algorithms. The system automatically extracts data from scanned EOB images, classifies document types, and converts them to EDI 835 format without human intervention, thereby eliminating time loss while maintaining high accuracy through intelligent validation.
Solution Approach 2:
The system enables self-service processing where the EOB conversion system automatically handles classification, data extraction, and validation without requiring manual configuration or intervention. The machine learning models self-adjust to different EOB formats through training, and the system autonomously manages the entire conversion workflow from image input to EDI output.
2Productivity
If existing automated solutions are implemented, then processing speed improves, but computational complexity and configuration difficulty increase
Solution Approach 1:
The patent transforms the complex classification problem into a simplified parameter-based system by training machine learning models on labeled EOB data. The system learns to identify document types, payers, and data element locations through parameter optimization during training, enabling fast automated processing without requiring complex manual configuration rules.
Solution Approach 2:
The system performs preliminary training actions offline where machine learning models are trained on extensive datasets of various EOB formats before deployment. This preliminary action pre-configures the system to handle different formats automatically, eliminating the need for complex runtime configuration and reducing operational complexity while maintaining high processing speed.
3Ease of manufacture
If traditional OCR methods are used for data extraction, then implementation is simple, but data accuracy decreases due to image quality variations
Solution Approach 1:
The patent introduces machine learning classification models as intermediary components between OCR and data extraction. These models first classify EOB types and identify relevant regions before OCR is applied, directing OCR to focus on specific areas with appropriate parameters. This intermediary step significantly improves extraction accuracy by adapting OCR behavior to different document formats and quality conditions.
Data Source
AI summary
Embodiments for automatically converting printed documents into electronic format using artificial intelligence techniques disclosed herein include: (i) receiving a plurality of images of documents; (ii) for each received image, using an image classification algorithm to classify the image as one of (a) an image of a first type of document, or (b) an image of a second type of document; (iii) for each image classified as an image of the first type of document, using an object localization algorithm to identity an area of interest in the image; (iv) for an identified area of interest, using an optical character recognition algorithm to extract text from the identified area of interest; and (v) populating a record associated with the document with the extracted text.