Automated Document Indexing for Large Medical File Review
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Solution Overview
Problem
Large files, such as health records, often cause delays and result in missed information during assessments due to manual compilation and review processes, leading to inefficiencies in tasks like insurance claims and medical evaluations.
Innovation Solution
An automated system utilizing artificial intelligence and machine learning to preprocess, classify, and index documents, enabling efficient extraction and organization of relevant medical information from large files, such as electronic health records, through optical character recognition, classification algorithms, and heuristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual compilation and review processes are used for large files, then human assessment can be performed, but delays and missed information occur
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated computer-based system that uses optical character recognition (OCR), classification algorithms, and heuristics to process, classify, and index document pages. This substitution eliminates human review delays while maintaining information accuracy through systematic automated analysis of thousands of pages.
Solution Approach 2:
The system performs preliminary actions by automatically preprocessing, classifying, and indexing all document pages before the actual assessment task. This advance organization creates a structured framework that enables rapid retrieval and analysis of relevant information during the assessment process, eliminating the need for manual compilation delays.
2Loss of information
If large files with several thousand pages are reviewed manually, then complete information can be assessed, but delays occur
Solution Approach 1:
The patent segments the large file into individual page units, each processed independently through preprocessing, classification, and indexing. This segmentation allows the system to handle thousands of pages through parallel processing and systematic organization, maintaining complete information coverage while dramatically improving processing speed compared to manual review of the entire file.
Solution Approach 2:
The system introduces an intermediary indexing structure that bridges the original unprocessed pages and the final assessment output. This intermediary layer organizes pages into classified groups with metadata and relationships, enabling efficient navigation and retrieval of complete information without requiring manual review of all pages.
3Productivity
If automated processing is implemented, then efficiency increases, but system complexity increases
Solution Approach 1:
The complex automated system is divided into distinct functional modules: preprocessing module for OCR and text extraction, classification module for categorizing pages using algorithms and heuristics, and indexing module for organizing pages into structured groups. This segmentation manages system complexity by making each module independent and manageable while maintaining high overall processing efficiency.
4Reliability
If manual compilation into reports is performed, then contextual understanding can be added, but time consumption increases
Solution Approach 1:
The patent replaces manual report compilation with automated generation that uses classification algorithms and heuristics to analyze page content, extract relevant information, and generate structured reports with contextual understanding. This substitution maintains report accuracy through systematic analysis while eliminating the time consumption of manual compilation.
Data Source
AI summary
A document index generating system and method are provided. The system comprises a processor and a memory storing a sequence of instructions which when executed by the processor configure the processor to perform the method. The method comprises preprocessing a plurality of pages into a collection of data structures, classifying each preprocessed page into at least one document type, segmenting groups of classified pages into documents, and generating a page and document index for the plurality of pages based on the classified pages and documents. Each data structure comprises a representation of data for a page of the plurality of pages. The representation comprises at least one region on the page, comprising for each page, normalizing the plurality of pages into a collection of images and a collection of plain text, obtaining vision features from the collection of images and processing the collection of plain text.