Generative AI Document Routing for Standardized Evidence-Based Drafting
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Solution Overview
Problem
Conventional systems lack efficient tools for generating documents, particularly in organizations like hospitals, where manual document generation is laborious and non-standardized, especially for handling large volumes of insurance claim denials, and there is a need for standardized processes leveraging large language models to automate tedious tasks.
Innovation Solution
A document generation system utilizing generative AI models, including large language models (LLMs), determines document types, extracts relevant evidence from data sources, and generates documents efficiently, incorporating evidence into templates with standardized structures, reducing human error and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual document generation is used, then flexibility and customization are maintained, but productivity and time efficiency deteriorate significantly
Solution Approach 1:
The system enables self-service document generation by automatically extracting evidence from data sources, selecting appropriate templates, and generating documents without human intervention. The AI model autonomously performs the complete document creation workflow, from evidence extraction to final document output, eliminating the need for manual document preparation while maintaining high customization through template selection.
Solution Approach 2:
The patent replaces manual mechanical document preparation processes with an AI-based automated system. The AI model substitutes human operators who manually gathered evidence, selected templates, and wrote documents, achieving significantly higher productivity while reducing manual labor to zero for the core document generation tasks.
2Loss of time
If manual document generation processes are used, then staff can handle complex cases with judgment, but loss of time and productivity increase
Solution Approach 1:
The AI model serves as an intermediary between raw data sources and final document output. It automatically extracts relevant evidence from multiple data sources, interprets the information, selects appropriate document templates, and generates finalized documents. This intermediary system eliminates the time-consuming manual processes while maintaining document quality and appropriateness.
Solution Approach 2:
The system performs preliminary actions by pre-extracting evidence from data sources and pre-selecting appropriate document templates before the actual document generation is needed. This advance preparation significantly reduces the time required for document creation, as the AI model has already gathered and organized all necessary information and selected the most suitable templates in advance.
3Manufacturing precision
If standardized document templates are implemented, then manufacturing precision and consistency improve, but adaptability to unique cases may worsen
Solution Approach 1:
The system applies local quality by using standardized document templates for the overall document structure while allowing AI-generated content to customize specific sections based on the unique evidence and characteristics of each case. The templates ensure consistent formatting, headings, and structure, while the AI dynamically inserts case-specific evidence and narrative, achieving both standardization and adaptability simultaneously.
Solution Approach 2:
The document templates are designed to be universal and multi-functional, capable of accommodating various types of cases and evidence through a standardized structure. The same template framework can handle different case types by dynamically inserting relevant evidence and adjusting content based on the specific case requirements, thus achieving both consistency across documents and adaptability to unique cases.
4Productivity
If AI models are used for document generation, then productivity increases five-fold, but device complexity increases
Solution Approach 1:
The AI system is segmented into distinct functional modules: evidence extraction module, template selection module, and document generation module. Each module performs a specific function and can be independently optimized or replaced. This segmentation manages the inherent complexity by organizing the system into manageable, functionally-separated components that work together to achieve high productivity.
Data Source
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AI summary
In some examples, systems and methods for document writing and/or document generation are provided. For example, a method includes: identifying one or more document types associated with a request, each document type being associated with a document template; extracting one or more pieces of evidence from one or more data records based on the request; accessing one or more routes for document generations, each route of the one or more routes using one or more template-specific computing models; generating one or more documents using the one or more routes; and outputting a selected document that is selected from the one or more generated documents.