GenAI EHR Summarization for Faster Care Management Enrollment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Care managers face overwhelming workloads and administrative burdens, including excessive documentation and challenges in enrolling high-risk subjects in care management programs, which undermines the effectiveness of care management programs and increases healthcare costs.
Innovation Solution
Utilizing a long record API to retrieve and filter unstructured EHR data through GenAI models, transforming it into structured outputs that reduce manual tasks by up to 75%, enabling efficient communication and care plan management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If care managers manually review and process unstructured EHR data, then comprehensive patient information is captured, but workload and administrative burden increase significantly
Solution Approach 1:
The patent replaces the mechanical manual review process with an automated GenAI-based system that processes unstructured EHR data. The system uses natural language processing and machine learning models to automatically extract, summarize, and structure patient information from unstructured text, replacing the need for manual human review while maintaining comprehensive information capture.
Solution Approach 2:
The patent introduces an intermediary automated processing layer between the unstructured EHR data and the care manager. This intermediary system includes GenAI models that act as a mediator to transform raw unstructured data into structured, actionable insights, reducing the burden on care managers while preserving information integrity.
2Reliability
If care managers spend time on detailed chart reviews and enrollment calls, then patient enrollment occurs, but time consumption and administrative burden increase
Solution Approach 1:
The patent applies preliminary action by automatically processing and structuring patient data before care managers need to review it. The system pre-generates structured summaries, identifies eligible patients, and prepares enrollment materials in advance, so when care managers do engage with patients, the groundwork is already completed, reducing time spent on each enrollment interaction.
Solution Approach 2:
The system enables self-service by automatically performing data extraction, summarization, and patient eligibility identification without requiring care manager intervention. The GenAI system serves itself to process vast amounts of EHR data, freeing care managers to focus only on patient communication and enrollment finalization.
3Loss of information
If comprehensive unstructured EHR data is processed, then complete patient picture is obtained, but data processing complexity and resource usage increase
Solution Approach 1:
The patent segments the complex data processing task into distinct modular components: data ingestion modules, GenAI processing modules, structuring modules, and output generation modules. Each segment handles a specific aspect of processing, making the overall system more manageable and maintainable while still processing comprehensive data.
Solution Approach 2:
The patent creates a universal GenAI-based processing platform that can handle multiple types of unstructured EHR data (notes, messages, charts, enrollment forms) through a single integrated system. The multi-functional GenAI models can adapt to different data types and processing requirements, reducing the need for separate specialized systems for each data category.
Data Source
AI summary
The present disclosure relates to generating a structured representation, particularly a summary, for a given subject by leveraging one or more generative artificial intelligence (GenAI) models based on one or more unstructured portions of electronic health record data of the subject. The techniques, as disclosed herein, may utilize a long record application programmable interface (API) to retrieve and process the one or more unstructured portions of electronic health record data of the subject into a semi-structured format. The disclosed techniques further utilize the one or more GenAI models to generate a prompt based on the semi-structured format, extract relevant data elements based on the prompt, and transform the data elements into a strategically reduced summary. The generated summary may assist users in making quick, informed decisions while reducing the need for exhaustive manual chart reviews of the subject.