Large Language Model for Clinical Data Summarization
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Solution Overview
Problem
Current clinical workflows are inefficient due to the time-consuming process of manually searching and retrieving patient data from various databases, which are often stored as unstructured data using different nomenclature, leading to scalability issues with semantic mapping across different clinical sites, regions, and countries.
Innovation Solution
The use of a large language model to generate summaries of patient data retrieved from multiple databases, where prompts comprising patient data and instructions are received, and responses are generated based on these instructions, enabling efficient summarization and interaction with clinicians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional approaches are used to connect data from various patient databases, then data can be displayed in a patient dashboard, but the system is not scalable due to different semantic mapping requirements for each clinical site, region, and country
Solution Approach 1:
The patent introduces an intermediary layer (the processing system with natural language generation capabilities) that sits between the diverse patient databases and the user interface. This intermediary automatically maps and translates data from different databases using standardized approaches, eliminating the need for custom semantic mapping at each clinical site while maintaining scalability across different regions and countries.
Solution Approach 2:
The system implements a universal data processing approach that can handle multiple database types and formats through a single standardized interface. The natural language generation system is designed to work with various patient databases regardless of their specific structure or nomenclature, providing a multi-functional solution that scales across different clinical sites without requiring site-specific customization.
2Productivity
If clinicians manually search and retrieve patient information from various databases, then all relevant patient data can be obtained, but the process is time-consuming and inefficient
Solution Approach 1:
The system enables self-service by automatically retrieving, processing, and presenting patient data without requiring manual intervention from clinicians. The processing system autonomously queries multiple databases, integrates the data, and generates natural language summaries, allowing clinicians to obtain comprehensive patient information instantly without spending time on manual data gathering.
Solution Approach 2:
The system performs preliminary actions by pre-processing and organizing patient data from multiple databases before the clinician needs it. Data is automatically retrieved, integrated, and transformed into readable formats in advance, so when the clinician accesses the system, the information is already prepared and presented, eliminating the time-consuming manual search process.
3Adaptability or versatility
If patient data is stored as unstructured data using different nomenclature in various databases, then data can be stored flexibly, but manual searching and semantic mapping become inefficient
Solution Approach 1:
The processing system acts as an intermediary that bridges the gap between unstructured data storage and structured data retrieval. It automatically interprets and maps unstructured data from various databases using standardized approaches, translating different nomenclatures into a common format without requiring changes to the original storage flexibility while enabling easy retrieval through natural language queries.
Data Source
AI summary
Systems and methods for generating a response summarizing patient data are provided. One or more prompts, comprising 1) patient data retrieved from one or more patient databases and 2) instructions, are received. A response summarizing the patient data is generated based on the instruction using a large language model. The response is output.