Context-Aware Note Generation From Multi-Modal Care Content
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Solution Overview
Problem
Existing note-taking systems fail to effectively utilize contextual content in multiple modes, such as images, videos, and audio, to generate a comprehensive summary of caregiver-patient interactions, leading to inefficient documentation of visits and care.
Innovation Solution
A system that utilizes a machine learning model to analyze audio information, caregiver and subject contextual content, and setting information in various modes to generate organized content blocks for a note, incorporating a large language model and vision language models for semantic understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional note-taking systems merely combine contextual content in various modes into one file or folder, then the system complexity is reduced, but the ability to generate comprehensive summary notes is lost
Solution Approach 1:
The system segments contextual content from multiple modes (images, videos, audio, text) into separate processing streams, each handled by specialized components. The machine learning model divides the analysis into identifying entities, relationships, and events separately, then integrates them to generate comprehensive summary notes, resolving the contradiction between simplicity and comprehensiveness.
Solution Approach 2:
The machine learning model acts as an intermediary between raw contextual content in various modes and the final note generation. It processes and transforms multi-modal inputs into structured information that can be synthesized into comprehensive summaries, enabling the system to maintain both simplicity and information completeness.
2Loss of information
If multiple modes of contextual content (images, videos, audio, text) are integrated for analysis, then the comprehensiveness of note generation is improved, but the processing complexity increases
Solution Approach 1:
The machine learning model is designed with multi-functionality to handle various content modes (images, videos, audio, text) through a unified processing framework. It performs entity identification, relationship extraction, and event detection across all modalities, reducing processing complexity while maintaining comprehensive contextual content utilization.
3Manufacturing precision
If a machine learning model is used to analyze and generate content blocks from multi-modal contextual content, then the quality of note documentation is improved, but the computational resources required increase
Solution Approach 1:
The machine learning model performs preliminary actions by pre-identifying entities, relationships, and events in the contextual content before generating final notes. This preliminary processing organizes the multi-modal data into structured formats, improving note documentation quality while optimizing computational resource usage by avoiding redundant processing during note generation.
Data Source
AI summary
Systems and methods to generate notes based on contextual content are disclosed. Exemplary implementations may: obtain audio information that represents sounds captured by a client computing platform, wherein the sounds convey utterances by participants during a conversation session, wherein the participants include at least the first caregiver and the first subject in the first setting; generate a prompt that requests generation of textualized content blocks for inclusion in one or more sections of a note; provide the audio information, first caregiver contextual content for the first caregiver, first subject contextual content for the first subject, and first setting contextual content for the first setting to a context window of a large language model; provide the prompt to the large language model; receive, from the large language model, the textualized content blocks; and populate the textualized content blocks in the note.