Entity Recognition Note Templates for Faster Clinical Documentation

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Solution Overview

Problem

The manual documentation of clinical notes by care providers is time-consuming and tedious, requiring extensive manipulation of computing platforms.

Innovation Solution

A system utilizing a trained entity recognition model to generate content block recommendations for notes, including components for model training, storage, template determination, entity recognition, recommendation generation, and presentation, to automate the clinical note generation process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual documentation is used for clinical notes, then accuracy and completeness of information can be ensured, but time consumption and operational complexity increase significantly

Engineering Contradiction:

Improveaccuracy of clinical note informationVSAvoidtime required for documentation

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by automatically extracting entities and generating note content before the provider needs to document. The entity recognition model pre-processes unformatted text to identify and categorize relevant information, and the note generation component creates draft notes in advance, reducing the time required for final documentation while maintaining accuracy through provider review.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary automated system between the unformatted clinical text and the final structured note. This intermediary includes the entity recognition model that categorizes entities and the note generation component that assembles content, acting as a mediator that reduces manual manipulation while preserving information accuracy through multiple processing stages.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Ease of operation

If manual documentation is used for clinical notes, then control over note content can be maintained, but operational complexity and tedious manipulation increase

Engineering Contradiction:

Improveease of note creationVSAvoidcomplexity of computing platform manipulation

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system enables self-service by allowing the automated note generation component to independently process unformatted text, extract entities, and generate structured notes without requiring extensive manual manipulation. The provider simply needs to review and approve the generated content, significantly reducing operational complexity while maintaining control over the final note.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical system of manual text manipulation and formatting with an automated computational system. The entity recognition model and note generation component substitute for manual typing, formatting, and organization operations, reducing the physical manipulation required while maintaining provider control through review and approval mechanisms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If automated entity recognition is implemented, then note generation speed increases, but system complexity increases

Engineering Contradiction:

Improvenote generation speedVSAvoidcomplexity of automated system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The automated system is segmented into distinct functional components: the entity recognition model for extracting and categorizing entities, the note generation component for assembling notes, and the template selection component for choosing appropriate formats. This segmentation allows each component to be optimized independently for speed while managing overall system complexity through modular architecture.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The entity recognition model serves multiple functions: identifying entities, categorizing them into predefined classes, and providing structured output for note generation. This multi-functionality increases productivity by consolidating multiple processing steps into a single automated component, reducing the need for separate systems while managing complexity through versatile design.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12518100B1Systems and methods to train and utilize an entity recognition model to generate content block recommendations for a note

Publication Date: 2026.01.06 SUKI AI INC

AI summary

Systems and methods to utilize a trained entity recognition model to generate content block recommendations for a note are disclosed. Exemplary implementations may: determine, from note templates stored in electronic storage, a note template to implement for a note based on first current visit information for a scheduled visit for a first subject; provide an information set as the text inputs to the trained entity recognition model; obtain, from the entity recognition model, the categorized entities; determine recommendation template(s) to utilize for configuring content in the note; provide the categorized entities and the recommendation template(s) to a large language model to generate content block recommendations for sections of the note; obtain the content block recommendations; generate the note that includes the content block recommendations; and effectuate, via a client computing platform, presentation of the note.