Guideline-Based Algorithm Modules for Clinical Decision Consistency

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Solution Overview

Problem

Current systems for analyzing and comparing patient data to published guidelines are limited by complexity and require manual application, leading to human error and inconsistency in using up-to-date best practices.

Innovation Solution

An apparatus and method using a machine learning model to generate algorithm modules from structured and unstructured data, incorporating a large language model to process patient data and guidelines, thereby automating the generation of algorithm modules for clinical decision support.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual processes are used to retrieve and apply medical guidelines, then healthcare professionals can interpret and document clinical decisions, but the process consumes significant time and effort and is prone to human error

Engineering Contradiction:

Improveaccuracy of guideline applicationVSAvoidtime to retrieve and apply guidelines

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables self-service by automatically retrieving, interpreting, and applying medical guidelines without requiring healthcare professionals to manually search and interpret them. The processor autonomously accesses the guideline database, compares patient data against guidelines, and generates compliance assessments, freeing clinicians from this administrative burden while ensuring consistent, error-free application of current best practices

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the manual mechanical process of guideline retrieval and interpretation with an automated computational system. The processor acts as an intelligent agent that automatically queries the guideline database, parses medical guidelines, extracts relevant criteria, and applies them to patient data, substituting human cognitive and manual efforts with machine-based automation that operates faster and without error

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of operation

If manual processes are used to apply medical guidelines, then healthcare professionals can make clinical decisions, but consistency in applying the latest guidelines is compromised

Engineering Contradiction:

Improvesimplicity of guideline applicationVSAvoidconsistency of guideline application

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system implements feedback by continuously comparing patient data against the latest medical guidelines stored in the database, automatically assessing compliance, and providing actionable insights to healthcare professionals. This closed-loop approach ensures that clinicians always operate with current best practices while the system learns from and adapts to clinical workflows, maintaining both ease of use and consistent, reliable application of guidelines

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary action by pre-processing and structuring medical guidelines in the database before they are needed for clinical decisions. Guidelines are automatically retrieved, parsed, and organized into machine-readable formats in advance, so when a patient case arises, the system can immediately compare patient data against pre-processed guideline criteria without delay, ensuring consistent and timely application

Inventive Principle:

Principle #10Preliminary action

3Reliability

If complex systems are used to analyze patient data against guidelines, then comprehensive assessment is achieved, but system complexity increases

Engineering Contradiction:

Improvecompleteness of guideline compliance assessmentVSAvoidcomplexity of the analysis system

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent applies segmentation by dividing the complex guideline compliance assessment into distinct modular components: a guideline parsing module that extracts criteria from medical guidelines, a data comparison module that matches patient data against guidelines, and a compliance assessment module that generates results. This modular architecture maintains comprehensive assessment capability while reducing overall system complexity through functional decomposition and independent, reusable components

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20260044709A1Apparatus and method for generating algorithm modules based on a plurality of published guidelines using machine learning

Publication Date: 2026.02.12 NFERENCE INC

AI summary

An apparatus and method for training a machine learning model to receive a plurality of subject data associated with a particular subject, wherein the plurality of subject data comprises structured data and unstructured data, receive a plurality of guidelines, instantiate a machine learning model, wherein the machine learning model is configured to receive the plurality of guidelines as an input and output a plurality of algorithm modules, process the plurality of subject data, wherein processing the plurality of subject data comprises instantiating at least a large language model, wherein the at least a large language model is configured to generate processed subject data by processing the plurality of subject data, generate, using the machine learning model, an output based on the processed data and the plurality of algorithm modules, and display the output, using a client device, through a graphical user interface.