Machine Learning Model for Clinical Assessment Automation

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Solution Overview

Problem

Current methods for automating clinical assessments are inadequate due to challenges in accurately transcribing and diarizing speech data, and in consistently rating and reviewing clinical assessments, which are prone to human error and require significant time and resources.

Innovation Solution

A computer-implemented method using a machine learning model that conditions the output based on a template for clinical assessments, allowing for higher-quality interpretation and analysis of assessment data, including transcription, diarization, rating, and review, with reduced human involvement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated transcription methods are used, then productivity is improved, but measurement precision deteriorates

Engineering Contradiction:

Improvetranscription speedVSAvoidtranscription accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent introduces an intermediary step where automated transcription output is refined through automated rating and review processes. The system uses machine learning models to act as intermediaries between raw transcription and final assessment scoring, improving precision without sacrificing the productivity gains of automation.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary automated transcription to capture all speech data, then applies subsequent automated processing steps (diarization, rating, review) to progressively refine the accuracy. This preliminary action allows high-speed capture followed by systematic precision improvement.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If manual transcription and rating are used, then measurement precision is improved, but productivity deteriorates

Engineering Contradiction:

Improveassessment accuracyVSAvoidassessment throughput

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system implements self-service automation where machine learning models perform transcription, diarization, rating, and review tasks that would otherwise require human operators. The automated system serves itself by using the output of one processing stage as input to the next, eliminating the need for manual intervention while maintaining high precision through multiple automated quality checks.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical human processes of listening, transcribing, rating, and reviewing with automated computational systems. Machine learning models substitute for human cognitive processes, enabling high-throughput processing while maintaining or improving consistency and accuracy through algorithmic precision.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If complex multi-stage processing is implemented, then measurement precision is improved, but device complexity deteriorates

Engineering Contradiction:

Improverating consistencyVSAvoidsystem architecture

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the complex assessment process into distinct modular stages: transcription, diarization, rating, and review. Each stage is handled by specialized machine learning models that process specific aspects of the data independently. This segmentation allows high precision through specialized processing while managing complexity through modular architecture where each component can be developed and maintained separately.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system merges multiple processing functions into an integrated automated workflow where the output of one stage automatically becomes the input of the next. By combining transcription, diarization, rating, and review into a unified automated pipeline, the system achieves high measurement precision through comprehensive processing while reducing operational complexity by eliminating manual handoffs between stages.

Inventive Principle:

Principle #5Merging (Combining)

4Reliability

If human personnel are used for assessment, then measurement precision is improved, but productivity deteriorates due to shortage of qualified personnel

Engineering Contradiction:

Improveassessment qualityVSAvoidservice provision capacity

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent creates automated copies of human assessment capabilities through machine learning models trained on human-rated data. The system learns from human expertise and replicates assessment quality through computational models that can process assessments at scale. This copying approach maintains the reliability and quality standards of human assessment while eliminating the productivity limitations imposed by the shortage of qualified personnel.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20250132036A1Method of performing a clinical assessment

Publication Date: 2025.04.24 FRISTED EMIL

AI summary

A computer-implemented method (200) is disclosed for performing a clinical assessment, the method comprising: providing a first input (206) to a machine learning model (208), the first input comprising template data encoding a template for carrying out a part of the clinical assessment; providing a second input (210) to the machine learning model, the second input comprising assessment data recorded during the clinical assessment; wherein the first input is provided to the machine learning model to condition the machine learning model to provide an output (212) based on the second input for use in the clinical assessment; and using the output from the machine learning model to perform the clinical assessment.