Intelligent Prior Comparison Study Selection Model

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing systems for selecting prior comparison imaging studies in medical imaging environments face challenges with large datasets, inefficiencies in data transfer, and difficulties in customizing relevancy rules, leading to suboptimal selection and retrieval of relevant studies.

Innovation Solution

The system uses patterns of selection and use of prior comparison studies by reading physicians to create new relevancy relationships, enabling intelligent automatic selection of prior comparison studies based on patient study history, physician usage patterns, and other parameters.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If simple rules-based selection is used for prior comparison studies, then the selection process is simple and fast, but the selection accuracy and relevancy deteriorate when patients have large numbers of prior studies

Engineering Contradiction:

Improveselection speedVSAvoidselection accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system monitors user interactions with selected prior comparison studies and uses this feedback to continuously update and refine the selection model. This feedback loop enables the system to learn from actual usage patterns and improve selection accuracy over time without sacrificing speed, as the model processes are optimized for efficient execution.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system dynamically adjusts selection parameters based on the specific characteristics of each patient's study history and the current clinical context. By changing parameters such as time windows, modality priorities, and body part relevance weights based on monitored usage patterns, the system achieves high accuracy in selecting the most relevant prior studies even when hundreds exist.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If relevancy rules are set too broadly to ensure comprehensive coverage, then more relevant studies are available, but data transfer bandwidth and processing resources are overwhelmed

Engineering Contradiction:

Improverelevancy coverageVSAvoiddata transfer volume

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system extracts and transfers only the most essential prior comparison studies that meet dynamically determined relevancy criteria, rather than transferring all potentially relevant studies. By using the selection model to identify and extract only the top-ranked relevant studies based on monitored usage patterns and current clinical context, the system maintains comprehensive relevancy coverage while significantly reducing data transfer volumes and processing requirements.

Inventive Principle:

Principle #2Taking out (Extraction)

3Extent of automation

If hard-coded relevancy rules are configured into the PACS system, then the selection process is automated, but the system becomes difficult to customize and adapt to different clinical needs

Engineering Contradiction:

Improveselection automationVSAvoidcustomization flexibility

Core Design Contradiction:

Extent of automationVSAdaptability or versatility

Solution Approach 1:

The system transitions from static hard-coded rules to a dynamic selection model that automatically adapts to different clinical needs and user preferences. The model is continuously refined based on monitored user interactions, allowing it to customize selections for different physicians, institutions, and clinical scenarios without requiring manual reconfiguration. This dynamic approach maintains full automation while providing adaptability to varying requirements.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system automatically monitors its own performance through user interaction data and self-adjusts the selection criteria and parameters without external intervention. This self-service capability enables the system to customize and optimize its selection process for different clinical contexts automatically, maintaining both automation and adaptability without requiring manual configuration or programming changes.

Inventive Principle:

Principle #25Self-service

4Loss of energy

If prior comparison studies are transferred late in the workflow, then bandwidth requirements are reduced, but clinical efficiency and diagnostic speed deteriorate

Engineering Contradiction:

Improvebandwidth consumptionVSAvoiddiagnostic time

Core Design Contradiction:

Loss of energyVSLoss of time

Solution Approach 1:

The system performs preliminary selection and prioritization of prior comparison studies using the trained selection model before actual data transfer occurs. By pre-identifying the most relevant studies that need to be transferred, the system enables timely retrieval of critical information without requiring excessive bandwidth for transferring unnecessary studies. This preliminary action ensures that essential prior comparisons are available when needed for efficient diagnosis.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12205290B2Intelligent automatic selection of a prior comparison study

Publication Date: 2025.01.21 MERATIVE US LP

AI summary

Systems and methods for selecting a prior comparison study. One system includes an electronic processor configured to, for a medical image study associated with a patient, select a prior comparison image study. The electronic processor is also configured to automatically determine, based on monitored user interaction with the selected prior comparison image study, a usefulness of the selected prior comparison image study. The electronic processor is also configured to automatically update a selection model based on the usefulness of the prior comparison image study to a user.