Predictive Model Optimizing Clinical Workflow Diagnostic Value
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Solution Overview
Problem
Optimizing clinical workflows in radiology is challenging due to the difficulty in defining and measuring suitable performance indicators, particularly in correlating MRI sequences, patient groups, and staff behavior with the diagnostic value of medical images, which requires extensive effort and is not feasible in routine clinical practice.
Innovation Solution
A predictive model is generated using machine learning techniques to estimate the diagnostic value of medical images based on physician attention and workflow metadata, allowing for adjustments to improve image quality without manual correlation by users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual correlation and evaluation of many acquired medical images is performed to find correlations between workflow parameters and diagnostic value, then measurement precision of workflow optimization is improved, but loss of time and productivity deteriorate due to excessive effort required
Solution Approach 1:
The patent introduces an intermediary system comprising automated image analysis algorithms and machine learning models that act as a mediator between raw medical images and workflow optimization decisions. This intermediary automatically extracts diagnostic value metrics and correlates them with workflow parameters, eliminating the need for manual evaluation while maintaining measurement precision.
Solution Approach 2:
The patent replaces the mechanical manual process of evaluating medical images with an automated computational system. Machine learning models and image analysis algorithms substitute human reviewers, automatically assessing diagnostic value and identifying correlations between workflow parameters and outcomes, thereby eliminating time loss while preserving measurement accuracy.
2Reliability
If comprehensive evaluation of many acquired medical images is performed to establish correlations with workflow parameters, then reliability of workflow optimization is improved, but device complexity and ease of operation worsen due to system complexity
Solution Approach 1:
The patent segments the comprehensive evaluation process into distinct modular components: image acquisition modules, analysis algorithms, machine learning models, and correlation engines. Each module performs a specific function independently, improving reliability through specialized processing while managing complexity through modular architecture that allows independent development and validation of each component.
Solution Approach 2:
The patent creates a universal workflow optimization system that can evaluate multiple types of medical images across different imaging modalities and workflow parameters using the same core infrastructure. The machine learning models and analysis algorithms are designed to handle diverse inputs and outputs, providing reliable multi-functional capability while avoiding the need for separate specialized systems for each imaging type.
3Productivity
If automated predictive models are implemented to estimate diagnostic value from metadata, then productivity is improved by reducing manual effort, but measurement precision may deteriorate due to reliance on metadata rather than direct image evaluation
Solution Approach 1:
The patent performs preliminary automated evaluation of medical images to generate diagnostic value estimates and workflow parameter correlations before manual review or clinical decision-making. This preliminary action captures key patterns and relationships that would otherwise require extensive manual analysis, improving productivity while maintaining measurement precision through sophisticated image analysis algorithms that process images directly rather than relying solely on metadata.
Data Source
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AI summary
A system and method are provided for generating a predictive model for use in optimizing a clinical workflow. The predictive model may be generated as follows. Workflow metadata is obtained which is indicative of the clinical workflow. A viewer log is obtained of an image viewer used by a physician to review one or more medical images. The viewer log may be indicative of one or more viewing actions performed by the physician using the image viewer. A diagnostic value of the one or more medical images is then estimated based on the viewing actions. The above steps are performed for different clinical workflows. A machine learning technique is then applied to the resulting plurality of diagnostic values and plurality of workflow metadata to generate the predictive model. The generated predictive model is predictive of the diagnostic value of medical images acquired by a particular clinical workflow given the workflow metadata of the particular clinical workflow. Advantageously, the predictive model can be used to modify the clinical workflow so as to increase the diagnostic value of the acquired images.