AI Case Complexity Index for Medical Image Worklist Routing
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Solution Overview
Problem
Imaging service providers face challenges in optimizing the efficiency and throughput of image reading and reporting while ensuring quality of care, as existing methods lack an objective metric for assigning cases to the right readers and managing worklists without analyzing actual imaging data.
Innovation Solution
A neural network is trained to calculate a case complexity index (CCI) based on medical images and reports, considering factors like complementarity, contradiction, and inconsistency, which is used to determine the complexity of downstream tasks and optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workflow orchestration software is used to implement processing rules for case assignment, then throughput and efficiency are improved, but the assignment is made without analysis of actual imaging data leading to suboptimal resource allocation
Solution Approach 1:
The system performs preliminary analysis of actual imaging data before case assignment by calculating a case complexity index (CCI) that quantifies the difficulty of reading specific imaging studies. This pre-assessment enables informed routing decisions that match case complexity with reader expertise and availability, optimizing both throughput and assignment quality simultaneously
2Ease of operation
If physicians pick cases from worklist based on preferences and simple criteria, then ease of operation is improved, but optimal assignment of medical images to practitioners is not achieved
Solution Approach 1:
The system replaces manual physician preference-based selection with an automated intelligent routing mechanism that calculates CCI scores and matches cases to readers based on objective complexity metrics, reader expertise, and availability. This substitution maintains ease of operation through automated decision-making while dramatically improving reading efficiency and resource optimization
3Reliability
If AI tools are used to mark cases with urgent findings, then priority identification is improved, but the overall effort involved in reading the entire worklist is not reduced
Solution Approach 1:
The system performs preliminary CCI calculation for all cases in the worklist before reading begins, enabling readers to prioritize not only urgent cases but also complex cases that require more time and expertise. This pre-assessment allows strategic planning of the reading workflow to minimize total reading time while maintaining high reliability in identifying both urgent and complex cases
Data Source
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AI summary
The present invention relates to an AI-based calculation of a case complexity index, CCI. For training a neural network, NN, the method (100) may comprise receiving (S102) training data, comprising: a medical image (I) of a set of medical images and optionally a related report (R) for the medical image; and a CCI for the medical image (I). The method (100) may further comprise training (S103) the NN for providing a trained NN, which is configured for determining the CCI for a medical image (I) by adjusting weights and biases of the NN such that a loss function is minimized.