AI Case Complexity Index for Medical Image Assignment
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Solution Overview
Problem
Imaging service providers face challenges in optimizing the assignment of medical cases to radiologists based on case complexity, leading to inefficiencies and suboptimal use of resources due to the lack of an objective metric for determining the effort and difficulty of image-based tasks.
Innovation Solution
A neural network-based method for calculating a case complexity index (CCI) that evaluates the complementarity and inconsistency between medical images and reports, considering factors like reading time and difficulty, to objectively assess case complexity and guide resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If cases are assigned based on physician preferences and simple criteria (age, care setting, referrer), then assignment process is simple and quick, but assignment efficiency and quality of care deteriorate due to non-optimal case-to-physician matching
Solution Approach 1:
The patent transforms the case assignment process by changing the parameters used for decision-making from simple demographic criteria to a comprehensive complexity index based on multiple imaging features. The CCI quantifies case complexity using radiological findings, anatomical regions, pathology types, and procedural characteristics, enabling optimized assignment that improves both efficiency and quality of care.
Solution Approach 2:
The patent introduces an intermediary computational system that acts as a mediator between case characteristics and physician assignment. This intermediary calculates the CCI by analyzing imaging data and matches cases to physicians based on their expertise profiles, replacing direct physician selection with an objective, data-driven matching process that optimizes workflow.
2Reliability
If AI tools (CADt) are used to mark urgent findings and prioritize studies, then urgent cases are identified, but overall reading effort and time required for the entire worklist are not reduced
Solution Approach 1:
The patent segments the worklist processing into complexity-based groups rather than treating all cases uniformly. By calculating CCI for each case and stratifying them into complexity tiers, the system enables physicians to efficiently process low-complexity cases while allocating appropriate time and resources for high-complexity cases, thereby reducing overall reading time without compromising urgent case identification.
Solution Approach 2:
The patent performs preliminary calculation of the case complexity index for all cases in the worklist before assignment and reading. This preliminary action provides physicians with advance information about case complexity, allowing them to mentally prepare and allocate cognitive resources appropriately, thereby reducing the actual reading time required for each case.
3Ease of operation
If physicians select cases with higher reimbursement over low reimbursement procedures, then personal income is optimized, but overall resource allocation and workflow efficiency deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the CCI system provides objective complexity metrics to physicians during case selection. This feedback replaces subjective reimbursement-based selection with data-driven complexity assessment, guiding physicians to choose cases that match their expertise level and optimizing overall workflow efficiency while maintaining physician autonomy in case selection.
4Reliability
If general radiologists read common findings cases and chest radiologists read rare findings cases, then specialist expertise is utilized, but the system lacks objective metric to control case assignment and computing resource allocation
Solution Approach 1:
The patent resolves the complexity issue by introducing a quantifiable parameter system - the Case Complexity Index - that objectively measures case difficulty based on radiological findings, anatomical regions, pathology types, and procedural characteristics. This parameter transformation converts subjective expertise matching into an objective, automated assignment process that optimizes both interpretation accuracy and resource utilization.
Data Source
AI summary
Systems and method for an AI-based calculation of a case complexity index, CCI. For training a neural network, NN, the method includes receiving training data comprising a medical image of a set of medical images and for example a related report for the medical image and a CCI for the medical image. The method may further include training the NN for providing a trained NN, that is configured for determining the CCI for a medical image by adjusting weights and biases of the NN such that a loss function is minimized.