Medical Image Worklist Assignment Using Study Complexity Balancing
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Solution Overview
Problem
Existing PACS systems inequitably distribute medical image studies among radiologists, leading to frustration among efficient physicians and stress for more deliberative ones due to unequal workload and inefficient assignment methods.
Innovation Solution
A medical image study assignment application that calculates a relative complexity value for each study based on factors like RVUs and user preferences, then equitably assigns studies to users using coefficients, ensuring fair distribution and efficient workload management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If medical image studies are distributed using traditional PACS systems, then studies can be assigned to radiologists, but the distribution is inequitable leading to workload imbalance and physician frustration
Solution Approach 1:
The system transforms the workload distribution problem by changing parameters from simple first-come-first-served assignment to a multi-parameter evaluation system that considers study complexity (RVUs), user preferences, and workload balance coefficients. This parameter transformation enables equitable distribution while maintaining physician productivity.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring workload distribution, comparing actual assignments against ideal equitable distribution, and adjusting future assignments based on this feedback. The equitable distribution algorithm uses feedback from past assignments and current workload states to optimize future study allocations.
2Device complexity
If studies are assigned without considering complexity, then assignment process is simple, but efficient physicians become frustrated and deliberative physicians experience stress
Solution Approach 1:
The system addresses physician stress by changing the assignment parameters to include study complexity metrics (RVUs) and user-specific coefficients. This transformation ensures that complex studies are appropriately distributed, reducing frustration among efficient physicians and stress among deliberative ones, while the automated calculation keeps the process manageable.
Solution Approach 2:
The system introduces an intermediary equitable distribution algorithm that mediates between study characteristics and physician capabilities. This intermediary layer calculates optimal assignments by considering RVUs, user preferences, and workload balance, thereby shielding physicians from the harsh realities of uneven workload distribution.
3Ease of manufacture
If traditional assignment methods are used, then implementation is straightforward, but workload distribution is unequal and inefficient
Solution Approach 1:
The system improves workload distribution efficiency by transforming the assignment methodology from simple chronological allocation to a parameter-driven approach. The algorithm processes multiple parameters (RVUs, user coefficients, workload balance) to generate optimized assignments, achieving superior efficiency despite increased computational complexity.
Solution Approach 2:
The equitable distribution system operates autonomously, automatically calculating optimal study assignments without requiring manual intervention. The system self-adjusts based on input parameters and continuously optimizes distribution, eliminating the need for manual workload balancing while maintaining high efficiency.
Data Source
AI summary
Disclosed are various embodiments for equitably assigning medical images for examination. Data describing medical image studies pending examination are obtained from a medical data server. A relative complexity value is determined for each of the medical image studies based on an average amount of time to perform a particular type of image study. The medical image studies are assigned for examination by a respective user based on preferences associated with the respective user and the relative complexity value determined for each medical image study. A user interface is rendered by a client device, where the user interface includes a respective user worklist for each user.