Medical Data Scheduling for Doctor Assignment and AI Priority Resolution
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Solution Overview
Problem
Existing medical data scheduling systems face challenges in optimizing the scheduling of medical data analysis by doctors, considering various parameters such as doctor availability, medical condition specialization, urgency of patient data, and budget constraints, while also addressing issues with subjective priority rankings and conflicting AI system prioritizations.
Innovation Solution
A dynamic scheduling system that incorporates multiple information sources and constraints to optimize the scheduling of medical data analysis. This system includes a scheduler that creates schedules for assigning medical data to available doctors, taking into account various objectives and constraints, and also provides techniques for equalizing scores associated with medical data to ensure fair operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple AI systems are incorporated to improve interpretation times for specific imaging studies, then interpretation efficiency is improved, but priority conflicts arise that hurt global interpretation times
Solution Approach 1:
The patent combines multiple AI prioritization systems into a single unified scheduling framework that coordinates their outputs. The scheduler integrates priority signals from different AI systems and resolves conflicts by optimizing for global interpretation time rather than allowing individual AI systems to operate independently, thus merging their capabilities while eliminating priority conflicts.
Solution Approach 2:
The scheduling system dynamically adjusts priorities based on real-time hospital workload, doctor availability, and urgent cases. Rather than using static priority rankings from AI systems, the scheduler continuously optimizes the schedule to minimize global interpretation time, adapting to changing conditions and resolving priority conflicts dynamically.
2Ease of manufacture
If subjective priority rankings are used to schedule medical data analysis, then ease of implementation is improved, but scheduling optimization is reduced due to abuse and inability to compare urgency
Solution Approach 1:
The patent introduces an objective scheduling framework that acts as an intermediary between subjective priority requests and actual scheduling decisions. The scheduler translates subjective urgency assessments into an optimized global schedule by considering hospital workload, doctor availability, and multiple objectives, preventing abuse while maintaining ease of use through automated optimization.
3Device complexity
If simple scheduling methods like first-in-first-out queue are used, then device complexity is reduced, but scheduling optimization is insufficient for multiple parameters
Solution Approach 1:
The patent transforms the scheduling problem from a simple queue-based system to an optimization problem with multiple parameters including doctor availability, medical condition specialization, urgency, and budget constraints. By changing the approach from first-in-first-out to multi-objective optimization, the system achieves superior scheduling performance while managing complexity through automated algorithms.
4Ease of operation
If scores from different devices are used without equalization, then ease of operation is improved, but scheduler operation is skewed due to non-unitary score computation
Solution Approach 1:
The patent applies score equalization techniques to bring scores from different AI systems and devices to a common scale or reference frame. This ensures that all priority scores are comparable and weighted appropriately in the optimization process, preventing any single device's scoring methodology from skewing the overall scheduler operation while maintaining ease of use through automated normalization.
Data Source
AI summary
A scheduling system includes: a plurality of input devices configured to output medical data, a workforce storage, configured to store working characteristics of a plurality of doctors, and a scheduler configured to receive as input data related to the medical data and the working characteristics, and configured to provide as output a plurality of schedules for the plurality of doctors for analysing the medical data.