Diagnostic Lab Load Planning Using Data-Reduced Analyzer Assignment
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Solution Overview
Problem
Large-scale diagnostic laboratories face computational inefficiencies and impracticality in optimizing test assignments across a large number of analyzers, leading to substantial computational burdens and time-consuming optimization processes that can take weeks or longer, making it impossible to perform in extremely large-scale systems.
Innovation Solution
Implementing a data-reduction scheme through successive surrogate optimization, grouping of common test order patterns into meta-samples, and iterative optimization over time periods to optimize test assignments across a large number of analyzers in a computationally-efficient manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If optimization-based load planning is implemented across a large number of analyzers, then test assignment optimization is improved, but computational burden increases substantially
Solution Approach 1:
The patent divides the large-scale diagnostic laboratory into multiple zones or regions, each with its own load planning optimization. Instead of optimizing all analyzers simultaneously as one large system, the computation is segmented into smaller independent or semi-independent optimization problems for each zone, reducing the overall computational burden while maintaining effective test assignment optimization.
2Productivity
If optimization-based load planning is implemented across a large number of analyzers, then test assignment optimization is improved, but computation time increases to weeks or longer
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing load planning solutions for smaller analyzer subsets or zones. When a large-scale optimization is needed, these pre-computed results serve as starting points or templates, dramatically reducing the actual computation time required while still achieving effective test assignment optimization for the entire system.
3Productivity
If conventional optimization methods are used in extremely large-scale systems, then test assignment optimization is attempted, but the process becomes impossible to perform
Solution Approach 1:
The patent introduces a hierarchical dimension to the optimization architecture, with multiple levels of load planning (e.g., zone-level optimizations feeding into system-level coordination). This dimensional approach transforms the intractable single-layer large-scale optimization into a multi-layered problem that can be solved incrementally, enabling scalability to extremely large systems that would otherwise be impossible to optimize.
Data Source
AI summary
Systems and methods include an optimization-based load planning module including a data-reduction scheme for analyzers of bio-fluid samples. The optimization-based load planning module is executable on a computer server and is configured to optimize assay type assignments across a large number of analyzers based on one or more objectives, such as: load balancing, efficient reagent usage, reduced turn-around-time, reduced quality assurance costs, and/or improved system robustness. The optimization-based load planning module uses a data-reduction scheme to generate a load plan comprising computer-executable instructions configured to cause a system controller of a diagnostic laboratory system to assign each of the requested test types to be performed over the planning period to one or more selected analyzers in accordance with the one or more preferences or priorities. Other aspects are also described.