Adaptive Order Management for Genomic Testing
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Solution Overview
Problem
Current cancer treatment planning is complex and inefficient due to the need for multiple specialists, extensive data analysis, and the challenge of managing numerous cancer types and treatment options, leading to delays and difficulties in optimizing treatment selection for individual patients.
Innovation Solution
An adaptive order management system that uses order management engines and an order hub to automate the processing of complex orders for genomic testing and treatment planning, including microservices that perform specific tasks and generate reports, while tracking dependencies and item completion to facilitate timely and accurate treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple specialists and extensive data analysis are used for cancer treatment planning, then treatment selection accuracy is improved, but processing time and system complexity increase
Solution Approach 1:
The system segments the complex cancer treatment planning process into discrete, manageable order items that can be processed independently and in parallel. Each order item represents a specific task or data requirement, allowing multiple specialists to work simultaneously on different segments without creating bottlenecks, thus maintaining high accuracy while reducing overall processing time.
Solution Approach 2:
The system performs preliminary actions by pre-defining order templates and item structures that capture all necessary data requirements and dependencies before the actual treatment planning begins. This preparation work is done once and reused across multiple cases, eliminating the need to re-establish the same analytical frameworks repeatedly, thereby reducing processing time without compromising accuracy.
2Measurement precision
If multiple specialists and extensive data analysis are used for cancer treatment planning, then treatment selection accuracy is improved, but device complexity increases
Solution Approach 1:
The system divides the complex multi-specialist coordination into segmented order items with clear definitions and boundaries. Each item is assigned to specific specialists or systems, reducing the complexity of inter-specialist coordination while maintaining the comprehensive analysis required for accurate treatment selection.
Solution Approach 2:
The system implements universal order templates and item structures that can accommodate multiple specialists, data types, and analysis methods through a single unified framework. This multi-functional approach allows the same system infrastructure to handle diverse treatment planning scenarios, reducing overall system complexity compared to having separate specialized systems for each specialist.
3Adaptability or versatility
If complex orders with multiple dependencies are processed manually, then flexibility in handling individual cases is maintained, but productivity decreases
Solution Approach 1:
The system implements dynamic order processing where the sequence and activation of order items are automatically adjusted based on real-time dependency satisfaction and case-specific requirements. This allows the system to adapt to individual case needs while maintaining high productivity through automated dependency management and parallel processing of independent items.
Solution Approach 2:
The system continuously monitors the completion status and dependencies of order items, providing real-time feedback that triggers automatic updates to the processing sequence. This feedback mechanism ensures that flexibility is maintained by responding to actual case progress while preserving productivity through automated rather than manual sequence adjustments.
4Loss of information
If real-time tracking of order status is implemented, then visibility and coordination are improved, but system complexity increases
Solution Approach 1:
The system implements self-service tracking where order items automatically report their own status changes and dependency satisfaction to the central coordination system. This eliminates the need for complex external monitoring mechanisms, as each order item serves itself by providing status information, thereby improving visibility without proportionally increasing system complexity.
Data Source
AI summary
A genomic test processing system and method employ an order management engine and one or more order processing engines, the order processing engines including a receiving engine, an execution engine, and a broadcasting engine. The receiving engine receives a state of an order from the order management engine. The execution engine determines a sequence of steps to advance the received state of an order to a final state, iteratively designates each step of the sequence of steps as completed before initiating the next step of the sequence of steps, and advances the state of the order to a final state when a last step of the sequence of steps is completed. The broadcasting engine broadcasts the final state of the order to the order management engine. The order management engine causes one of the order processing engines to generate a next-generation sequencing report from the final state of the order.