Intelligent Resource Bidding Platform for Healthcare Matching
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Solution Overview
Problem
The inability to quickly and efficiently match entities with appropriate resource systems after discharge from initial resource systems leads to bottlenecks in both initial and related resource systems.
Innovation Solution
A method involving a computing system that receives entity attributes, generates consideration datasets using machine-learning models, and matches resource systems with entities based on these datasets, facilitating efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual matching processes are used to locate post-discharge resource systems, then matching accuracy may be maintained, but the time required to locate appropriate resources increases significantly
Solution Approach 1:
The patent replaces manual mechanical matching processes with an automated electronic system that uses machine learning models and data processing to match entities with resource systems. The system automatically receives entity attributes, generates consideration datasets, evaluates resource systems, and identifies matches without human intervention, thereby reducing time loss while maintaining or improving matching efficiency.
2Productivity
If entities remain in initial resource systems due to inability to locate appropriate post-discharge resources, then resource allocation efficiency improves, but bottlenecks form in both initial and related resource systems
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously receives entity attributes, evaluates multiple resource systems using machine learning models, and provides matched recommendations. This feedback loop enables dynamic resource allocation that prevents bottlenecks by proactively identifying and assigning entities to appropriate post-discharge resource systems before capacity issues arise in initial systems.
3Loss of time
If automated matching systems are implemented to quickly locate resource systems, then time loss is reduced, but system complexity increases
Solution Approach 1:
The patent segments the matching system into distinct functional modules: an entity attribute receiving module, a consideration dataset generation module using machine learning models, a resource system evaluation module, and a matching recommendation module. This segmentation allows the complex automated matching process to be managed through specialized sub-systems, reducing overall system complexity while maintaining fast matching capability.
Data Source
AI summary
A method performed by one or more processors of a computing system includes: receiving an entity attribute associated with an entity; generating an entity consideration dataset using the received entity attribute; providing the generated entity consideration dataset to a plurality of resource systems; receiving one or more responses from the plurality of resource systems in response to the provided entity consideration dataset; generating a resource consideration dataset using the received one or more responses; and matching a resource system of the plurality of resource systems with the entity based on the resource consideration dataset.