Atomized Coverage Graph for Dynamic Health Insurance Rebalancing
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Solution Overview
Problem
Current insurance systems lack the ability to provide on-demand, personalized coverage that aligns with individual health needs, leading to inefficiencies and increased costs due to annual coverage based on broad service categories, which does not account for individual disease progression or epidemiology.
Innovation Solution
The Use Determination Risk Coverage Datastructure (UDRCD) introduces a self-evolving atomized coverage graph data structure that allows for on-demand coverage based on specific conditions, enabling personalized insurance decisions by linking coverage to clinical condition objects, treatment paths, and provider objects, utilizing data science tools to assess risk and provide condition-specific coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If annual coverage based on broad service categories is used, then system simplicity is maintained, but personalization and alignment with individual health needs deteriorate
Solution Approach 1:
The patent segments broad service categories into atomized coverage units tied to specific clinical conditions, treatments, and providers. This allows the system to maintain operational simplicity through standardized atomic units while achieving personalization by selectively combining these units based on individual health needs and disease progression patterns.
Solution Approach 2:
The coverage structure transitions from static annual categories to dynamic, on-demand atomized coverage that can be adjusted in real-time based on changing health conditions. The system enables continuous modification of coverage to align with individual disease progression and epidemiological data while maintaining systematic organization through the atomized framework.
2Productivity
If atomized coverage graph data structure is implemented, then coverage detection efficiency is improved, but data structure complexity increases
Solution Approach 1:
The patent divides the coverage determination problem into discrete atomic units (conditions, treatments, providers) organized in a graph structure. This segmentation enables efficient detection by allowing the system to evaluate individual atomic coverages independently and combine results, rather than processing broad service categories as monolithic blocks.
Solution Approach 2:
The atomized coverage graph serves as an intermediary data structure that bridges raw health data and coverage decisions. It introduces standardized nodes and edges that facilitate efficient traversal and evaluation, transforming complex coverage determination into a structured graph processing problem that can be solved algorithmically.
3Speed
If on-demand coverage detection is implemented, then responsiveness to individual needs is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary organization of coverage data into atomized units and pre-establishes the graph structure with all possible condition-treatment-provider relationships. This upfront preparation enables rapid on-demand queries by allowing the system to traverse pre-structured paths rather than computing coverage decisions from scratch for each request.
Solution Approach 2:
The patent applies local optimization by evaluating coverage only for specific atomic units relevant to the individual's condition, rather than processing entire service categories. This localized approach reduces computational resources by focusing evaluation only on the subset of atomized coverages that apply to the specific health scenario at hand.
Data Source
AI summary
The Use Determination Risk Coverage Datastructure for On-Demand and Increased Efficiency Coverage Detection and Rebalancing Apparatuses, Methods and Systems (“UDRCD”) transforms coverage enrollment request, event signal, ACGG request, search request inputs via UDRCD components into coverage enrollment response, add-in recommendation, ACGG response, search response outputs. A set of clinical conditions is determined. A set of treatments is determined for each clinical condition. Treatment paths data that specifies a set of treatment paths, wherein each treatment path comprises an ordered subset of treatments, is determined for each clinical condition. Providers are determined for each treatment. Practice patterns data that specifies, for each clinical condition treated by each provider, how likely the respective provider is to utilize each of the treatment paths is determined. An atomized coverage graph data structure is generated that includes a set of clinical condition objects, a set of treatment objects, and a set of provider objects.