UDRCD Atomized Coverage Data Structure for Dynamic Health Plan Rebalancing
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Solution Overview
Problem
Current insurance systems lack flexibility and efficiency in providing coverage, as they typically offer annual, broad service category-based policies that do not align with individual health needs or disease progression, leading to misaligned underwriting and inefficient resource use in healthcare.
Innovation Solution
The Use Determination Risk Coverage Datastructure (UDRCD) introduces on-demand, condition-based coverage that atomizes insurance into relevant events, allowing personalized coverage choices based on individual health needs, utilizing data science and specialized data structures to model disease progression and provide customizable insurance plans through a combination of core coverage and add-ins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If annual, broad service category-based insurance policies are used, then administrative simplicity and ease of operation are improved, but adaptability to individual health needs and alignment with disease progression deteriorate
Solution Approach 1:
The patent segments broad service categories into atomized, condition-specific coverage units that can be individually selected and combined. Instead of offering annual policies covering entire service categories, the system breaks down coverage into discrete medical conditions, procedures, and services that can be customized to match individual patient needs and disease progression patterns.
Solution Approach 2:
The patent implements dynamic coverage that can be adjusted in real-time based on changing health conditions, disease progression, and treatment needs. Coverage can be added, removed, or modified on-demand as patients transition between different health states, allowing the insurance policy to adapt dynamically rather than remaining static for entire policy years.
2Adaptability or versatility
If condition-based, atomized coverage is implemented, then adaptability to individual needs and coverage precision are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent employs a nested data structure where atomized coverage units are organized hierarchically within condition-specific groupings, which are themselves nested within broader service categories. This nested organization allows the system to manage complexity by providing structured layers of abstraction, where detailed condition-level data is organized within manageable hierarchical frameworks.
Solution Approach 2:
The patent uses standardized data templates and reusable coverage definitions that can be copied and configured for different conditions and patients. Instead of creating unique complex data structures for each coverage scenario, the system employs template-based copying of standardized coverage units, reducing overall system complexity while maintaining customization capabilities.
3Adaptability or versatility
If on-demand coverage adjustments are enabled, then responsiveness to health events and coverage relevance are improved, but loss of time for coverage determination and processing increases
Solution Approach 1:
The patent pre-configures atomized coverage units and establishes coverage rules in advance for common conditions and treatment scenarios. When health events occur, the system can quickly match events to pre-defined coverage units rather than determining coverage from scratch, significantly reducing processing time while maintaining on-demand responsiveness.
Solution Approach 2:
The patent implements feedback mechanisms where coverage decisions and patient outcomes are continuously monitored and used to refine coverage matching algorithms. This feedback loop improves the system's ability to quickly and accurately determine coverage over time, reducing processing time as the system learns from accumulated data and optimizes its matching processes.
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 inputs via UDRCD components into coverage enrollment response, add-in recommendation outputs. A coverage enrollment request from a user is obtained. Available options for an enrollment user interface are configured based on the plan sponsor settings. Copay setting selections for individual core coverage services, atomized condition add-in selections, and atomized procedure add-in selections are obtained via the enrollment user interface. A core coverage cost and add-in coverage costs are calculated using the associated modeling data. A user cost for the user is calculated based on the core coverage cost, the subsidization settings for the core coverage cost, the add-in coverage costs, and the subsidization settings for the add-in coverage costs. The enrollment user interface is configured to display the calculated user cost for the user.