Time-Based Parameter Optimization With Cross-Entity Value Attribution
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Solution Overview
Problem
Traditional predictive techniques fail to accurately optimize parameter sequences for subjective considerations, such as an individual's quality of life, due to a lack of network connectivity between computing entities within a multi-payer ecosystem, leading to increased risks and complications in understanding optimal treatment sets.
Innovation Solution
Implement a parameter optimization process using real-time optimization models based on outcome-time features, entity attribute sequences, and entity parameter sequences to generate optimized parameter sequences, enabling cross-platform collaboration and recapturing future positive impacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional predictive techniques are used to optimize parameter sequences, then objective measurements can be achieved, but subjective considerations such as quality of life cannot be accurately optimized
Solution Approach 1:
The patent introduces an intermediary system that connects disparate computing entities (payers, providers, patients) through a shared ecosystem. This intermediary enables the transfer and attribution of value across entity boundaries, allowing subjective quality of life measurements to be incorporated into the optimization process while maintaining objective measurement capabilities.
Solution Approach 2:
The optimization system is designed to handle both objective measurements (cost, clinical outcomes) and subjective considerations (quality of life, patient preferences) within a single unified framework. This multi-functional capability allows the system to optimize parameter sequences that simultaneously consider multiple types of value metrics.
2Reliability
If costly interventions are implemented to improve future quality of life, then long-term benefits are achieved, but the entity bearing the cost does not recapture the benefits due to lack of network connectivity
Solution Approach 1:
The patent implements a feedback mechanism through value attribution tokens that allow the originating entity to recapture benefits from downstream savings. When a costly intervention prevents future expenses, the system attributes this saved value back to the entity that made the initial investment, creating a feedback loop that enables rational long-term decision-making.
Solution Approach 2:
The system recovers value that would otherwise be lost to disconnected entities. By implementing cross-entity value attribution, the system prevents the dissipation of benefits across organizational boundaries and ensures that the entity bearing the initial cost can recover and retain the long-term value generated.
3Adaptability or versatility
If parameter optimization considers multiple subjective and objective factors, then optimal treatment sequences can be determined, but the complexity of the optimization model increases
Solution Approach 1:
The patent segments the complex optimization problem into manageable components by introducing modular value attribution tokens and structured data representations. This segmentation allows the system to handle multiple subjective and objective factors through composed, reusable elements rather than monolithic complex processing.
Solution Approach 2:
The system transforms the complex multi-factor optimization problem into a standardized parameter optimization framework by representing quality of life, cost, and other factors as comparable parameters. This parameter transformation enables the use of established optimization techniques while maintaining the ability to consider diverse factors.
4Loss of information
If cross-platform collaboration is implemented to enable value recapture, then network connectivity and benefit sharing improve, but the system complexity and integration requirements increase
Solution Approach 1:
The patent uses digital token representations (value attribution tokens) that replicate and transfer value information across different computing entities and platforms. This copying mechanism enables seamless cross-platform value attribution without requiring complex direct integrations between disparate systems, as tokens can be transmitted and recognized across the ecosystem.
Data Source
AI summary
Various embodiments of the present disclosure provide parameter optimization and collaborative networking techniques for improving traditional disparate computing ecosystem. The techniques may include identifying a condition-specific entity cohort for a data entity that is associated with (i) a condition and (ii) a primary computing entity within a computing entity ecosystem. The techniques include generating a real-time optimization model for the condition using the condition-specific entity cohort and, using the real-time optimization model, generating an optimized entity parameter sequence for the data entity. The techniques include initiating the performance of a prediction-based action and, responsive to the prediction-based action, may include receiving a parameter modification for the data entity, generating a simulated recovery feature for the data entity, and provide access to data indicative of the simulated recovery feature to the computing entity ecosystem.