Health Indicator Generation Logic Using Payment Transaction Data
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Solution Overview
Problem
Current systems lack an efficient method to generate comprehensive health indicator data that reflects consumers' health-conscious decisions from payment transaction data, which can be used to incentivize healthy behaviors and improve healthcare management.
Innovation Solution
The method involves generating behavioral health indicators based on payment transaction data, de facto health indicators from healthcare sector data, and composite health indicators by integrating both, while ensuring privacy controls through opt-in requests, using consumer identification and health tracking device data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If comprehensive health indicator data is generated from payment transaction data, then the completeness and usefulness of health information is improved, but the complexity of data processing and system integration increases
Solution Approach 1:
The system segments health indicator generation into distinct components: behavioral health indicators from payment transaction data, de facto health indicators from healthcare sector data, and composite health indicators that integrate both. This segmentation allows each component to be processed independently through dedicated logic modules, reducing overall system complexity while maintaining comprehensive health information.
Solution Approach 2:
The patent introduces intermediary logic layers including consumer identification logic that bridges payment transaction data with health indicator generation, and composite health indicator generation logic that mediates between behavioral and de facto health indicators. These intermediaries facilitate integration without requiring direct complex connections between all data sources.
2Measurement precision
If behavioral health indicators are generated from payment transaction data, then the ability to monitor health-conscious decisions is improved, but the difficulty of detecting and measuring health behavior increases
Solution Approach 1:
Consumer identification logic serves as an intermediary that bridges payment transaction data with health indicator generation. This intermediary extracts and processes relevant consumer information from transaction records, transforming raw payment data into meaningful behavioral health indicators without requiring direct complex analysis of spending patterns.
Solution Approach 2:
The system replaces traditional mechanical health monitoring methods (wearable sensors, direct observation) with an information-based approach using payment transaction data. This substitution enables health behavior measurement through existing commercial infrastructure rather than requiring new measurement devices or direct intervention.
3Adaptability or versatility
If health indicator data is communicated to third parties, then the usefulness for healthcare management is improved, but the privacy protection requirements increase
Solution Approach 1:
The system implements preliminary privacy protection by generating privacy control data through opt-in request processing before any health indicator data is communicated to third parties. This advance preparation ensures privacy compliance is built into the data sharing process from the outset, enabling versatile healthcare management applications while pre-establishing protective measures.
4Measurement precision
If historical payment transaction data is stored and processed, then the accuracy of updated health indicators is improved, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary processing by storing historical payment transaction data and generating initial behavioral health indicators in advance. When new transactions occur, the system builds upon previously processed data rather than analyzing all historical data from scratch, significantly reducing processing time while maintaining accuracy through incremental updates.
Solution Approach 2:
The system applies partial processing by focusing on new and changed transaction data rather than reprocessing entire historical datasets. This selective approach processes only the necessary portion of data to generate updated health indicators, reducing time loss while preserving measurement precision through targeted analysis.
Data Source
AI summary
An apparatus and method is disclosed for generating health indicator data such as behavioral health indicators, de facto health indicators, and composite health indicators. Behavioral health indicators represent a degree to which the one or more persons associated with the payment transaction data make health-conscious decisions and are generated based on payment transaction data. Behavioral health indicators may also be generated using a food behavior health indicator, a lifestyle health indicator, an activity health indicator, and a body metric health indicator. De facto health indicators represent a degree to which the one or more persons are observably healthy and are generated based on health sector data. Composite health indicators represent a holistic and complete picture of current and future health and are generated based on behavioral health indicator and de facto health indicator.