Automated Debt Settlement Forecasting System
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Solution Overview
Problem
Current methods for settling medical debts are often manual, prone to human error, and lack accuracy in predicting settlement prices, leading to inefficiencies and increased costs due to delayed processes and rejections, while also failing to provide clear settlement prices to debtors.
Innovation Solution
The PODS system, a computerized software and process that forecasts provider actions and settlement amounts based on specific variables, allowing for automated debt assumption and settlement, eliminating human error and bias, and enabling debtors to know exact settlement prices from the outset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to settle medical debts, then flexibility in negotiation is maintained, but human error and bias increase, leading to reduced accuracy in predicting settlement prices
Solution Approach 1:
The patent replaces manual mechanical negotiation processes with an automated computer-based system that uses algorithms to predict settlement prices and manage debt resolution. The system automatically processes debt data, calculates settlement amounts, and communicates with creditors and debtors, eliminating human error and bias while maintaining negotiation flexibility through structured computational logic.
Solution Approach 2:
The system enables self-service operation by automatically performing debt analysis, settlement calculation, and communication without requiring continuous human intervention. The automated platform allows debtors to access settlement information and providers to receive negotiation requests through the system's automated interfaces, improving consistency while reducing operational complexity.
2Productivity
If manual debt settlement processes are used, then human judgment can be applied to complex cases, but processing time increases and productivity decreases
Solution Approach 1:
The patent divides the debt settlement process into distinct segmented modules: data collection and validation, debt analysis and calculation, settlement prediction, communication management, and closing. Each module handles a specific function independently, allowing the system to process complex cases through structured computational steps while maintaining overall simplicity and ease of implementation.
Solution Approach 2:
The system manages complexity by transforming qualitative human judgment into quantitative parameters and algorithms. Settlement predictions are based on calculable factors such as debt amount, age of account, provider type, and historical data, converting complex negotiation scenarios into manageable computational parameters that can be processed efficiently and consistently.
3Loss of time
If automated systems are implemented for debt settlement, then processing speed increases, but initial complexity and implementation difficulty increase
Solution Approach 1:
The patent creates a universal automated platform that handles multiple functions within a single system: data collection from diverse sources, validation against multiple criteria, calculation of settlement amounts, prediction of provider actions, and automated communication. This multi-functional design reduces time delays by consolidating processes while managing implementation complexity through integrated architecture rather than separate systems.
Solution Approach 2:
The system performs preliminary actions by pre-processing and validating data before settlement calculations begin. Historical data is pre-loaded, calculation parameters are pre-established, and communication templates are pre-configured, allowing the automated system to process new debt cases quickly without requiring complex real-time decision-making, thereby reducing time loss while simplifying implementation.
4Loss of information
If human negotiators are used, then adaptability to unique circumstances is maintained, but error and bias increase, reducing measurement precision
Solution Approach 1:
The patent incorporates feedback mechanisms where the system continuously learns from actual settlement outcomes to improve its predictions. Provider responses and settlement actuals are fed back into the system to refine algorithms and calculation parameters, increasing the accuracy of settlement price information over time while maintaining ease of operation through automated continuous improvement rather than requiring manual reconfiguration.
Data Source
AI summary
An automated method for resolving a debtor's debt obligation to a creditor uses a rating algorithm that implements predictive analytics to determine an anticipated amount for which the creditor will settle the debt. A debt purchase amount to be offered to the Debtor to have a third-party entity fully assume the Debtor's debt obligation is calculated based on the anticipated amount for which the creditor will settle the debt and operating costs and profit margin of the third-party entity. An offer to the Debtor to have the third-party entity fully assume the Debtor's debt obligation for the debt purchase amount is generated and sent with a document indicating that the Debtor has agreed to transfer the Debtor's debt obligation to the third-party entity pursuant to the offer. The creditor is notified that the third-party entity has assumed responsibility for the Debtor's debt obligation and resolved the Debtor's debt obligation.