Automated Lien Dispute Resolution System with ML Predictions
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Solution Overview
Problem
Current methods for resolving lien disputes between healthcare entities and legal entities are time-consuming and resource-intensive, often relying on physical storage and proprietary hardware transfers.
Innovation Solution
A system utilizing machine learning to generate resolution suggestions for lien disputes, integrating a lien reduction interface and a lien resolution interface to automate the workflow, and leveraging a medical billing database to calculate predicted lien resolution datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical storage and proprietary hardware transfers are used for communication between healthcare entities, then secure data exchange is achieved, but time consumption and resource intensity increase significantly
Solution Approach 1:
The patent replaces physical storage media (CD-ROM, portable memory devices) and proprietary hardware transfer mechanisms with an electronic communication system that uses standard network protocols. This substitution eliminates the need for physical media handling while maintaining secure data exchange through authenticated electronic communications, thereby reducing time consumption without sacrificing security.
Solution Approach 2:
The patent introduces a secure messaging system with authentication mechanisms as an intermediary between healthcare entities. This intermediary layer provides secure data exchange without requiring direct physical contact or proprietary hardware transfers, enabling reliable communication through standardized electronic channels that reduce overall process time.
2Reliability
If personnel install and authenticate permissions on client devices to set up secure exchange, then secure communication is established, but time and resources required increase
Solution Approach 1:
The patent implements self-service authentication where the system automatically handles permission installation and authentication without requiring manual intervention from personnel. The authentication mechanism operates autonomously to establish secure communications, eliminating the time-consuming manual permission installation process while maintaining security standards.
Solution Approach 2:
The patent performs authentication and permission setup actions in advance through pre-configured systems and automated protocols. By preparing authentication credentials and communication parameters beforehand, the system eliminates the need for real-time manual authentication, reducing the complexity and time required during actual communication establishment.
3Measurement precision
If manual review and negotiation of lien reduction requests is performed, then accurate resolution is achieved, but productivity decreases
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model continuously learns from resolved lien reduction cases to improve its predictions. The system provides feedback loops that allow it to refine its resolution suggestions based on actual outcomes, thereby maintaining high accuracy while enabling automated rapid processing of lien disputes without requiring manual review of each case.
Solution Approach 2:
The patent uses machine learning models to create virtual copies of human decision-making processes. The model analyzes historical data and generates resolution suggestions that replicate accurate human judgment patterns, enabling automated processing that maintains resolution accuracy while significantly improving productivity by eliminating manual review requirements for routine cases.
Data Source
AI summary
Systems and methods including generating, by a legal entity, via a lien reduction interface, a request to decrease a lien amount for a patient. In addition, the systems and methods may include receiving, by a medical entity, the request to decrease the lien amount. The systems and methods may include reviewing, by a user associated with the medical entity, via a lien resolution interface, the request to decrease the lien amount. Moreover, the systems and methods may include calculating, based on one or more resolved lien reductions associated with the legal entity, via a medical billing database, a predicted lien resolution dataset. Also, the systems and methods may include displaying, based on the predicted lien resolution dataset, via the lien resolution interface, a lien reduction response. Further, the systems and methods may include sending, by the user associated with the medical entity, to the legal entity, the lien reduction response.