Medical Claims Payment Estimation Using Payer and Patient Patterns
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Solution Overview
Problem
The existing medical claims processing system is complex and inefficient, involving multiple entities and databases, with challenges in verifying patient and payer information, determining billing amounts, and managing reimbursement across various payers and insurance types.
Innovation Solution
A system utilizing a payment pattern application engine that applies demographic and financial data to identify payer and patient payment patterns, verifies active coverage, and calculates payment estimates using machine learning classifiers to streamline claims processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual claims processing is used, then administrative control and verification are maintained, but processing efficiency is low and payment estimation accuracy is poor
Solution Approach 1:
The patent replaces manual mechanical processing with an automated machine learning system. The ML model analyzes historical claims data, patient demographics, and billing codes to automatically generate payment estimates, substituting human administrative work with computational processing that achieves both high speed and high accuracy simultaneously.
Solution Approach 2:
The patent introduces an intermediary payment estimation system that sits between claims submission and final payment processing. This intermediary ML-based estimation engine provides predicted payment amounts before actual processing, enabling better financial planning while maintaining traditional processing pipelines.
2Reliability
If multiple entities and databases are involved in claims processing, then comprehensive payment verification is achieved, but system complexity increases
Solution Approach 1:
The patent segments the complex claims processing system into distinct functional modules: data collection from multiple sources, ML model processing, payment estimation generation, and results delivery. Each module handles specific tasks independently, making the overall complex system manageable and maintainable while preserving comprehensive verification capabilities.
3Loss of time
If real-time payment estimation is implemented, then patient care and financial planning are improved, but data processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and storing historical claims data in structured formats before actual estimation is needed. The ML model is trained in advance on comprehensive datasets, so when real-time estimation is required, the system can quickly apply learned patterns without performing heavy computation at the moment of query, thus reducing real-time resource consumption.
Data Source
AI summary
Systems and methods for calculating medical claims payment estimates may receive a medical claim for a first patient including billing code(s) and demographic data, apply the demographic data to identify payer(s) for the first patient, access a data universe including patient data collection(s) of patient data records for a second group of patients, payer data collection(s) of data records for payers, and a financial history data collection including financial data record(s) for the first patient, identify, for each billing code, a payer payment pattern based on a combination of the patient data records and payer data record(s) corresponding to the payer for the first patient, identify a patient payment pattern based on the financial data record(s), and apply the payer payment pattern and the patient payment pattern to the medical claim for the first patient to calculate a payment estimation for the medical claim.