Machine Learning Model for Predicting Health Plans from Missing Input Data
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Solution Overview
Problem
Healthcare providers face challenges in accurately generating and processing medical claims due to incorrect or missing insurance information, leading to errors and denied claims, especially when health plan data on insurance cards cannot be matched to electronic health record systems or when patients are from different states or localities.
Innovation Solution
A machine learning model is trained using HIPAA transactions to intelligently link payers and health plans to specific employers, predicting valid payer-health plan combinations even when initial input data is missing, by building a mapping of employers, payers, and health plans from EDI insurance transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual matching of health plan data is used, then flexibility in handling various insurance cards is maintained, but accuracy decreases leading to errors and denied claims
Solution Approach 1:
The patent replaces manual mechanical matching processes with an automated machine learning model that analyzes insurance card data and predicts appropriate health plan assignments. This substitution increases accuracy by eliminating human error while maintaining system manageability through algorithmic decision-making.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between insurance card data and health plan assignment decisions. This intermediary processes and interprets the data, bridging the gap between raw input and accurate matching outcomes, thereby improving precision without requiring direct manual intervention.
2Measurement precision
If comprehensive health plan data is built in EHR for all employers, then matching accuracy improves, but data maintenance complexity and resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-training the machine learning model with extensive employer-payer-health plan relationship data before deployment. This preliminary training enables the model to accurately predict health plan assignments without requiring the EHR to maintain comprehensive manual data sets for all possible employers, reducing ongoing data management complexity.
Solution Approach 2:
The patent enables the system to serve itself by allowing the machine learning model to automatically learn and update health plan matching patterns from transaction data. This self-learning capability reduces the need for manual data entry and maintenance, as the system continuously improves its matching accuracy using incoming data without additional human intervention.
3Productivity
If insurance card scanning is implemented, then data collection speed increases, but errors occur when cards are missing or contain incorrect information
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning model continuously learns from transaction outcomes and claim adjudication results. When scanning errors or missing data occur, the system receives feedback from subsequent processing steps and uses this information to improve future predictions, thereby maintaining reliability despite occasional data collection failures.
Solution Approach 2:
The patent prepares contingency measures in advance by training the machine learning model to handle various data quality scenarios including missing cards, incorrect information, and unusual formats. This prior cushioning ensures that the system can maintain productivity and reliability even when scanning encounters problems, as the model is already equipped to handle these edge cases.
4Measurement precision
If corrective actions are taken for incorrect claims, then claim accuracy improves, but processing time and computing resources increase
Solution Approach 1:
The patent performs preliminary validation and prediction of health plan assignments before claims are submitted. By using the machine learning model to pre-determine the correct health plan matching, the system prevents errors before they occur, eliminating the need for subsequent corrective actions and reducing overall processing time while maintaining high accuracy.
Data Source
AI summary
Methods, computer systems, and computer storage media are provided for utilizing machine learning to predict health plans. A machine learning model is trained to predict valid combinations of employer-payer-health plan in response to one or more missing identifiers based on transaction data from electronic data interchange (EDI) insurance transactions that include valid combinations of employer identifier, payer identifier, and health plan identifier. In response to a request to identify a valid combination based on at least one missing identifier, at least one known identifier corresponding to an employer name, a payer name, or a health plan name is inputted and work location data associated with a patient. The machine learning model generates and displays on a user interface, a predicted set of one or more valid combinations of employer-payer-health plans that correspond to the one known identifier and the work location information that is inputted.