Data Reconciliation for 340B Duplicate Discount Detection
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Solution Overview
Problem
Pharmaceutical manufacturers face challenges in identifying and preventing 'duplicate discounts' for 340B-acquired drugs due to the complexity of disparate data sources across various healthcare datasets, making it difficult to differentiate between 340B and normal commercial sales.
Innovation Solution
A method involving the reconciliation and linking of multiple electronic data sets, including prescription fulfillment, third-party prescription information, discount eligibility, and merchant relationships, to automatically identify eligible discounts and generate notifications, using a uniquely linked database system that integrates data from various sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manufacturers use traditional methods to identify duplicate discounts based only on dispensing entity certification status, then the identification process is simple, but the accuracy is insufficient to differentiate between 340B and commercial sales at retail pharmacies
Solution Approach 1:
The patent segments the identification process into multiple independent data sets (prescription fulfillment data, third-party prescription information, discount eligibility information, merchant relationship information, and sale information) that are reconciled separately and then integrated. This allows complex data analysis to be broken down into manageable components while maintaining high accuracy in duplicate discount identification.
Solution Approach 2:
The patent introduces a data reconciliation system as an intermediary that connects and integrates multiple disparate data sources. This intermediary system reconciles data across different feature sets and identifies relationships between prescribing physicians, merchants, and approved entities, enabling accurate duplicate discount detection without requiring direct integration of all source systems.
2Reliability
If manufacturers implement comprehensive data reconciliation across multiple electronic data sets, then the accuracy of discount eligibility determination is improved, but the system complexity and implementation difficulty increase
Solution Approach 1:
The data reconciliation system performs multiple functions simultaneously: it identifies prescribing physicians, determines merchant relationships with approved entities, verifies discount eligibility, and detects duplicate discounts. This multi-functional approach consolidates what would otherwise require separate systems into a single unified platform, improving reliability while managing complexity.
Solution Approach 2:
The system performs preliminary data reconciliation and relationship identification before final duplicate discount determination. By pre-processing and pre-identifying relationships between entities in the multiple data sets, the system establishes a reliable foundation for accurate discount eligibility determination, reducing the complexity of the final analysis.
3Measurement precision
If manufacturers manually track and analyze disparate health care data to prevent duplicate discounts, then data accuracy can be maintained, but the time and resources required increase significantly
Solution Approach 1:
The data reconciliation system automatically performs data matching, relationship identification, and duplicate discount detection without requiring manual intervention. The system self-services by autonomously reconciling multiple data sets, identifying prescribing physicians, determining merchant relationships, and generating duplicate discount identifications, thereby maintaining high accuracy while eliminating time-consuming manual analysis.
Solution Approach 2:
The system implements automated feedback loops where reconciliation results from one data set inform the analysis of subsequent data sets. This iterative feedback process continuously refines the identification of duplicate discounts by cross-validating information across all data sources, maintaining high accuracy while reducing the time required compared to sequential manual review.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for linking a first electronic data set to a second set of data fields in a second electronic data set. Automatically identifying a prescribing physician identifier based on the linked first and second electronic data sets. Determining a relationship between a physician associated with the prescribing physician identifier and at least one of the approved entities based on comparing the prescribing physician identifier and identifiers of the one or more approved entities to a fourth set of data fields from a fourth electronic data set. Automatically generating an electronic notification indicating that a product sold by the merchant is eligible for the discount in response to determining a relationship between a physician and the at least one of the approved entities.