Healthcare Data Integration via Source Priority and Segmentation
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Solution Overview
Problem
Current data management systems face challenges in integrating and processing vast amounts of transaction and interaction data, particularly in healthcare, where data from various sources is often overwhelming, erroneous, and disparate, making it difficult to derive meaningful insights from prescription drug claims and medical claims.
Innovation Solution
The system integrates data through a network of devices including a data query device, data manager device, data analysis device, benefit manager device, and data supplier device, which cleans, verifies, and standardizes data to create accurate master records, enabling the identification of links and relationships between healthcare participants, and facilitating fraud detection and optimization of provider networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from multiple sources is integrated and processed, then the quantity and variety of available information increases, but the complexity of data management and processing increases
Solution Approach 1:
The system segments data processing into distinct functional modules: data reception modules for different sources (pharmacy claims, medical claims, enrollment data), data cleaning modules, verification modules, and integration modules. Each module handles specific data types or processing tasks independently, reducing overall system complexity while managing large quantities of diverse data.
Solution Approach 2:
The system introduces intermediary processing layers including data standardization modules that convert various data formats into a common structure, and verification modules that act as intermediaries between raw data and final analysis. These intermediaries simplify the integration process by handling format conversion and quality control centrally.
2Reliability
If data cleaning and verification processes are applied, then the accuracy and reliability of master records improve, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data cleaning and standardization immediately upon data reception, before integration. Data is pre-processed to remove obvious errors, standardize formats, and validate required fields early in the workflow, reducing the computational burden during later integration and analysis phases.
Solution Approach 2:
The system applies different levels of verification and cleaning intensity to different data sources and data types based on their specific characteristics and reliability requirements. High-criticality data receives more rigorous verification, while routine data undergoes streamlined processing, optimizing the balance between accuracy and processing time.
3Reliability
If comprehensive data integration is performed across multiple healthcare data sources, then the ability to detect fraud and optimize provider networks improves, but the difficulty of managing disparate data formats and structures increases
Solution Approach 1:
The system implements universal data structures and standardized schemas that can accommodate multiple data sources and formats. The master record structure is designed to be multi-functional, capable of storing and relating information from pharmacy claims, medical claims, enrollment data, and provider information in a unified framework, simplifying integration while enabling comprehensive fraud detection.
Data Source
AI summary
Systems and methods for integrating data are described. In an example embodiment, a plurality of data attributes of comparison data and the plurality of data attributes of a master record are respectively compared to determine that there is a difference, the comparison data originating from a data source. A relative level of source priority of the data source of the comparison data is determined relative to the data source of a current state version of the master record in accordance with source evaluation criteria. The current state version of the master record is stored in reference data based on a determination that there is a difference and that the source priority of the data source of the comparison data is equal to or greater than the data source of the current state version of the master record. Mapped comparison data is used to update the current state version of the master record based on the determination that there is a difference and that the source significance of the data source of the comparison data is equal to or greater than the data source of the current state version of the master record to create an updated state version of the master record, the mapped comparison data being based on the comparison data. Additional methods and systems are disclosed.