Healthcare Claims Query Service with Standardized Data Structures
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Solution Overview
Problem
The challenge lies in efficiently querying and accessing claims data across disparate databases maintained by different healthcare entities, which are often fragmented and lack integration, leading to inefficiencies, irrelevant data selection, and resource wastage due to redundant searches.
Innovation Solution
A centralized database query service aggregates claims data using a standard template, applies heuristics and data mining to identify alternative claims data, and facilitates communication between entities to streamline data access and switching processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If claims data are stored across multiple separate databases by different entities, then data can be maintained by individual organizations, but data integration and accessibility deteriorate
Solution Approach 1:
The patent introduces a centralized query service as an intermediary layer between multiple entity databases and users. This query service aggregates claims data from various sources using standardized templates and provides unified access without requiring direct integration between individual entity databases, thus improving accessibility while maintaining organizational autonomy
Solution Approach 2:
The query service implements universal data access by creating standardized data structures that can represent claims data from multiple different entities and sources. This allows a single query interface to access diverse data sources through common protocols and formats
2Productivity
If collaborative filtering is used to query claims data, then some data selection capability is provided, but data sparsity and lack of heuristics worsen selection quality
Solution Approach 1:
The system incorporates feedback mechanisms where the query service learns from query patterns, selection outcomes, and data relationships to continuously improve its heuristic rules. This feedback loop enables the system to refine its understanding of data correlations and improve selection accuracy over time
Solution Approach 2:
The patent transforms the data selection process by changing from simple collaborative filtering parameters to complex heuristic rules that consider multiple factors including data relationships, entity-specific criteria, and contextual information. This parameter transformation enables more precise and accurate data selection
3Ease of operation
If entities maintain separate databases with proprietary formats, then organizational autonomy is preserved, but data standardization and query efficiency deteriorate
Solution Approach 1:
The standardized data structures serve as an intermediary representation layer that translates proprietary entity formats into a common queryable format. This allows efficient standardized querying without requiring entities to abandon their internal data formats or invest in complex direct integration infrastructure
Data Source
AI summary
Presented herein are systems and methods for aggregating claims data. A database query service may aggregate claims data from patients, care providers, pharmacy services, and a multitude of other entities to store and maintain on a centralized database. The claims data may be stored and maintained as one or more data structures in accordance with a standard template across the database to facilitate access by the entities using the database. The claims data may also identify information for entities available for provision to address health conditions of patients. The service may also establish a communication session to facilitate exchange of messages through an interface between a patient and the entities. The service may monitor for usage of an electronic card at the pharmacy service.