Healthcare Integration Platform Using Graph Analytics for Dynamic Interface Configuration
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Solution Overview
Problem
Current healthcare systems face challenges in efficiently integrating disparate information systems due to variability in standards like HL7, leading to redundancy, inefficiency, and a lack of interoperability, which hinders effective data exchange and patient care.
Innovation Solution
A cloud-based integration platform with an intelligent mediation engine and graph analytics that facilitates dynamic interface definition and configuration, enabling seamless data exchange between healthcare systems using reusable interface and route definitions, and predicting traffic patterns to optimize connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional healthcare systems integrate disparate information systems manually, then connections between systems can be established, but the process becomes complex, time-consuming, and redundant
Solution Approach 1:
The patent introduces an integration platform as an intermediary system that mediates between disparate healthcare information systems. This platform provides standardized interfaces, message routing, and data translation capabilities, eliminating the need for direct point-to-point integrations between each system pair. The intermediary handles the complexity of integration internally while presenting simplified connections to users.
Solution Approach 2:
The integration platform implements universal interface definitions and route templates that can be reused across multiple system integrations. Instead of creating custom integration logic for each system pair, the platform provides multi-functional templates that handle common healthcare data exchange scenarios (ADT, ORM, ORU, etc.), reducing redundancy and improving integration efficiency.
2Reliability
If standardized interfaces are implemented across all healthcare systems, then interoperability improves, but adaptability to varying system requirements decreases
Solution Approach 1:
The integration platform implements dynamic interface definitions that can be configured and adapted based on specific system requirements. The system allows for flexible parameter configuration, conditional routing logic, and adaptive message transformation that maintains standardized interfaces while accommodating varying operational needs of different healthcare systems.
Solution Approach 2:
The platform applies local quality by allowing specific customization at the interface level while maintaining global standardization. Each integration point can have tailored configurations, mappings, and transformations suited to local system requirements, while still adhering to overall standardized protocols and data formats for interoperability.
3Ease of operation
If manual interface configuration is used for each system connection, then connections can be established, but time consumption and manual effort increase significantly
Solution Approach 1:
The system implements preliminary action by pre-configuring interface definitions, route templates, and message mappings during system deployment or through automated discovery processes. Common integration scenarios are prepared in advance with standardized templates, eliminating the need for manual configuration during operational setup and significantly reducing integration time.
Solution Approach 2:
The integration platform implements self-service capabilities through automated interface discovery, configuration generation, and validation. The system can automatically detect available systems, generate appropriate interface definitions, and configure routing logic without requiring manual intervention, thereby reducing both time consumption and manual effort.
Data Source
AI summary
A systems, method, and apparatus to improve connections within a healthcare ecosystem are provided. Example systems, methods, and apparatus can facilitate dynamic interface definition and configuration. An example method includes storing a plurality of reusable interface and route definitions to translate and exchange data messages between source and target systems in a healthcare ecosystem; monitoring message exchanges and message patterns in the healthcare environment via a machine learning system to predict traffic and utilization patterns in the healthcare ecosystem; tracking metadata regarding connections involving the source and target systems and storing the metadata in a graph database; suggesting connections between the source and target systems based on the monitored message exchanges and message patterns and metadata from the graph database using graph analytics; provisioning an interface between the source and target systems based on a suggested connection, the interface provisioned from the reusable interface and route definitions based on the suggested connection.