Rendezvous Server for Secure Healthcare Data Synchronization
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Solution Overview
Problem
Current methods for securely exchanging electronic medical records between remote healthcare systems are inefficient, relying on manual processes, expensive private networks, or personnel intervention, and lack seamless interoperability due to variations in data standards like HL7.
Innovation Solution
A distributed computing system that uses rendezvous servers and intelligent agents to generate and process payloads, determining destination nodes and synchronizing data securely without manual intervention, enabling seamless data exchange and reconciliation between remote EMR systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If private networks are established between hospital and EMR location, then secure data transmission is achieved, but cost and network management complexity increase
Solution Approach 1:
The patent introduces a rendezvous server as an intermediary component that mediates between the hospital system and remote EMR systems. This server handles secure data transmission, authentication, and protocol translation without requiring direct private network connections between all systems, thereby reducing network management complexity while maintaining security.
Solution Approach 2:
The patent replaces complex physical private network infrastructure with a software-based solution using standardized communication protocols (HL7, XML, JSON) over existing networks. This substitution eliminates the need for dedicated private network cables and hardware while maintaining secure data exchange capabilities.
2Ease of operation
If manual processes are used for data collection and entry, then personnel can process data, but time consumption and operational errors increase
Solution Approach 1:
The patent implements automated self-service data exchange where the rendezvous server automatically receives, validates, and transmits healthcare data between systems without requiring manual intervention. The system autonomously handles authentication, data formatting, and transmission, eliminating time-consuming manual processes while reducing errors.
Solution Approach 2:
The patent performs preliminary actions by pre-configuring data formats, authentication credentials, and communication protocols before data exchange occurs. This preparation enables automated data processing without manual intervention during actual data transmission, significantly reducing time consumption.
3Adaptability or versatility
If HL7 interface engines are used for data translation, then interoperability is improved, but system complexity and cost increase
Solution Approach 1:
The patent employs a universal Rendezvous server that handles multiple data exchange functions including authentication, data translation, protocol conversion, and transmission. This single multi-functional system replaces the need for separate HL7 interface engines at each endpoint, reducing overall system complexity while maintaining broad interoperability.
Solution Approach 2:
The Rendezvous server acts as a centralized intermediary that performs all data translation and protocol conversion tasks. Instead of each system maintaining its own complex interface engine, the mediator handles format translation between different systems, simplifying individual system architectures while preserving interoperability.
4Ease of manufacture
If secure email or web methods are used for data delivery, then existing technology is leveraged, but personnel intervention is still required
Solution Approach 1:
The patent extends automated self-service by implementing background processes that continuously monitor for incoming data, automatically authenticate sources, translate formats, and transmit data without requiring personnel to check email or access websites. The system autonomously manages the entire data exchange lifecycle.
Solution Approach 2:
The Rendezvous server operates continuously, maintaining persistent connections and automatically processing data as it arrives. This continuous operation eliminates the intermittent nature of manual checking and ensures uninterrupted data exchange, enhancing automation while leveraging existing communication infrastructure.
Data Source
AI summary
Systems and methods for reconciling healthcare data between multiple distributed computing nodes that enable an individual node, a topic object, or an intelligent agent to determine synchronization with other nodes, comprising sending source node data to a payload generator, the source node data including difference data, an encapsulated topic object, or intelligent agent communications, generating a payload including the source node data and destination attributes, and sending the payload to a destination node, topic object, or destination intelligent agent, and using the source node data to update destination node data according to destination node, topic object, or destination intelligent agent requirements.