Distributed Dialysis Data Sharing Without a Central Server
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Solution Overview
Problem
Existing dialysis machines require centralized data collection systems that incur significant hardware and maintenance costs, necessitating IT expertise, leading to manual and error-prone data collection in clinics lacking adequate IT support.
Innovation Solution
A distributed database system for medical devices, such as renal failure therapy systems, that operates within a local area network without a centralized server, allowing machines to share and synchronize medically related data autonomously, including prescription inputs and treatment outputs, while supporting different manufacturers and types of devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a centralized server system is used to collect treatment data from multiple dialysis machines, then data collection is automated and efficient, but hardware and installation costs increase significantly and IT expertise is required for maintenance
Solution Approach 1:
The patent divides the centralized data collection system into multiple independent peer nodes (dialysis machines), where each machine runs its own database server software. This segmentation eliminates the need for a single centralized server, reducing hardware costs and system complexity while maintaining automated data collection capabilities across the network.
Solution Approach 2:
Each dialysis machine in the network autonomously performs data collection, storage, and sharing functions without requiring external centralized server management. The peer-to-peer architecture enables machines to self-organize and self-maintain the data network, eliminating the need for specialized IT expertise for system maintenance.
2Extent of automation
If a centralized server is deployed for data collection, then automated data collection is achieved, but hardware costs and installation requirements increase
Solution Approach 1:
The patent makes each dialysis machine multi-functional by enabling it to serve both as a treatment device and as a database server. This universality allows the system to achieve automated data collection without adding separate centralized server hardware, thereby reducing overall hardware resource requirements while maintaining full automation.
Solution Approach 2:
The patent combines the data collection and storage functions with the existing dialysis machine infrastructure, merging what would traditionally be separate centralized server resources into the distributed machine network. This consolidation eliminates the need for additional dedicated hardware while achieving automated data collection.
3Device complexity
If manual data collection is used to avoid centralized servers, then hardware costs are reduced, but data collection becomes time-consuming and error-prone
Solution Approach 1:
The patent implements automated feedback mechanisms where each peer node continuously collects treatment data, validates it against predefined criteria, and automatically shares it with other nodes in the network. This automated feedback loop ensures data accuracy and consistency without manual intervention, maintaining high reliability while keeping the system architecture simple.
Solution Approach 2:
The patent replaces the mechanical manual data collection process with an automated electronic data exchange system. Treatment data is automatically captured by sensors and processors within the dialysis machines, then electronically transmitted and stored across the peer network, eliminating manual data entry errors while maintaining system simplicity through standardized communication protocols.
Data Source
AI summary
A medical device system includes renal failure therapy machines each including a memory. The renal failure therapy machines are communicatively coupled such that the memories collectively form a distributed database. The system also includes a logic implementer associated with each renal failure therapy machine. Each logic implementer is programmed to automatically access the distributed database, so that each renal failure therapy machine periodically delivers prescription input parameters and/or treatment output data to at least one of the other renal failure therapy machines, and retrieves prescription input parameters and/or treatment output data from at least one of the other renal failure therapy machines. One of the renal failure therapy machines is configured to create at least one treatment record trend from the treatment output data and to share the at least one treatment record trend with other renal failure therapy machines through the distributed database.