Distributed Medical Support System with Edge Anonymization
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Solution Overview
Problem
In today's healthcare, finding the optimal treatment for patients is challenging due to rising cost pressures and increased information availability, and electronic medical support systems are limited by patient data privacy concerns, making it difficult to provide effective real-time medical diagnostics and procedures.
Innovation Solution
A medical support system utilizing a distributed computing architecture with a central computing entity and local medical entities that preprocess patient data to generate anonymous data, allowing real-time analysis and parameter determination while ensuring privacy through AI algorithms and secure data communication networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If patient data is collected and analyzed centrally to improve medical diagnostics, then diagnostic accuracy is improved, but patient data privacy is compromised
Solution Approach 1:
The system segments the centralized data processing into distributed edge computing nodes at local medical entities and a central coordinating server. Each edge node processes data locally to generate anonymized results, which are then aggregated centrally. This segmentation enables diagnostic accuracy improvement through centralized coordination while maintaining data privacy through distributed processing and anonymization at the edge.
Solution Approach 2:
The system introduces an intermediary anonymization layer between data collection and central analysis. Edge computing entities act as intermediaries that strip personally identifiable information from patient data before transmission to central servers. This intermediary mechanism enables centralized diagnostic analysis while protecting patient privacy through automated anonymization processes.
2Loss of time
If real-time medical support is provided through centralized computing, then response time is improved, but data transmission requirements and privacy risks increase
Solution Approach 1:
The system performs preliminary data processing and anonymization at edge computing nodes before central transmission. By preprocessing data locally to extract only essential diagnostic features and anonymized results, the system reduces the volume of data requiring real-time transmission while maintaining rapid response capabilities. This preliminary action at the edge enables fast central coordination without overwhelming network bandwidth.
Solution Approach 2:
The system transitions from transmitting raw patient data to transmitting processed diagnostic features and anonymized results. This dimensional transformation converts high-volume raw data into compact, essential information, reducing transmission requirements while maintaining real-time response capabilities through efficient data representation.
3Manufacturing precision
If AI algorithms are deployed centrally to enhance medical procedures, then procedure precision is improved, but system complexity and deployment difficulty increase
Solution Approach 1:
The system implements self-service AI deployment where edge computing entities autonomously download and execute AI models locally for real-time inference. Central servers automatically manage model updates and distribution without requiring complex manual configuration at each site. This self-service approach enables high procedure precision through advanced AI while reducing deployment complexity through automated model management and local execution.
Data Source
AI summary
A medical support system for patient treatment. The medical support system includes a central computing entity; and at least one local medical entity configured to obtain physiological data of a patient, preprocess the physiological data to generate anonymous preprocessed data, and send the anonymous preprocessed data to the central computing entity via at least one communications network, wherein the central computing entity is configured to analyze the anonymous preprocessed data, determine one or more parameters in relation to a medical support function based on the analysis of the anonymous preprocessed data, and send the determined one or more parameters to the at least one local medical entity via the at least one communications network, and wherein the at least one local medical entity is configured to implement the one or more parameters received from the central computing entity to provide the medical support function for the patient.