Health Messaging System Batching Service Latency Reduction
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Solution Overview
Problem
Current health messaging systems lack modular subsystem isolation, leading to latency issues and poor user experiences, especially in secure health messaging applications.
Innovation Solution
An intelligent secure networked health messaging system is developed, featuring a data retention system, health analytics system with a deep neural network, web services layer, and application server layer. This system includes asynchronous processing, secure token usage, and a batching service to manage requests transparently, ensuring continuous user interaction without disruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If asynchronous processing with batching service is implemented, then latency is reduced and user experience is enhanced, but device complexity increases due to multiple layers (data retention system, health analytics system, web services layer, application server layer)
Solution Approach 1:
The system is divided into distinct modular subsystems including data retention system, health analytics system, web services layer, and application server layer. Each layer handles specific functions independently, allowing asynchronous processing and batching operations to reduce latency without creating bottlenecks. The segmentation enables parallel processing of health messaging operations.
Solution Approach 2:
A batching service acts as an intermediary layer between the application server and data retention systems. This mediator consolidates multiple requests into batches, processes them asynchronously, and returns results, thereby reducing the number of individual transactions and overall latency while maintaining system modularity.
2Measurement precision
If deep neural network processing is used for health analytics, then processing capability and accuracy are improved, but computational power and energy consumption increase
Solution Approach 1:
Data is pre-processed and organized in the data retention system before being passed to the deep neural network for analytics. This preliminary action includes data cleaning, normalization, and structuring, which reduces the computational burden on the neural network and lowers energy consumption while maintaining high processing accuracy.
3Reliability
If secure token usage is implemented across all layers, then data security is enhanced, but processing speed decreases due to authentication overhead
Solution Approach 1:
Security tokens are generated and validated in advance at the application server layer before data is passed to lower layers. This preliminary authentication reduces the need for repeated security checks during data processing, maintaining high security while minimizing speed degradation from authentication overhead.
Data Source
AI summary
Exemplary embodiments include an intelligent secure networked health messaging system configured by at least one processor to execute instructions stored in memory, the system including a data retention system and a health analytics system, the health analytics system performing asynchronous processing with a patient's computing device and the health analytics system communicatively coupled to a deep neural network, a web services layer providing access to the data retention and the health analytics system, a batching service, wherein an application server layer transmits a request to the web services layer for data, the request processed by the batching service transparently to the patient, the request processed by the batching service transparently to the patient such that the patient can continue to use a patient facing application without disruption, and the patient-facing application having an audio sensor and a computer video sensor.