Real-Time Diagnostic Data Display via Batch and Snapshot Tables
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Solution Overview
Problem
Traditional medical diagnostic systems face challenges in displaying real-time updates and filtering large datasets, leading to inaccurate and non-real-time representations of relevant medical diagnostics data, especially in very large databases, which results in excessive memory consumption and user unawareness of ongoing changes.
Innovation Solution
A system that automatically updates client views in real-time by using data selection filters to arrange and sort medical diagnostics data, allowing clients to display relevant information according to their needs, with mechanisms for generating data records, notifications, and database queries to ensure timely updates and efficient data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional paging mechanisms are used to display medical diagnostics data, then the system can handle large databases, but the display cannot be updated in real-time without user intervention
Solution Approach 1:
The system implements a notification mechanism that automatically detects when new data records are added to the database and pushes updates to client views. This feedback loop eliminates the need for manual page refreshes while ensuring data accuracy is maintained through controlled update triggers.
Solution Approach 2:
The paging mechanism is enhanced with automatic self-updating capabilities. The system monitors database changes and automatically refreshes client views when new relevant data arrives, allowing the display to serve itself without user intervention while maintaining data reliability.
2Adaptability or versatility
If client-side filtering is applied to large databases, then each client can customize their view, but memory consumption increases excessively
Solution Approach 1:
The system extracts only the essential filtering and sorting logic to the server side, while clients receive pre-filtered data sets. This removes the burden of processing entire large databases on client machines, significantly reducing memory consumption while preserving client customization capabilities through server-side filtering parameters.
Solution Approach 2:
The server acts as an intermediary that receives client filtering requirements, processes them against the large database, and returns only the relevant filtered results. This mediator approach allows clients to customize their views without directly handling the full data set, thereby reducing memory consumption on client devices.
3Adaptability or versatility
If multiple paging components each utilize their own data and logic, then each component can be independently configured, but the system complexity increases
Solution Approach 1:
The system implements a universal paging component that can serve multiple client views with different filtering and sorting requirements. This single multi-functional component replaces multiple independent paging components, reducing system complexity while maintaining the ability to independently configure different client views through parameter settings.
4Reliability
If frequent database updates occur, then the data remains current, but excessive memory consumption results
Solution Approach 1:
Instead of loading and processing entire data sets on clients, the system implements partial action by sending only the specific new records or changed portions to clients when database updates occur. This selective data transmission maintains data currency while minimizing memory consumption by avoiding unnecessary data transfers.
Data Source
AI summary
A system and method for real time updating and displaying medical diagnostics data, wherein the information stored in databases comprising large amounts of sensitive data that is being updated frequently are provided herein. By using a batch table for data filtering and a snapshot table for data retrieval, the system's performance remains substantially predictable and stable regardless of the base tables' size.