Diagnostic Instrument Bay Control for Uneven Wear Management
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Solution Overview
Problem
Diagnostic instruments experience uneven wear and performance due to inconsistent usage patterns, leading to preventable medical errors and inefficiencies in sample processing, with existing systems lacking mechanisms to manage individual processing compartments effectively.
Innovation Solution
Implementing a control unit with disable, standby, and suggest rule engines to monitor and manage diagnostic instrument bays based on historical data, disabling underperforming bays, putting them on standby, or suggesting high-performing ones to optimize usage and extend instrument lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If diagnostic instruments are used continuously without monitoring, then productivity is maintained, but reliability deteriorates due to uneven wear and performance degradation
Solution Approach 1:
The system performs preliminary monitoring of bay performance metrics and takes preventive action by disabling underperforming bays before they cause medical errors. The rule engines continuously evaluate bay data and proactively disable bays that fall below performance thresholds, preventing reliability issues before they affect sample processing capability.
Solution Approach 2:
The system implements continuous feedback loops where bay performance data is collected, analyzed by rule engines, and used to dynamically adjust bay status. The processor receives bay data, compares it against predefined baselines, and provides feedback by disabling or restoring bay access based on performance trends, creating a closed-loop reliability management system.
2Productivity
If all bays are kept active, then productivity is maximized, but device complexity increases due to need for monitoring and management
Solution Approach 1:
The system enables self-service bay management where the instrument automatically monitors its own bay performance and takes corrective action. The rule engines and processor work autonomously to evaluate bay data, disable underperforming bays, and restore them when conditions improve, eliminating the need for external manual intervention and reducing operational complexity.
Solution Approach 2:
The system segments the diagnostic instrument into independently manageable bays, each with its own performance monitoring. By treating bays as separate manageable units rather than a monolithic system, the complexity is distributed across individual bay management rather than requiring system-wide complex controls.
3Reliability
If bays are disabled based on performance data, then reliability improves, but ease of operation deteriorates due to automated restrictions
Solution Approach 1:
The system provides transparent feedback to users about why bays are disabled through the user interface, displaying performance data and reasons for bay restrictions. This feedback mechanism maintains ease of operation by explaining automated decisions, allowing users to understand and work around restrictions without confusion.
Solution Approach 2:
The rule engines act as intermediaries between raw bay data and user operations. Rather than directly restricting users, the rule engines process data, apply business logic, and translate complex performance monitoring into simple bay disable/enable decisions, mediating between reliability requirements and operational flexibility.
Data Source
AI summary
Disclosed are methods, systems and apparatuses in which a diagnostic instrument analyzes data collected over time, and based on that information, disables a bay, suggests a bay, puts a bay on standby or combinations thereof. In some embodiments, the method, system and apparatus allows the diagnostic instrument to have higher validity rates in the field and allows the provider to set up service calls, schedule maintenance and perform remote maintenance, as well as other functions based on the data collected over time.