Modular Location Engine for Clinical Asset Tracking
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Solution Overview
Problem
Current Real Time Locating Systems (RTLS) in healthcare settings face challenges in accuracy and adaptability across various clinical environments, necessitating more precise and flexible tracking solutions for assets and personnel.
Innovation Solution
A modular location engine system that arranges network nodes in series or parallel to estimate tag locations, allowing for efficient identification of individuals needing assistance, with features for prioritization and storage of inputs/outputs for debugging and IT assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a traditional RTLS is deployed to track locations of assets and personnel, then location tracking capability is provided, but accuracy and adaptability across various clinical environments are insufficient
Solution Approach 1:
The location engine is divided into multiple independent network nodes that can be distributed throughout the clinical environment. Each node processes location data independently and contributes to the overall location estimation, allowing the system to be adapted to different environmental configurations while maintaining tracking accuracy
Solution Approach 2:
The system employs dynamic node configuration where nodes can be arranged in series, parallel, or combinations of both depending on the specific clinical environment requirements. This dynamic adaptability allows the same modular architecture to optimize location tracking accuracy across diverse settings without requiring complete system redesign
2Measurement precision
If multiple network nodes are used to improve location estimation accuracy, then measurement precision improves, but system complexity increases
Solution Approach 1:
By segmenting the location engine into standardized modular nodes, the system manages complexity through repetition of identical functional units rather than complex interconnections of different components. Each node performs the same location estimation function, simplifying system design and deployment
Solution Approach 2:
Each network node is designed as a universal module capable of performing the same location estimation function. This universality allows nodes to be interchangeably deployed in various configurations (series, parallel, or combinations) without requiring node-specific customization, thereby improving accuracy while controlling complexity
3Ease of repair
If individual network nodes store their inputs and outputs for debugging, then troubleshooting capability improves, but data storage requirements increase
Solution Approach 1:
Each network node stores only its own local inputs and outputs data, rather than centralizing all data storage. This local quality approach allows troubleshooting to be performed at the individual node level, reducing the storage burden on any single component while collectively providing comprehensive debugging capability across the distributed system
4Measurement precision
If the location engine is configured in series to improve processing accuracy, then measurement precision improves, but processing time increases
Solution Approach 1:
The system dynamically configures nodes in series when processing accuracy is prioritized and in parallel when processing speed is prioritized. This dynamic reconfigurability allows the same modular system to optimize for different operational requirements without hardware changes, balancing accuracy and processing time based on clinical needs
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances location tracking accuracy and adaptability in clinical environments, enabling efficient deployment of resources and troubleshooting of system malfunctions, thereby improving care delivery and system performance.
Implementation Method 1
Based on the times-of-flights (or angles-of-arrival) of the wireless signal being received by the multiple receivers, and the positions of the receivers, a tag's location can be derived within an environment
Data Source
AI summary
A location system can be used to identify locations of assets in a clinical environment. An example location system receives, from a primary receiver, timing data indicating times at which multiple receivers including the primary receiver received a wireless signal from a tag. The multiple receivers may be located in the clinical environment. The example location system further identifies that the timing data includes a flag indicating that a user of the tag has requested assistance, identifies an identifier of the tag based on the timing data; and transmits, to a reporting system, a first message indicating an identifier of the tag and that the user of the tag has requested assistance.