Dynamic Location-Aware Patient Care Team Control
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Solution Overview
Problem
Current methods for controlling patient care teams and tracking patient care assets are manual and inefficient, lacking dynamic location-aware enhancements.
Innovation Solution
A system utilizing low energy beacons to track mobile devices in real-time, correlating their location with clinical tasks, and dynamically managing patient care teams and assets through a backend server system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to control patient care teams and track assets, then device complexity is reduced, but productivity and responsiveness deteriorate
Solution Approach 1:
The system employs mobile devices that serve multiple functions: they act as location trackers via beacon detection, communication tools for care team coordination, and asset tracking devices. This multi-functionality improves productivity without proportionally increasing device complexity, as existing mobile devices are leveraged rather than introducing entirely new specialized equipment.
Solution Approach 2:
The system automatically detects patient care team member locations through beacon signals and dynamically assigns tasks based on proximity and availability, eliminating the need for manual team coordination. The automated nature of location-based task assignment significantly improves productivity while the complexity is managed through algorithmic rather than structural complexity.
2Loss of time
If manual tracking of patient care assets is used, then device complexity is minimized, but loss of time and productivity increase
Solution Approach 1:
The system replaces manual mechanical tracking methods with automated electronic beacon-based location detection. Mobile devices and assets equipped with sensors automatically report locations to the backend server, eliminating time-consuming manual tracking while the complexity is software-based rather than hardware-based, allowing for scalable implementation.
3Adaptability or versatility
If dynamic location-aware tracking is implemented, then responsiveness and adaptability improve, but device complexity and measurement difficulty increase
Solution Approach 1:
The system dynamically adjusts patient care team compositions and asset assignments based on real-time location data from beacon signals. Team members are automatically added or removed from active tasks based on their proximity to patients and current availability status, providing high adaptability. The complexity is managed through dynamic algorithms that process beacon data rather than complex hardware architectures.
Solution Approach 2:
The system continuously monitors beacon signals from mobile devices and assets, providing real-time feedback on location and availability. This feedback loop enables the backend server to dynamically reassign tasks and adjust team compositions, improving adaptability. The feedback mechanism relies on simple beacon signal detection rather than complex measurement systems, keeping device complexity manageable.
4Productivity
If real-time location tracking of mobile devices is implemented, then productivity and responsiveness improve, but use of energy and device complexity increase
Solution Approach 1:
Instead of continuous location tracking that would consume excessive energy, the system uses periodic beacon signal detection. Mobile devices detect low-energy beacons transmitted by assets and infrastructure at predetermined intervals, providing sufficient real-time tracking capability while significantly reducing energy consumption compared to continuous GPS or other active tracking methods.
Data Source
AI summary
A system having a system backend having a backend processor configured to define a patient clinical team associated with a patient clinical process comprising of a series of patient clinical tasks effected within the healthcare facility, each patient clinical task with an associated patient clinical task location within the healthcare facility, a group of mobile devices communicably connected via a network to the system backend, each mobile device being associated with and configured for use by at least one care member of the patient clinical team, a low energy (LE) beacon array arranged in a predetermined relationship with and differentiating between different patient clinical task locations, and communicably connected to each of the mobile devices that is configured to resolve its proximity, from the beacon array, relative to the at least one patient clinical task location. The backend processor is configured to dynamically enhance the patient clinical team by dynamically changing in real-time a predetermined characteristic of the patient clinical team based on the resolved proximity of each mobile device associated with each care member of the patient clinical team.