Multi-tier Publish Subscribe Framework for Remote Monitoring
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Solution Overview
Problem
Current remote patient monitoring systems are limited by static, hard-coded data models that require universal updates and lack flexibility, hindering customization and personalization of care, which restricts the ability to differentiate services and burdens clinical teams with manual customization applications.
Innovation Solution
A multi-tier publish/subscribe framework with version control and population change management, allowing for the creation, editing, and customization of policy templates that can be seamlessly updated across subscribed entities without overwriting customizations, enabling scalable and personalized remote therapeutic monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If static, hard-coded data models are used for remote patient monitoring, then system stability and ease of deployment are improved, but flexibility and adaptability for customization are worsened
Solution Approach 1:
The patent implements a dynamic data model architecture where monitoring parameters, thresholds, and care plans can be modified in real-time without system reconfiguration. The system transitions from static hard-coded models to dynamic configurable models that adapt to different patient populations and clinical scenarios, resolving the contradiction between stability and flexibility.
Solution Approach 2:
The system enables parameter changes by allowing clinical teams to modify monitoring thresholds, alert conditions, and care plan parameters through a configuration interface. These parameter changes are propagated across the distributed system without requiring code changes or universal updates, maintaining system stability while enabling customization.
2Stability of the object's composition
If universal updates are required across all parties in the system, then data consistency is improved, but system complexity and deployment burden are worsened
Solution Approach 1:
The patent segments the data model into independent, modular components that can be updated and configured separately. Each monitoring parameter, alert rule, and care plan element is a discrete unit that can be modified independently, eliminating the need for universal system-wide updates while maintaining data consistency through standardized interfaces.
Solution Approach 2:
The system introduces a configuration management intermediary layer that handles data model definitions and updates. This intermediary sits between the core system and individual participants, allowing localized configuration changes without requiring coordination across all parties, thus reducing system complexity while maintaining consistency.
3Adaptability or versatility
If manual application of customizations is required for each clinical team, then customization capability is improved, but time consumption and operational burden are worsened
Solution Approach 1:
The patent implements self-service capabilities where clinical teams can independently configure and customize their own monitoring parameters and care plans through an intuitive interface. The system automatically applies these customizations without requiring manual intervention from implementers, eliminating time-consuming manual configuration while maintaining full customization capability.
Solution Approach 2:
The system provides pre-configured templates and default settings that clinical teams can adopt immediately. These preliminary configurations cover common monitoring scenarios, allowing teams to start with ready-made solutions and only customize what is necessary, significantly reducing the time and effort required for setup while maintaining adaptability.
4Device complexity
If a monolithic data model is used universally, then system simplicity is improved, but ability to differentiate services and provide personalized care is worsened
Solution Approach 1:
The patent implements local quality by allowing each clinical team or organization to have customized data model configurations tailored to their specific needs and patient populations. While the core system remains simple and standardized, local configurations enable service differentiation and personalized care without complicating the overall system architecture.
Data Source
AI summary
Therapeutic monitoring systems and their use by participants are disclosed herein. The participants include without limitation patients, patient representatives, health care providers, health care payers, research institutions and other types of stake holders in the healthcare world. More particularly, the disclosed systems and methods relate to the creation, use and amendment of policy templates containing regimens, where a system of creation, use and amendment of the templates allows for practically unlimited tiers of participants and the non-conflicting movement of both templates and regimen customization through the disclosed system.