Health Sensor Pairing Automation for Patient Monitoring
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Solution Overview
Problem
Conventional multi-sensor monitoring systems require significant user setup and are inflexible, leading to labor inefficiencies and translational costs in health sensor systems, which lack automated data processing and decision-making capabilities to detect patient deteriorations and intervene accordingly.
Innovation Solution
A customizable population health system that automatically configures and pairs health sensors with a user device, processes data, and generates alerts to inform treatment or behavioral plans, integrating sensors, algorithms, and therapeutic services for personalized patient care.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional multi-sensor monitoring systems are implemented, then health monitoring capability is provided, but user setup complexity increases significantly
Solution Approach 1:
The system performs automatic device pairing and configuration without requiring manual user intervention. The central computing device automatically discovers and pairs with peripheral sensors, and the system automatically configures the sensor combinations based on the user's health needs, eliminating the need for users to manually connect devices or configure settings.
Solution Approach 2:
The system pre-configures multiple sensor combinations in advance that correspond to different health conditions or user needs. When a user selects a desired health monitoring profile, the system has already prepared the appropriate sensor configurations, eliminating the need for users to perform complex setup procedures during initial use.
2Ease of operation
If hard-coded sensor pairing is implemented, then pairing workflow is streamlined, but system flexibility decreases
Solution Approach 1:
The system transitions from static hard-coded sensor pairings to dynamic, configurable sensor combinations. The system allows sensor combinations to be changed and adapted based on different users' health needs, medical conditions, and treatment requirements, while maintaining automated pairing processes that keep the user experience simple.
Solution Approach 2:
The system creates a universal platform that can accommodate multiple types of peripheral sensors and configure them in various combinations to address different health monitoring needs. Rather than being limited to a fixed set of sensors, the system can work with diverse sensor types and adapt their configurations to serve multiple purposes and user requirements.
3Loss of information
If manual data interpretation and data injection processes are used, then health sensor data can be processed, but labor inefficiency increases
Solution Approach 1:
The system replaces manual, mechanical processes of data interpretation and data injection with automated electronic data processing and transmission. Health sensor data is automatically collected, transmitted to the central computing device, processed through algorithms, and delivered to appropriate service providers without requiring manual intervention at any stage, thereby eliminating labor inefficiencies.
Solution Approach 2:
The system establishes automated feedback loops where health sensor data continuously flows from sensors to the central computing device, which processes the data and generates alerts or notifications to service providers when specific health thresholds are met. This continuous automated feedback process eliminates the need for manual data review and enables real-time health monitoring and intervention.
4Device complexity
If health sensor systems lack automated decision-making, then system simplicity is maintained, but patient deterioration detection capability decreases
Solution Approach 1:
The system implements automated decision-making through feedback mechanisms where health sensor data is continuously monitored against predefined thresholds and clinical criteria. When data indicates patient deterioration, the system automatically generates alerts and notifications to appropriate service providers, enabling timely intervention while maintaining system simplicity through rule-based automated logic rather than complex manual processes.
Data Source
AI summary
The present disclosure provides systems, methods, and kits for collecting health measurement data and processing the health measurement data to generate alerts and modify patient treatment plans. An example system can be configured to (i) receive data defining a plurality of health sensors; and (ii) generate, based on the data, application logic configured to cause, in response to one user input, a user device to: (a) couple to the plurality of health sensors, thereby establishing communication between the user device and each of the plurality of health sensors, and (b) provide instructions to a user of the user device through an interface of the user device. The instructions can comprise instructions for taking health measurements using the plurality of health sensors.