Sensor-Based Treatment Progress Monitoring for Remote Plan Adjustment
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Solution Overview
Problem
Remote healthcare professionals face challenges in monitoring patient progress and adapting treatment plans in real-time due to geographical separation, leading to inefficiencies and potential inaccuracies in treatment delivery and condition detection.
Innovation Solution
A system and method that utilizes sensor data from treatment devices and wearable devices to generate treatment information, which is analyzed by machine learning models to provide real-time adjustments to treatment plans and detect patient conditions, enabling remote monitoring and control of treatment devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If remote monitoring is implemented to overcome geographical separation, then healthcare professionals can access patient data from any location, but the system complexity increases due to need for sensor integration and data transmission infrastructure
Solution Approach 1:
The system is divided into separate functional modules: wearable sensors for patient monitoring, communication module for data transmission, and processing device for analysis. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while enabling remote monitoring capability.
Solution Approach 2:
A communication module serves as an intermediary between the wearable sensors and the healthcare professional's processing device. This intermediary handles data transmission and protocol conversion, reducing the complexity of direct connections and enabling flexible remote access without requiring complex integration between all system components.
2Reliability
If real-time treatment adjustments are made based on sensor data, then treatment efficacy improves, but the processing time and computational resources required increase
Solution Approach 1:
Treatment parameters and adjustment protocols are pre-configured in the system before actual monitoring begins. The processing device contains pre-programmed algorithms that automatically determine appropriate adjustments based on sensor data patterns, eliminating the need for real-time computational analysis and reducing processing time while maintaining treatment efficacy.
Solution Approach 2:
The system performs self-adjustment through automated algorithms that continuously monitor sensor data and automatically modify treatment parameters without requiring manual intervention. This self-service capability reduces processing time by eliminating human reaction delays while maintaining reliable treatment adjustments based on real-time patient conditions.
3Measurement precision
If comprehensive sensor data is collected to accurately monitor patient progress, then measurement precision improves, but the quantity of data processed increases leading to higher storage and transmission requirements
Solution Approach 1:
The system extracts and processes only the most relevant sensor data parameters for monitoring patient progress, filtering out redundant or less significant information. This selective extraction maintains measurement precision by focusing on critical metrics while reducing the overall data volume that requires storage and transmission.
Solution Approach 2:
The system dynamically adjusts the granularity and frequency of data collection based on patient condition and treatment stage. During critical phases, the system collects data at higher precision intervals, while during stable phases it reduces sampling frequency, maintaining adequate measurement precision while minimizing total data quantity generated.
Data Source
AI summary
A method includes receiving treatment data pertaining to a user capable of using a treatment device to perform a treatment plan and receiving activity data pertaining to the user while the user engages in at least one activity. The method also includes generating treatment information using the treatment data and the activity data and writing to an associated memory, for access by a healthcare professional, the treatment information. The method also includes modifying at least one aspect of the treatment plan in response to receiving, from the healthcare professional, treatment plan input including at least one modification to the at least one aspect of the treatment plan.