Home Dialysis AI Therapy Settings Using Biometric Feedback
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Solution Overview
Problem
Existing home therapy systems for dialysis lack automation and guidance for setting control parameters, leading to a steep learning curve and cognitive load for patients and caregivers, and potentially resulting in sub-optimal treatment outcomes and health risks.
Innovation Solution
A medical device ecosystem that incorporates artificial intelligence (AI) to assist in setting therapy parameters, including a dialysis machine with sensors, a user interface, a controller, and a network interface that receives a pre-trained AI model from a cloud service to recommend optimal therapy settings such as blood flow rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If home hemodialysis is implemented with manual control settings, then patient autonomy and treatment flexibility are improved, but cognitive load and learning curve for patients and caregivers increase significantly
Solution Approach 1:
The system enables self-service by allowing the AI to automatically monitor patient biometrics and adjust therapy parameters without requiring manual intervention from patients or caregivers. The AI circuit continuously analyzes sensor data and autonomously modifies dialysis settings, freeing patients from the cognitive burden of manual control while maintaining treatment adaptability.
Solution Approach 2:
The patent replaces the mechanical/cognitive system of manual parameter adjustment with an electronic AI-based system. The AI circuit electronically processes sensor data and automatically controls therapy delivery, substituting the need for human cognitive processing and manual adjustment with automated electronic control.
2Reliability
If comprehensive training is provided for home dialysis, then treatment safety and proper operation are improved, but training time and resource requirements increase
Solution Approach 1:
The AI system provides self-service monitoring and adjustment capabilities that eliminate the need for extensive training. The system automatically ensures treatment safety by continuously monitoring biometrics and adjusting parameters, replacing the need for trained personnel with autonomous electronic monitoring and control.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor patient biometrics in real-time and the AI circuit automatically adjusts therapy parameters based on this feedback. This closed-loop control ensures treatment safety without requiring trained operators, as the system self-corrects based on real-time patient status.
3Ease of operation
If AI automation is implemented for therapy parameter setting, then ease of operation and cognitive load reduction are improved, but device complexity and processing requirements increase
Solution Approach 1:
The AI circuit acts as an intermediary between simple sensor inputs and complex therapy control. It receives basic biometric data from sensors and translates this into appropriate therapy parameter adjustments, shielding users from system complexity while enabling sophisticated automated control.
Solution Approach 2:
The system performs self-service by automatically processing sensor data and adjusting therapy parameters without user intervention. This automation handles the computational complexity internally, presenting a simple interface to users while maintaining sophisticated control capabilities.
4Device complexity
If manual monitoring and adjustment of therapy parameters is performed, then system simplicity and lower processing requirements are maintained, but treatment consistency and adequacy may be compromised
Solution Approach 1:
The system implements continuous feedback monitoring where sensors track patient biometrics throughout treatment and the AI circuit automatically adjusts parameters to maintain optimal therapy delivery. This ensures treatment consistency by responding to real-time patient status changes, preventing the variability associated with manual monitoring.
Solution Approach 2:
The AI system performs self-service monitoring and adjustment, automatically ensuring treatment adequacy without requiring complex manual intervention protocols. The system autonomously maintains treatment consistency through continuous automated monitoring and parameter optimization.
Data Source
AI summary
A medical device is provided, including: a therapeutic subsystem to deliver a medical therapy, including a sensor to monitor a biometric health factor; a user interface; a controller, including a processor and a memory; therapy software including instructions encoded within the memory to instruct the processor to receive a prescribed therapy, to receive a therapeutic setting recommendation, and to display the therapeutic setting recommendation to an operator via the user interface; a network interface, and instructions to receive, via the network interface, a prepared artificial intelligence (AI) model from a cloud service; and an AI circuit having execution hardware including at least one logic gate, and further including instructions to instruct the execution hardware to execute the prepared AI model to provide a recommended therapy setting for the therapeutic subsystem, wherein the circuit is further to incorporate into the prepared AI model data from the sensor.