Home Dialysis Device Maintenance via Predictive Monitoring
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Solution Overview
Problem
Patients with chronic kidney disease or end-stage renal disease face limitations in in-center dialysis, including restricted scheduling, transportation needs, and reduced quality of life, highlighting the need for more flexible and effective at-home dialysis management solutions.
Innovation Solution
A health service provider computing system that ingests information from at-home dialysis devices and secondary devices, using machine learning models to detect alarms or failures, identify potential resolution actions, and train on historical data to predict and prevent future issues, thereby reducing the need for technician visits and improving patient care.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If in-center dialysis treatment is provided, then patient treatment is available, but patient flexibility and quality of life are limited
Solution Approach 1:
The system enables patients to perform dialysis treatments at home using automated equipment, allowing them to independently manage their own treatment schedule and location. The automated device monitors patient status and executes treatments without requiring constant center staff presence, thus providing flexibility while maintaining treatment reliability.
Solution Approach 2:
The system performs preliminary monitoring and assessment of patient status, device conditions, and treatment requirements before actual dialysis sessions. This advance preparation ensures that treatments can be successfully performed at home by anticipating and resolving potential issues before they arise, maintaining treatment reliability while enabling patient flexibility.
2Ease of operation
If at-home dialysis device is used, then patient flexibility is improved, but device maintenance complexity increases
Solution Approach 1:
The system continuously monitors device parameters, patient status, and treatment progress in real-time, providing feedback that automatically triggers maintenance alerts and recommendations. This continuous feedback loop enables proactive maintenance scheduling, reducing the complexity of managing home devices by providing clear, timely guidance on when and what maintenance is needed.
Solution Approach 2:
The system performs preliminary assessments of device condition and patient status before maintenance is actually needed, allowing for planned maintenance scheduling. By anticipating potential issues and scheduling maintenance in advance, the system reduces the complexity of home device management by transforming reactive repairs into predictable, scheduled events.
3Reliability
If technician visits are performed frequently, then device reliability is maintained, but time and resource consumption increase
Solution Approach 1:
The system performs preliminary monitoring and assessment of device conditions, patient status, and treatment requirements before actual dialysis sessions. This advance preparation ensures that treatments can be successfully performed at home by anticipating and resolving potential issues before they arise, maintaining treatment reliability while enabling patient flexibility.
Solution Approach 2:
The system continuously monitors device parameters, patient status, and treatment progress in real-time, providing feedback that automatically triggers maintenance alerts and recommendations. This continuous feedback loop enables proactive maintenance scheduling, reducing the complexity of managing home devices by providing clear, timely guidance on when and what maintenance is needed.
Data Source
AI summary
A health service provider computing system comprising one or more processing circuits including one or more processors communicably coupled to one or more memories having instructions stored thereon that, when executed by the one or more processors, cause the one or more processing circuits to ingest information associated with an at-home dialysis device from the at-home dialysis device and at least one secondary device; detect a trigger event indicating one of an alarm or a failure state associated with the at-home dialysis device; and identify at least one potential resolution action based on the one of the alarm or the failure state and the information ingested from the at-home dialysis device and the at least one secondary device.