DBS Stimulation Control via Patient Feedback Logging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical systems fail to effectively counteract acute reductions in therapeutic outcomes, as they lack the full context of the underlying causes, leading to inadequate responses to sudden symptom worsening or side effects in patients.
Innovation Solution
A system that utilizes user logging and feedback to identify patterns in therapy reductions, allowing for the automatic adjustment of stimulation programs in real-time, such as in deep brain stimulation systems, to address acute issues by selecting and implementing alternative therapy programs based on reported symptoms, side effects, and activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If closed loop systems rely on internal physiological sensing or longer-term external sensing, then the system can monitor patient conditions, but the system cannot counter acute reductions in therapeutic outcome because it lacks full context of what is really leading to the reduction
Solution Approach 1:
The system implements a feedback mechanism where patients report acute reductions in therapy through user input, and the system automatically adjusts stimulation parameters based on this feedback. The processing system receives feedback about the acute reduction, identifies the cause through pattern recognition, and modifies therapy parameters accordingly, creating a closed-loop feedback system that addresses both monitoring and contextual understanding.
Solution Approach 2:
The system introduces an intermediary processing system that acts as a mediator between the stimulation device and the patient's acute condition changes. This intermediary analyzes user reports, sensor data, and patterns to identify causes of acute reductions, then coordinates appropriate therapy adjustments, filling the information gap between monitoring and contextual understanding.
2Productivity
If the system automatically adjusts therapy programs based on user feedback and pattern recognition, then the system can respond to acute reductions in therapy, but the system complexity increases due to multiple sensors, processing systems, and adjustment mechanisms
Solution Approach 1:
The system performs preliminary actions by pre-configuring multiple therapy programs with different parameters and pre-establishing patterns for acute reductions. When an acute reduction is detected, the system can quickly select from pre-prepared adjustment strategies rather than creating new responses in real-time, reducing computational complexity while maintaining fast response.
Solution Approach 2:
The system implements self-service through automated pattern recognition and self-diagnosis capabilities. The processing system automatically identifies causes of acute reductions by analyzing patterns in user feedback and sensor data, then autonomously selects and implements appropriate therapy adjustments without requiring manual intervention, reducing the operational burden on users while maintaining system complexity in the background.
Data Source
AI summary
A system may include a stimulator configured to deliver a DBS therapy according to a first therapy program, and a processing system. The processing system may be configured for use to receive a logged event indicative of an acute reduction in therapy, automatically identify a second therapy program for the DBS therapy to address the acute reduction in therapy, deliver the DBS therapy according to the second therapy program, prompt the user to report whether the user likes or dislikes the DBS therapy delivered according to the second therapy program, and control the DBS therapy based on the report by choosing a third therapy program, reverting to the first therapy program, or continuing to deliver the DBS therapy according to the second therapy program.