Neuromodulator Control System for Patient-Reported Pain Data Correlation
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Solution Overview
Problem
Existing neuromodulation systems struggle to reliably quantify patient-reported pain experiences outside of clinical settings and correlate these experiences with optimal dosing parameters for personalized treatment regimens.
Innovation Solution
A system comprising a neuromodulator with a control system that optimizes dosing based on patient-reported information, collected through electronic diaries and correlated with dosage logs, to generate customized dosage regimens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If patient feedback is collected outside clinical setting, then dosing optimization is improved, but data reliability deteriorates
Solution Approach 1:
The system implements a feedback loop where patients report pain scores and treatment effectiveness in electronic diaries, which are then processed by the control system to automatically adjust dosing parameters. This continuous feedback mechanism enables dosing optimization based on real-world patient experiences while maintaining data reliability through structured collection protocols and validation mechanisms.
2Measurement precision
If quantitative pain measurement is implemented, then dosing precision is improved, but system complexity increases
Solution Approach 1:
The electronic diary serves as an intermediary tool between the patient and the control system, providing a standardized interface for pain score reporting. This intermediary structure simplifies the overall system by using a dedicated data collection framework that bridges the gap between subjective patient experience and objective dosing parameters, reducing the complexity of direct measurement systems.
3Reliability
If personalized dosage regimen is generated, then treatment effectiveness is improved, but data processing complexity increases
Solution Approach 1:
The control system processes patient-reported data and automatically adjusts dosing parameters such as intensity, duration, and frequency based on pain score changes and treatment effectiveness feedback. By systematically varying these parameters in response to measured outcomes, the system generates personalized dosage regimens that optimize treatment effectiveness while managing data processing complexity through algorithmic approaches.
Data Source
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AI summary
Methods and apparatuses for collecting and quantifying patient-reported data from a neuromodulator. The patient-reported data typically includes a pain score that quantifies the level of pain experienced by the patient at a particular time. The apparatus can include a user interface that allows the patient to enter such information in real time. The methods and apparatuses can include a correlation process, whereby each patient-reported entry is correlated with a corresponding neuromodulation treatment. This correlation can be used to identify treatment parameters and dosages that are most effective, and to further identify when certain dosages are most effective. This information can further be used as feedback to generate optimized treatments for a specific patient and iteratively improve the treatments. In some cases, the correlation process identifies under reported or over reported data, which can be filtered out to provide more accurate optimized treatments.