Wellness Factor Algorithm for Implantable Pulse Generator Therapy Optimization
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Solution Overview
Problem
Current methods for evaluating the effectiveness of implantable neurostimulator therapies, such as Spinal Cord Stimulation (SCS) and Deep Brain Stimulation (DBS), rely heavily on subjective qualitative measurements and lack widespread use of quantitative measurements, making it difficult to universally assess therapy effectiveness for all patients.
Innovation Solution
A system that employs a wellness modelling algorithm to correlate qualitative and quantitative patient measurements, allowing for the determination of a wellness factor that can adjust the stimulation program to improve therapy efficacy, using a combination of qualitative patient feedback and objective quantitative data from sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective qualitative measurements are used to evaluate therapy effectiveness, then patient feedback can be obtained, but the assessment lacks objectivity and reliability
Solution Approach 1:
The patent introduces an intermediary algorithm that processes both qualitative patient feedback and quantitative sensor measurements to generate a wellness factor. This intermediary computation layer transforms subjective inputs into an objective assessment metric, resolving the contradiction between ease of feedback collection and reliability of assessment.
Solution Approach 2:
The system implements a feedback mechanism where quantitative measurements from sensors continuously monitor patient status and are combined with qualitative feedback. This closed-loop feedback system enhances assessment reliability by objectively verifying and adjusting therapy effectiveness based on multiple data sources.
2Device complexity
If only qualitative measurements are used, then implementation is simple, but measurement precision is insufficient
Solution Approach 1:
The patent merges qualitative patient feedback with quantitative sensor measurements into a unified wellness factor assessment. By combining these complementary measurement types through an integrated algorithm, the system achieves high measurement precision without requiring complex separate systems for each measurement type.
Solution Approach 2:
The wellness factor algorithm serves multiple functions: it processes qualitative feedback, integrates quantitative measurements, generates objective assessments, and guides therapy adjustments. This multi-functional approach achieves high measurement precision while maintaining relatively simple system architecture.
3Reliability
If quantitative measurements are incorporated into the assessment system, then objectivity and reliability improve, but device complexity increases
Solution Approach 1:
The system employs self-service principles by using the implantable device's existing sensors to automatically collect quantitative measurements and integrate them with patient feedback. The algorithm autonomously processes data and generates wellness factors without requiring external complex processing equipment, thus improving reliability while limiting complexity increase.
4Productivity
If a comprehensive algorithm using both qualitative and quantitative data is implemented, then therapy optimization improves, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary action by continuously collecting and pre-processing quantitative measurements in the background while the patient provides qualitative feedback. The algorithm has pre-established correlation models that enable rapid integration of new data, reducing real-time processing requirements and allowing comprehensive therapy optimization without significant time loss.
Data Source
AI summary
A system is disclosed in one example which allows for modelling the wellness of a given Implantable Pulse Generator (IPG) patient. The modelling, embodied in an algorithm, uses one or more qualitative measurements and one or more quantitative measurements taken from the patient. The algorithm correlates the qualitative measurements to the various quantitative measurements to eventually, over time, learn which quantitative measurements best correlate to the qualitative measurements provided by the patient. The algorithm can then using current quantitative measurements predict a wellness factor or score for the patient, which is preferably weighted to favor the quantitative measurements that best correlate to that patient's qualitative assessment of therapy effectiveness. Additionally, the wellness factor may be used to adjust the stimulation program that the IPG device provides to the patient.