Automated Spinal Cord Stimulation Programming via Efficacy and Medication Analysis

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional spinal cord stimulation systems require extensive clinician expertise and time to program, especially with high frequency SCS, due to numerous electrode configurations and stimulation parameter combinations, complicating the identification of optimal therapy programs.

Innovation Solution

Automated methods and systems for programming signal generators in spinal cord stimulation systems, utilizing patient input and historical data to select and configure therapy programs based on efficacy, medication use, and lead position confidence, reducing the burden on clinicians.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional SCS programming methods are used with high frequency stimulation, then effective pain relief without paresthesia is achieved, but the complexity of programming increases significantly due to numerous electrode configurations and stimulation parameter combinations

Engineering Contradiction:

Improvepain relief efficacyVSAvoidprogramming complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs self-programming by automatically selecting optimal electrode configurations and stimulation parameters based on patient feedback and historical data, eliminating the need for manual clinician programming and reducing programming complexity while maintaining effective pain relief

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system incorporates patient feedback loops where patient responses to stimulation are automatically processed to adjust and optimize programming parameters, enabling the system to adapt and select the best configuration automatically without requiring extensive clinician expertise

Inventive Principle:

Principle #23Feedback

2Reliability

If manual programming by clinicians is used, then optimal therapy programs can be selected, but the time and expertise required for programming increases

Engineering Contradiction:

Improvetherapy optimizationVSAvoidprogramming time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system automatically selects and optimizes therapy programs using patient feedback and historical data, performing the optimization function that previously required clinician expertise and time, thereby reducing programming time while maintaining therapy effectiveness

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system pre-processes patient feedback and historical data to prepare optimized programming recommendations in advance, so that when programming is needed, the system has already performed the analytical work that would otherwise require clinician time and expertise

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11198001B1Systems and methods for automatically programming patient therapy devices

Publication Date: 2021.12.14 NEVRO CORP

AI summary

Methods for automatically programming a signal generator in a patient therapy system and associated systems are disclosed. A representative method comprises retrieving data including therapy program parameters, level of efficacy, and medication use corresponding to a plurality of time periods; identifying from the data a target time period having a corresponding level of efficacy; determining from the data if medication was used during the target time period; determining from the data if medication was used during a prior time period immediately before the target time period; calculating a lead position confidence factor; and programming the signal generator to repeat therapy with the therapy program parameters corresponding to the target time period if the confidence factor is greater than a threshold value and medication was used during the prior time period and not during the target time period.