Neurostimulation Therapy via Machine Learning and Secure Remote Programming
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Solution Overview
Problem
Current implantable medical devices and personal medical devices are typically programmed in-person or in-clinic using short-range communication links, limiting remote patient care and increasing healthcare delivery costs.
Innovation Solution
A digital health network architecture that enables remote patient therapy through machine learning operations, allowing for secure telehealth sessions and remote programming of implantable medical devices using cloud-centric digital health implementations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If short-range communication links are used for device programming, then security against unauthorized access is improved, but remote patient care capability deteriorates
Solution Approach 1:
The patent introduces a remote communication system with encrypted data transmission as an intermediary between the implantable device and external programming devices. This mediator enables secure remote access by establishing authenticated communication channels that prevent unauthorized access while allowing legitimate remote programming operations.
Solution Approach 2:
The patent replaces the mechanical requirement of physical proximity (short-range communication) with wireless remote communication capabilities. This substitution allows clinicians to program and monitor implantable devices from distant locations, eliminating the need for in-person visits while maintaining security through encrypted digital communication protocols.
2Reliability
If in-person programming sessions are required, then device security is improved, but healthcare delivery costs increase
Solution Approach 1:
The patent enables self-service capabilities where patients can initiate and manage their own therapy programming remotely through secure digital interfaces. This reduces the need for costly in-person clinical visits while maintaining device security through authenticated access controls and encrypted communication channels.
Solution Approach 2:
The patent introduces a secure remote communication infrastructure as an intermediary that enables cost-effective healthcare delivery. This mediator provides encrypted data transmission and authenticated access, allowing clinicians to perform programming sessions remotely at lower costs while maintaining the same security standards as in-person visits.
3Reliability
If machine learning operations are implemented for remote therapy, then patient outcomes are improved, but system complexity increases
Solution Approach 1:
The patent segments the machine learning system into separate functional modules: data collection from the implantable device, data transmission through secure communication channels, machine learning processing on external servers, and therapy adjustment based on ML predictions. This segmentation reduces overall system complexity by distributing computational burden and isolating complex ML operations from the implantable device itself.
Solution Approach 2:
The patent introduces external computing infrastructure and secure communication protocols as intermediaries that handle the complex machine learning operations. This mediator allows the implantable device to benefit from advanced ML-driven therapy optimization without requiring complex ML algorithms to be embedded within the device, thus reducing device complexity while improving patient outcomes.
Data Source
AI summary
The present disclosure provides systems and methods for providing neurostimulation therapy according to patient features. The patient features may be analyzed to develop a patient model between physiological and/or patient reported features and optimal settings for a neurostimulation therapy using machine learning operations. The model is used to control ongoing neurostimulation therapy for the patient.