Ambulatory Device Programming Using Patient Digital Twin Feedback
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Solution Overview
Problem
Ambulatory medical devices often suffer from suboptimal programming, leading to inefficient resource consumption and reduced device lifespan due to inappropriate therapy or parameter settings, necessitating frequent in-person follow-ups and resource-intensive adjustments.
Innovation Solution
A system that simulates programming changes on digital models of patients and devices, predicting outcomes and optimizing resource usage by continuously updating training data based on patient physiologic information, and implementing changes with a two-factor approval process for safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent in-person follow-ups and resource-intensive adjustments are performed to optimize device programming, then device efficiency and patient outcomes improve, but healthcare costs and resource consumption increase
Solution Approach 1:
The system creates a digital twin (virtual model) of the patient that replicates physiological responses to device programming changes. This digital copy allows clinicians to test and optimize programming parameters remotely without requiring repeated in-person visits, thereby improving device efficiency while reducing healthcare resource consumption
Solution Approach 2:
The digital twin serves as an intermediary between the actual patient and the device programming process. It mediates the optimization by simulating patient responses to programming changes, allowing remote adjustment and reducing the need for direct patient-clinician interactions, thus lowering resource consumption while maintaining reliability
2Ease of operation
If suboptimal programming is used to simplify device operation, then ease of operation improves, but device lifespan and resource efficiency deteriorate
Solution Approach 1:
The system performs preliminary optimization by using the digital twin to simulate and determine optimal programming parameters before actual device implementation. This advance preparation ensures the device is programmed optimally from the start, extending its lifespan and resource efficiency while maintaining ease of operation through automated remote programming
Solution Approach 2:
The digital twin enables the system to self-optimize programming parameters without requiring complex manual adjustments. The virtual model automatically tests and identifies optimal settings, allowing the device to operate at peak efficiency throughout its lifespan with minimal intervention, thus extending usable duration while maintaining operational simplicity
3Productivity
If personalized programming optimization is implemented through digital models, then device efficiency and resource optimization improve, but system complexity and computational requirements increase
Solution Approach 1:
The digital twin platform serves multiple functions: it simulates patient physiology, tests programming changes, predicts outcomes, and guides optimization decisions. This multi-functional approach consolidates complex computational tasks into a single universal system, achieving resource optimization without proportionally increasing overall system complexity
Data Source
AI summary
Systems and methods are disclosed for evaluating patient condition and programming an ambulatory medical device to optimize resources of the ambulatory medical device, including simulating application of a proposed programming change on a digital model of the patient or the ambulatory medical device and predicting a change in the digital model in response thereto, programming the ambulatory medical device to implement a programming change, updating the digital model based on received physiologic information of the patient subsequent to the ambulatory medical device implementing the programming change, and updating, based on a determined difference between the updated digital model and the predicted change in the digital model, the training data to account for or include the programming change and the physiologic information of the patient.