Ambulatory Device Programming Using Digital Twins for Battery Life
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Solution Overview
Problem
Ambulatory medical devices often experience suboptimal programming, leading to inefficient resource consumption and reduced device lifespan due to inappropriate parameter settings, necessitating frequent in-person follow-ups and unnecessary medical interventions.
Innovation Solution
A system that utilizes digital models to simulate and predict the outcomes of programming changes for ambulatory medical devices, optimizing resource usage by continuously updating patient and device models based on physiologic information, and implementing recommendations through a two-factor approval process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ambulatory medical devices use frequent monitoring and continuous high-resource operation to detect patient conditions, then patient care quality and early detection capability are improved, but device battery life and resource durability are reduced
Solution Approach 1:
The system dynamically adjusts device operating modes and resource consumption based on patient risk stratification. High-risk patients receive continuous high-resource monitoring, while low-risk patients receive reduced monitoring to conserve battery life. This dynamic adaptation resolves the contradiction by making resource usage proportional to actual medical need rather than operating at constant maximum capacity.
Solution Approach 2:
The system changes operational parameters (monitoring frequency, data transmission intervals, sensor activation) based on determined patient risk levels and simulated programming outcomes. By adjusting these parameters according to actual patient needs rather than maintaining fixed high-resource settings, the system extends device battery life while maintaining adequate monitoring capability.
2Reliability
If ambulatory medical devices implement comprehensive monitoring and frequent programming adjustments, then patient outcomes are improved, but healthcare costs and unnecessary interventions increase
Solution Approach 1:
The system performs simulation of programming changes on digital patient models before applying actual device reprogramming. This preliminary testing allows prediction of outcome improvements and avoids unnecessary in-person follow-ups and interventions. By pre-evaluating programming changes virtually, the system reduces healthcare resource consumption while maintaining patient outcome quality.
Solution Approach 2:
The system creates and uses digital copies (digital twins) of patients to test programming changes before applying them to actual patients. These digital models allow comprehensive evaluation of programming strategies without consuming real healthcare resources or exposing patients to unnecessary interventions, thereby reducing healthcare costs while preserving outcome quality.
3Reliability
If ambulatory medical devices require frequent in-person follow-ups for programming optimization, then device performance is improved, but patient convenience and device usability are reduced
Solution Approach 1:
The system enables remote programming optimization using wearable sensors and automated analysis, eliminating the need for frequent in-person clinic visits. The device and associated system automatically monitor patient metrics, simulate programming changes, and implement optimizations remotely, allowing patients to self-manage their device programming without requiring physician presence, thus greatly improving ease of operation.
Solution Approach 2:
The system replaces the mechanical requirement for in-person physical follow-ups with remote digital monitoring and wireless communication. By substituting the physical clinic visit mechanism with automated remote system operations, the patent maintains programming optimization capability while dramatically reducing patient burden and improving usability.
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 updating a digital model of a patient based on received physiologic information of the patient, determining a change in the updated digital model of the patient, generating, based on the determined change in the updated digital model of the patient, a programming recommendation for the ambulatory medical device to optimize resources of the ambulatory medical device, and programming the ambulatory medical device based on the generated programming recommendation.