Adaptive Digital Twin Control for Healthcare Reliability
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Solution Overview
Problem
Digital twins in healthcare face challenges in reliability and accuracy due to variations in user expertise, input quality, and model variations, leading to inconsistencies in output complexity and uncertainty, which can impact clinical decision-making.
Innovation Solution
A control system that adapts digital twins based on user characteristics, subject status, and historical usage data to generate parameter values for input, processing code, and output characteristics, enabling dynamic and automatic adjustments to improve accuracy and usability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital twins are personalized to better fit and model a subject, then the accuracy and reliability of the digital twin is improved, but the complexity of the system increases due to variations in user expertise, input quality, and model variations
Solution Approach 1:
The digital twin system dynamically adapts its parameters, complexity level, and functionality based on real-time assessment of user expertise, input data quality, and contextual requirements. The system transitions between different operational modes to match the user's needs, maintaining high reliability while managing complexity through dynamic adjustment rather than fixed configuration.
Solution Approach 2:
The system changes key parameters such as computational complexity, data processing depth, and output detail levels based on assessed user capabilities and input quality. By adjusting these parameters dynamically, the system maintains optimal reliability for each user context without being permanently overwhelmed by maximum complexity requirements.
2Measurement precision
If the digital twin provides multiple outputs with high accuracy, then the information quality is improved, but the output complexity and uncertainty variations increase due to different user needs and environmental conditions
Solution Approach 1:
Different outputs and information components are tailored to match local user needs and contextual requirements rather than providing uniform high-accuracy outputs for all scenarios. The system assesses which outputs are most relevant for each user context and adjusts their complexity and detail levels accordingly, maintaining measurement precision where needed while reducing complexity where unnecessary.
Solution Approach 2:
The system provides comprehensive accurate outputs when the user context and input quality support it, but selectively reduces the scope and complexity of outputs when user needs or environmental conditions limit the benefit of full accuracy. This partial action approach prevents output complexity from becoming a burden when high precision is not feasible or desired.
3Ease of operation
If the digital twin is adapted to fit user characteristics and needs, then the ease of operation is improved, but the device complexity increases due to automatic adjustments and parameter generation
Solution Approach 1:
The digital twin system performs self-adjustment based on assessment of user characteristics, expertise level, and contextual requirements. The control system automatically generates and applies parameter values without requiring manual user configuration, making the system easier to operate while containing the complexity management within the automated control logic rather than user interactions.
Solution Approach 2:
The system continuously assesses user interactions, input data quality, and operational context to feedback-adjust its configuration and parameter settings. This feedback mechanism simplifies operation by automatically adapting to user needs while managing complexity through systematic monitoring and adjustment based on observed performance and user characteristics.
Data Source
AI summary
Systems and methods are proposed for controlling a Digital Twin of a biological asset of a subject based on: (i) characteristics of a user of the DT; (ii) a status of the subject; and (iii) previous usage of the DT. Such control is facilitated through the generation of one or more parameter values for the DT. For instance, the parameter value(s) may define one or more elements or components of the DT, such as program code, input requirements, output characteristics or a value of a tunable element of the DT.