DT4PCP: A Digital Twin Framework for Personalized Care Planning Applied to Type 2 Diabetes Management

TL;DR

This paper presents a practical digital twin framework for personalized care planning in Type 2 Diabetes, integrating real-time data, predictive modeling, and social determinants to improve patient management and reduce emergency visits.

Contribution

It introduces a comprehensive digital twin framework specifically designed for T2D management, incorporating social determinants and predictive analytics for personalized care.

Findings

Real-time data collection enables personalized interventions.

Predictive models estimate emergency risk effectively.

Simulation of care strategies reduces ED visits.

Abstract

Digital Twin (DT) technology has emerged as a transformative approach in healthcare, but its application in personalized patient care remains limited. This paper aims to present a practical implementation of DT in the management of chronic diseases. We introduce a general DT framework for personalized care planning (DT4PCP), with the core components being a real-time virtual representation of a patient's health and emerging predictive models to enable adaptive, personalized care. We implemented the DT4PCP framework for managing Type 2 Diabetes (DT4PCP-T2D), enabling real-time collection of behavioral data from patients with T2D, predicting emergency department (ED) risks, simulating the effects of different interventions, and personalizing care strategies to reduce ED visits. The DT4PCP-T2D also integrates social determinants of health (SDoH) and other contextual data, offering a comprehensive view of the patient's health to ensure that care recommendations are tailored to individual needs. Through retrospective simulations, we demonstrate that integrating DTs in T2D management can lead to significant advancements in personalized medicine. This study underscores the potential of DT technology to revolutionize chronic disease care.