A comprehensive review of digital twin in healthcare in the scope of simulative health-monitoring

Abstract

Objective: Digital twins (DTs) emerged in the wake of Industry 4.0 and the creation of cyber-physical systems, motivated by the increased availability and variability of machine and sensor data. DTs are a concept to create a digital representation of a physical entity and imitate its behavior, while feeding real-world data to the digital counterpart, thus allowing enabling digital simulations related to the real-world entity. The availability of new data sources raises the potential for developing structured approaches for prediction and analysis. Similarly, in the field of medicine and digital healthcare, the collection of patient-focused data is rising. Medical DTs, a new concept of structured, exchangeable representations of knowledge, are increasingly used for capturing personal health, targeting specific illnesses, or addressing complex healthcare scenarios in hospitals.

Methods: This article surveys the current state-of-the-art in applying DTs in healthcare, and how these twins are generated to support smart, personalized medicine. These concepts are applied to a DT for a simulated health-monitoring scenario.

Results: The DT use case is implemented using AnyLogic multi-agent simulation, monitoring the patient's personal health indicators and their development.

Conclusion: The results indicate both possibilities and challenges and provide important insights for future DT implementations in healthcare. They have the potential to optimize healthcare in various ways, such as providing patient-centered health-monitoring.

Keywords: Digital twin; health-monitoring; healthcare; personalized medicine; simulation.

Figure 1.

Structure of the paper.

Figure 2.

The digital twin TechWear Biomarker Data Flow model, implemented with AnyLogic, represents a monitoring system that captures and analyzes multiple biomarkers for patient health monitoring.

Figure 3.

Step 5: Vary parameters and initialization actions. This figure presents a detailed view of the fifth step to detail the used health indicators (based on AnyLogic).

Figure 4.

Detailed view of “Step2_Collect_Data”: Generation of synthetic data with triangular distribution and variables (based on AnyLogic).

Figure 5.

Conditions and parameter: This figure presents a detailed view of the conditions and parameters of transition12 (based on AnyLogic).

Figure 6.

Conditions and parameter: This figure presents a detailed view of the conditions and parameters of transition14 (based on AnyLogic).

Figure 7.

Time plot for various physiological parameters: Exemplary visualization of various vital sign parameters of a patient (heart rate, respiratory rate, temperature, etc.) over time (here in seconds) (based on AnyLogic).

Figure 8.

Wearable-assisted Biomarker Monitoring and Intervention System (BMIS) implemented with AnyLogic.

Figure 9.

“Main”—patient simulation area (based on AnyLogic).