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

DTs in cancer research and therapy

In oncology, interdisciplinary collaboration, for example, with other clinical disciplines ³⁷ and informed decision-making through tumor boards enable personalized therapeutic approaches. Molecular pathology identifies individual tumor vulnerabilities, and AI could optimize these processes in the future.^{38,37,40–42,39} Advances in cancer research and precision medicine provide a wealth of data enabling tumor classifications and more personalized treatments based on the genetic profiles, supported by DT and computational oncology. Epigenetics, which involves the analysis of DNA methylation and gene expression patterns, plays a crucial role in treatment optimization. ⁴³ Batch et al. ⁴⁴ introduced a DT for predicting the “patterns of metastatic progression across cancer sites” within the body by analyzing structured radiology reports using natural language processing based on neural network types. The authors suggest that analyzing a large number of reports may simplify the prediction of cancer spread across a patient’s organs, thus contributing to PM. ⁴⁴

Thiongó and Rutka discussed the possibilities of using DTs as a future technology to improve the prediction of neurological complications of pediatric cancer patients, focusing on the factors of precision medicine, predictive analytics, possibilities to model cancer care and to level out different opinions of clinicians, or extend clinical trials with the help of DTs. ⁴⁵

Kim et al. ⁴⁶ developed a DT model for prostate cancer patients. It incorporates clinical patient data, prostate cancer process knowledge, expert knowledge and uses ML for training a prediction model for pathology and biochemical recurrence. The DT supports clinical decisions regarding the illness. The DT solution by Moztarzadeh et al. ³⁶ incorporates a metaverse, a layer of the DT that receives new data on breast cancer patients and updates the DT based on different ML models, thus providing a new basis for decision support. They recommend using individual DTs for each patient to enhance monitoring within the metaverse. ³⁶ In the field of tumor-agnostic cancer therapy, eHealth and PM targeted therapies are developed in DT utilizing antibodies and inhibitors, often combined with chemotherapy. Advanced molecular diagnostics and AI methods aid in identifying personalized therapies, especially in rare cancer cases or when standard treatment options are lacking. Tumor-agnostic cancer therapy, focusing on specific cell characteristics, is becoming increasingly relevant. ⁴⁷ A concrete example of this targeted therapy in PM is the treatment of melanoma using DNA analysis. Medications that target specific mutations can temporarily slow tumor growth; however, they offer no benefits to patients lacking these mutations.^38,7

DTs in the context of network medicine

Network medicine utilizes tools from network science, encompassing networks of biological, technological, or social systems, to investigate the molecular complexity of diseases and the relationships between different phenotype. ⁴⁸ This leads to the identification of disease genes, modules, and pathways or the discovery of drug targets and biomarkers. A better understanding of cellular networks can improve clinical practices through more accurate biomarkers, refined disease classification, and personalized therapeutic approaches. ⁴⁸

Medical DTs should integrate all relevant data sources and variables to form multi-layered modules encompassing biological molecules such as mRNAs, genes, and proteins.^48,1 These modules enable computer-simulated procedures and treatment. Experiments are conducted virtually to support hypothesis development, testing, diagnostics, and therapy. Diagnostic and therapeutic decisions should also consider other data types, including symptoms, psychopathology, phenotypic modules, and environmental factors.^48,1 Hussain et al. ⁴⁹ presented a digital twin in the field of network medicine by using the DT concept to create a diagnostic method for strokes, which may support clinical decision-making for patient treatment.

DTs in cardiology

Research about heart digital twins (heart DTs) offers novel diagnostic possibilities and improved therapeutic approaches for cardiovascular diseases.^{4,8,50,51,11,52} Rudnicka et al. ⁵³ conducted a literature study that concluded improvements in data quality, image processing, and 3D imaging, combined with technologies like extended reality and AI, may contribute to developing effective heart diagnostics and personalized cardiology. The aforementioned technologies may be the basis for the construction of a heart DT, which again may support applications related to diagnosis, prognosis and therapy optimization. While these are positive aspects, greater importance should be placed on the ethical dimensions of DTs, such as the responsible integration of AI. ⁵³ Corral-Acero et al. ⁵⁴ discussed a vision of using DTs for precision cardiology with the help of using computational models. Like Rudnicka et al., ⁵³ they also envision an application of DTs for diagnosis, prognosis, and treatment plans. ⁵⁴ In precision cardiology, DTs should incorporate statistical and mechanistic models to create synergies and support the specified applications. ⁵⁴ DIGIPREDICT, a EU funded research project, develops a novel DT for predicting disease progression and early intervention in infectious and cardiovascular diseases.^55,11 Advanced imaging algorithms and technologies like AI, ML, and decision logic support the creation and simulation of heart DTs.^8,50

In addition to researchers, technology companies such as Siemens are also working on creating medical DTs.^4,8 Technologies like Philips’ HeartNavigator system for heart imaging and analysis ⁵⁶ contribute to diagnosing various heart problems and monitoring the effectiveness of therapy approaches.^57,5

Heart DTs provide more precise diagnostic and treatment options for cardiovascular diseases. These models can non-invasively identify biomarkers and create personalized DT models, offering applications such as predicting pressure drops in flow obstructions and optimizing medication.^11,5 Electrocardiographic imaging (ECGI) combines imaging and electrophysiological data non-invasively, supports heart function monitoring at home, and transmits results to doctors via a mobile app. It involves advanced techniques like ECG classification learning algorithms, nonlinear MRI registration, heart surface mesh extraction, and high-density sensors. ⁵

DTs can be used in medicine to simulate dosage effects and device reactions before treatments, assessing the suitability of therapy or devices for patients. FEops’ HEARTguide, for example, provides personalized heart DTs to optimize the care of patients with specific structural heart diseases after transcatheter aortic valve implantation. “The FEops HEARTguide platform (FEops NV) is cloud-based and uses the virtual twin technology based on patient-specific digital replicas of the heart to aid the clinician with procedural planning and device sizing.” ⁵⁸ Linking individual DTs with AI-based anatomical analyses forms the basis for data-driven insights improving the treatment course for heart diseases.^5,51,59,60

DTs play a growing role in treating cardiovascular diseases and aneurysms, enhancing ablation guidance for infarct-associated ventricular tachycardia (higher heart frequency based on an infarct), and generating synthetic physiological data for comprehensive information on blood pressure and flow changes. Patient-specific aneurysm DTs by the French startup Sim&Cure support surgeons in identifying optimal implants through the creation of a 3D aneurysm model for minimal invasive endovascular repairs, with promising initial results. Further research is necessary to explore these applications fully.^5,59,61

DTs in surgery

DT technology enables more accurate surgical planning and minimizes unintended anatomical damage during neurosurgery, vascular surgery, and interventional surgery. In vascular surgery, it contributes to the development of diagnostic tools and the simulation of surgical procedures. The combination of in-vivo vibration measurements and virtual data increases the accuracy of DT computer models and expands diagnostic and treatment options, taking into account clinical, morphological and hemodynamic data. ⁵ Bjelland et al. ⁶² conducted a study to determine the applicability of DTs for arthroscopic surgery, in particular a knee surgery. Their motivation is the trend of using simulators for training surgeons, which could be enhanced with DTs incorporating patient-specific and intraoperative data. The authors view advantages in using DTs over existing simulators in their adaptability and flexibility and not a pre-fixed training sequence. They determined key aspects for DTs in the form of “patient-specific imaging, real-time intraoperative data collection techniques, material models for biomechanical tissue, tissue deformation simulation, cutting simulation, virtual interaction, haptic feedback, and system architectures.” ⁶²

DT technology also enhances the training of surgeons and teaches technical skills in various disciplines. Using virtual reality platforms, surgical training becomes more patient-centric, leading to improved performance. ⁶³ The use of DTs in the field of precision medicine, like in surgery, is still under development. Limitations, regarding computational and processing power have a major impact on the realistic use of DT. This is only possible, if, for example, the MRI scan for knee injury is mandatory and the quality of the scan is in high quality, such as the nonaltering of the digital representation, through the automatic segmentation of anatomical structures. ⁶² There is a need for formulating clear limitations in future research for the use of DT in precision medicine such as surgery to avoid potential risks.

DTs in orthopedics

DTs in orthopedics enable real-time monitoring and analysis of the lumbar spine, as well as the prediction of biomechanical properties. This requires combining physics-based experimental models, data-driven numerical models, and multi-agent system technologies.^15,5 A research team developed a DT of the lumbar spine using human motion capture technology, wearable VR devices and sensor data. ⁶⁴ VR devices and sensor data capture body position and posture, while inverse kinematics, finite element methods, and Gaussian process regression are utilized for analysis. They developed a 3D virtual reality system, which enables orthopedic treatments and effective warnings, especially in spinal rehabilitation.^15,5 Furthermore, DTs have been used in orthopedic surgical models to provide clinical decision insights. DT models evaluate factors like mechanical stability and interfragmentary stress, which help assess the risk of recurrent fractures. A patient-centered model combining CT, AI, and DT technologies has been developed to improve the accuracy of alignment and adjustment examinations of the subtalar joint axis. ⁵

Technical challenges for implementing DTs in healthcare

In the application of DTs in healthcare, such as patient care and hospital management, there are challenges ranging from the precision of the simulation, validation approaches, technical limitations and data collection and integration.^{73,72,74,5,75,4} The lack of a standardized architecture and individual solutions as well as the limited use of the potential of DTs over its entire life cycle pose further problems. Effective use of DTs requires a data flow between different companies ⁷⁶ and a sufficient data quality. ⁴ Creating complex high-fidelity models for DTs is a technical challenge. This is particularly true when considering multiphysical processes (mechanical, thermal, electrical, magnetic, chemical, or biological) and the integration of information across different scales or levels, coupled with a lack of experimental data. Such models require advanced mathematical methods, algorithms, and powerful computing resources, including 5G, sensor technology, and quantum computing.^{73,72,74,5,75} There are also technical limitations between the interaction of DT objects and DT people, such as the real-time connection and the need for standardization. ⁸ Times and latency for data gathering and transfer between sensors and data analysis components need to be considered. Additionally, bridging the gap between DT-calculated results and real-world scenarios remains a challenge. For example, unforeseen or underestimated risks may arise during the planning of a surgical procedure and become apparent during the actual surgery. ⁵

Security, data protection, and ethics of DTs

PM poses challenges at the intersection of business and data protection, particularly in the fields of pharmacy and medical technology, including standards for data trading and patient information. Medical innovations and data transfer are facilitated by manufacturers, with state repositories serving as data storage. This also involves far-reaching adjustments in infrastructure, organization, financing and compliance with regulatory and data protection aspects. ⁶⁶

The comprehensive application of DTs and AI in the healthcare sector requires solutions for current regulatory and data protection challenges. While medical environments are subject to strict (country specific) regulations such as HIPAA (US Health Insurance Portability and Accountability Act), FDA (US Food and Drug Administration), or the EU GDPR (EU General Data Protection Regulation), non-medical areas are less regulated.^4,77,5 The exploitation of these regulatory loopholes by technology companies such as Facebook and Google collects and monetizes personal health data, which requires a review of the role of medical devices and data. ⁷⁷

There is also mistrust of AI-supported decisions, for example, with regards to reliability of the proposed recommendations or potential bias against patient groups due to used training data or detected patterns. Armeni et al., ⁴ for example, mentioned that currently there is oftentimes a bias in health data due to data of white men being overrepresented, ⁴ which might pose the question if the same recommendation may be applied for women. Implementing validation and approval regulations for predictive biomedical computer models could reassure doctors and patients, who are often skeptical about AI-supported decisions.^4,8 The blending of medical and health-related areas through digital biomarkers increases the difficulty of integrating wearables into medical applications, as current regulatory frameworks do not sufficiently consider data outside clinical environments. Interdisciplinary collaboration is needed to investigate these challenges and discuss international data processing standards, while analyzing the underlying technologies and their impact on medical and health-related areas.^78,77 Wearables in medical applications of wearables require adjustments to the regulatory framework to ensure security and data protection. The distinction between end-user and medical technology, as well as meeting regulatory requirements, are key challenges. There is a need for a technical platform for recording vital parameters and a clinical integration, taking security and encryption techniques into account. The implementation of DTs and AI in healthcare requires an examination of technical, regulatory, ethical and responsibility issues. This necessitates intensive interdisciplinary scientific exchange.

Limitations

A major challenge of integrating DTs in healthcare is the integration of real-time data from multiple sources. A digital twin is a complex structure representing a physical entity, such as a patient or an illness. The real-time integration of data from various sources and its following analysis remains highly complex. For that advanced architectures are needed, which are highly secure, patient centric and provide a high standard of data protection. The healthcare infrastructure needs to be adjusted to the idea of digital twin applications, which may be challenging, but future-oriented. In addition, the concerns regarding to the aforementioned bias in medical training data needs to be addressed to prevent non-fitting recommendations or decisions based on the DT.

Advantages of simulation techniques for DTs in healthcare

Simulation techniques offer numerous advantages in the development and optimization of DTs. They enable the creation of simulation systems of DTs to create a realistic representation and analysis of the physical twin. Through simulation, the behavior and responses of these systems under various conditions and scenarios can be examined without directly affecting real systems or processes.^81,80

Advantages of simulation techniques in relation to DTs:

Risk reduction: Simulations allow critical, risky or future-oriented scenarios to be tested in a safe environment. This reduces the risk when implementing new strategies in actual operation.^82,80

Simulation software solutions

There are various simulation software solutions available on the market. Abar et al. ⁷⁹ conducted a comprehensive survey of 85 ABMS tools. This survey was based on criteria such as the availability of source code, agent interaction, the availability of the programming language used, APIs or graphical programming interfaces, the necessary compiler, operating system, the effort required to implement a use case, application areas, and scalability. ⁷⁹ Although healthcare applications are mentioned and various tools classified as applicable, the study does not specifically discuss DTs implemented with ABMS, likely due to its 2017 publication date. Nonetheless, the study also evaluates Anylogic which was chosen for the prototypical implementation of the DT in this article due to its extensive functional scope. AnyLogic is evaluated as applicable in healthcare as a high- or large-scale ABMS tool and a “user-friendly graphical environment for visual model development.” ⁷⁹

AnyLogic is a widely used platform for creating various types of simulations, including production processes, traffic flows, supply chain management, and personalized medical applications. It provides a versatile environment suitable for modeling various systems and scenarios in PM.^84,90 Other solutions on the market include Simio, ⁹¹ Arena by Rockwell Automation, ⁹² and others.

Compared to the other aforementioned solutions, AnyLogic stands out for its flexibility, versatility, and support for a broad range of simulation methods, which might be used in DT development. AnyLogic allows for integrating system dynamics, discrete events, and agent-based models within a single environment, significantly enhancing model efficiency and optimizing solutions in PM. This allows users to select the simulation approach that best meets their specific needs. This feature is particularly useful in interdisciplinary applications, such as PM, where complex and dynamic systems need to be accurately modeled. Furthermore, AnyLogic provides a free version that already offers a wide range of features, and special licenses are available for universities, making it accessible for educational and research purposes.^{84,86,87,85,88,89}

Short comparison between simulation software solutions:

AnyLogic: As a comprehensive platform, AnyLogic supports a multitude of simulation approaches and is especially suited for modeling complex, interdisciplinary systems. ⁸⁵

Introduction to agent-based modeling with AnyLogic

Agent-based modeling is an advanced simulation method used to mimicking complex systems and multi-agent systems through autonomous agents, which range from individuals to organizations. These agents interact with one another, resulting in emergent behaviors that contribute to the system’s overall dynamics. One software platform for different types of multi-agent simulations is AnyLogic. AnyLogic is a powerful software platform that integrates agent-based modeling with other simulation methods, enabling detailed analyses of complex systems. It supports the representation of DTs, increasingly significant in the era of Industry 4.0 and PM. ⁸⁵ AnyLogic facilitates the creation of realistic models through the integration of high-resolution real data. Its multi-method approach allows for individual and autonomous agent modeling, provides a platform for training AI and ML algorithms, and supports synthetic data generation. This approach bridges the gap between theory and practice, assisting businesses in making informed, evidence-based decisions. It supports the modeling of complex scenarios, such as pharmaceutical launches or the use of wearables, by simulating the dynamic interactions between various actors.^84,86–88 In the next section, a design concept will be introduced, which will later be implemented in the form of an agent-based simulation with AnyLogic.

The emergence of medical DTs—an industrial legacy

The transition of DTs from industrial applications to medical uses marks a significant advance in simulation technologies. Initiated in the manufacturing industry to optimize product life cycles and maintenance, the technology is undergoing a transformative adoption in the healthcare sector. The transition is facilitated by rapid digitization and access to extensive datasets, which supports the creation of precise, patient-specific models. In medicine, DTs utilize advanced industrial simulation techniques that have been adapted to address the unique challenges of medical data analysis and diagnostics. Integrating this technology to medical practice promises a revolution in PM by enabling individual treatment plans and preventive measures based on the specific patient data. Furthermore, integrating DTs into medical workflows facilitates research and development of new therapies. This is achieved by offering a deeper understanding of disease mechanisms, pathogenesis, and drug effects on the human body.^97,15

PM with DTs

At the intersection of health and technology, DTs offer a visionary perspective for medicine. This DT model, called TechWear Biomarker Data Flow model, demonstrates how IoT components continuously capture health data—ranging from wearables such as smartwatches, fitness trackers, or smart patches, to cloud-computing systems—thus forming the foundation for a personalized health assistant for every individual. Particularly for the cardiovascular system, it enables the detailed simulation of data flow, from capturing sensor data to analysis. This illustrates the potential for the future implementation with real data and highlights the potential of DTs to revolutionize medical diagnostics and treatment. By simulating synthetic data, the DT model clarifies the precision and timeliness of the digital representation of the patient, providing insights into future implementations based on real data.

DT model and application use case

The core component and idea of the DT-TechWear Biomarker Data Flow model is a simulation of a holistic data processing process from patient to application in diagnosis, analysis, or treatment decisions. The DT model serves as a crucial link between the real and digital worlds, with a specific focus on the cardiovascular system.

The concept of the model allows the capturing of different kinds of data related to the patient and the treatment process. Therefore, it could be simulated cyclically to capture biomarker data from, for example, wearables or smart patches, which are then processed in a dynamic health monitoring system. Here, synthetic data was used to represent the process. However, real-time data can also be used to run the model. The process, as shown in Figure 2, therefore, encompasses a continuous data collection state called “Step1_Wearables.” Step 1 (see Figure 2, Step 1) describes the physical world and Step 2 (see Figure 2, Step 2, etc.), onwards presents the virtual world of the DT. Next, the data aggregation from various sources including IoT, sensor technologies, and manual health reports, is represented in the state “Step2_Collect_Data,” while a secure data storage is the focus of “Step3_Save_Data.” This state is a fundamental step for in-depth subsequent analyses and interpretations. These processes are supported by simulation techniques that generate and adjust realistic health data, illustrating DTs’ integration into medical processes and thereby enabling precise medical analyses.

OPEN IN VIEWER

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.

Emulation of human health data

A key feature or requirement of the DT TechWear Biomarker Data Flow model is its ability to realistically emulate human health data. It uses ‘‘uniform’’ probability distributions to generate random yet realistic starting values for various health parameters. The uniform probability distribution method was used for the initialization of the vital signs, such as heart rate, due to its simple and effective way to generate values which are equally probable within a given healthy range. The range may be defined based on a realistic min. and max. value. With the “uniform”-method, it is possible to provide equally, resulting distributions of starting values. The triangular distribution method is preferred, due to its possibility to model natural fluctuations of physiological parameters, as it is defined through a minimum, maximum, and modal value. Due to its great simulation approach, triangular provides natural fluctuations, which can be modeled realistically by placing the most frequent value in the middle range, while more extreme values occur less frequently. The Gaussian distribution could be useful for some applications, however, it is often unsuitable for biomarkers as it allows unlimited values, which goes against the physiological realistic criteria. The “uniform” distribution and the “triangular” distribution limit the values to physiologically plausible ranges. In future, such models, can be empirically derived distributions, or AI approaches, to model vital signs distributions, which can lead to a higher accuracy and realistic distributions.

In the AnyLogic models, real-time data is generated by producing random numbers that follow known distributions. For instance, values for bio-vital parameters, such as “heart rate,” are randomly selected from a defined healthy range at the start of the simulation, for example, ‘‘HeartRate=uniform(LHR,UHR)," using the ‘‘uniform’’ distribution between lower and upper heart rate. During the simulation, these values may fluctuate, with the size of these fluctuations determined by the ‘‘triangular’’ distribution, adding a realistic measure of variability to the simulated data. In each simulation interval, the real-time values undergo a change.^89,90 Additionally, the model interacts with external data sources gathered in a file to obtain and modify parameters, demonstrating its flexibility and adaptability. External data sources, could be medical-certified devices, such as smartwatches, ECG machines, blood pressure machines, and weight machines. The here recorded data can be transferred to the digital twin or hospital information systems, to provide clinical data sources for the simulation environment. Especially, long-term data transmissions, through, for example, heartrate parameter or a long-term ECG, can significantly show the health-benefits, during the simulation. In real-world applications, it will interact with hospital information systems and other DTs to create a comprehensive simulation environment. This model can also simulate exceptional conditions such as heart disease or increased heart rate due to physical activity. For instance, it can replicate scenarios where the heart rate exceeds normal levels during exercise or drops sharply in the case of a cardiac event. This capability ensures that the model can realistically represent a wide range of physiological states and respond to various health conditions, enhancing its utility in both everyday health monitoring and critical care simulations.

States & Transitions

Step2_Collect_Data—Synthesis and data modeling

“Step2_Collect_Data” initiates the generation of synthetic (generated) biomarker data, which are used in the model simulation to achieve a realistic representation of health information. The code line ‘‘HeartRate += triangular(-HR_step, 0, HR_step)’’ (see Figure 4), varies the heart rate within a triangular distribution range, corresponding to the variability of real biometric data. Triangular distributions may be used “as a model for a source of randomness when no system data is available”, ⁸³ thus supporting the creation of random data for our simulation. This state serves as a proxy for various data sources from health records and sensor data.

OPEN IN VIEWER

Figure 4.

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

State transitions and management in the context of the DT TechWear Biomarker Data Flow model

After explaining the different states, this paragraph focuses on the state transitions within the DT model:

Transition3: –

transition3: Activated when the critical thresholds defined in “Step 5: Vary Parameters” (see Figure 3) are exceeded, initiated by data from “excelFile,” requiring immediate medical consultation or intervention.

OPEN IN VIEWER

Figure 5.

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

OPEN IN VIEWER

Figure 6.

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

Transition conditions

Transitions are based on algorithmic checks of biomarker data. For example, “transition12” responds to parameters (see Figure 5) such as HeartRate that exceed or fall below specified critical thresholds ‘‘HeartRate < LHR1 || HeartRate > UHR1," initiating corresponding notification actions.

Data storage and visualization

In the model, real-time visualization of biometric data occurs under the agent “Patient.” The X-axis serves as the time axis ‘‘time()’’ and the Y-axis for measurement values like ‘‘Value: HeartRate’’ for ‘‘_plot_expression0_dataSet_xjal’’ (see Figure 7), supported by various graphical elements. This facilitates pattern recognition and supports the analysis of intervention impacts as well as the assessment of therapy effects. Figure 7 shows an exemplary visualization of various vital sign parameters of a patient (heart rate, respiratory rate, temperature, etc.) over time (here in seconds). Such a plot supports using various tools for the analysis of irregularities, peaks, interventions, or the assessment of therapy effects, such as pattern recognition or other ML methods.

OPEN IN VIEWER

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).

In the future, the DT TechWear Biomarker Data Flow model can be enhanced by testing the model with real-time data, and evaluating its response to the various data and data types of medically certified devices. Such real-time real data testing, can increase the effectiveness of the model.

Example scenarios for model application

The following scenarios illustrate the application of the “BMIS-DT model”:

Early warning system: The model may preemptively recognize the risk of cardiovascular disease in a patient by analyzing changes in heart rate and other cardiovascular biomarkers. Based on these data, an early medical consultation is recommended.

Wearables play a central role in the BMIS-DT model by enabling continuous monitoring. Individuals are divided into two main groups, monitored (“is_Monitored”) and unmonitored (“is_Unmonitored”). This directly affects the intensity of monitoring, early detection, and the system’s responsiveness (see Figure 8). Detailed monitoring of individual health data allows the model to provide personalized healthcare and medical interventions.

Monitored individuals (with wearables): Allow for continuous, real-time biomarker monitoring and a DT, enabling immediate detection of deviations and automatic or rapid medical intervention methods for critical changes. Based on the monitored data, preventive measures are possible.

Functions

Key functions such as “set_InitialBiomarkers,” “set_Biomarkers,” and “set_Severity” simulate the initialization, daily updating, and assessment of the severity of medical conditions based on the biomarker values. Transitions control the switches between states, for example, from “Is_Unmonitored” to “Is_Monitored” (see Figure 8), depending on wearable usage and health condition, corresponding to altered biomarker severity values. For example, a case study might illustrate the application of the model to a patient experiencing sudden tachycardia, where the “set_Severity” function increases the severity level and triggers an urgent emergency intervention via the “set_Medical_Assistance” function. Table 1 summarizes the functions of the BMIS-DT model.

OPEN IN VIEWER

Table 1.

BMIS-DT model functions.

Function	Usage
Initializing of biomarkers	The function set_Initial_Biomarkers generates initial biomarker values with the help of a uniform function, ensuring a statistical even distribution within predefined limits. Example: heartRate = uniform(main.heartRate_lower, main.heartRate_upper). This uniform distribution ensures that every possible value within the clinical spectrum occurs with equal probability, creating a comprehensive coverage and representative database for the model.
Updating biomarkers	The function set_Biomarkers supports the simulation of daily fluctuations of biomarker values through random variability within the set interval. Example: heartRate +=uniform(-main.heartRate_step, main.heartRate_step);. These variations represent natural physiological changes and potential measurement errors. Next to heartRate, other biomarkers are glucose, bloodPressure, pulseOxi, respiratoryRate, temp for sugar level, blood pressure, pulse, respiration and temperature.
Severity evaluation	If a biomarker exceeds the set critical thresholds, the function set_Severity increases the severity value. Example: if( heartRate < (1 - main.heartRate_critical) * main.heartRate_lower || heartRate > (1 + main.heartRate_critical) * main.heartRate_upper) severity++;. Severity assessment follows an algorithm based on thresholds derived from clinical guidelines and patient data, where exceeding these limits triggers an escalation in the treatment process to align the model’s reactions with actual health states.
Determination of frequency of support	The function set_Natural_Assistance_Freq sets the frequency at which patients naturally receive medical assistance, which is based on the severity and is calculated using the equation natural_Assistance_Freq = main.medicaAssistance_naturalFreq * (severity * main.medicaAssistance_naturalSeverityImpact); This formula allows the intervention frequency to be dynamically adjusted to the individual’s health condition. Another variable set of this function is assistance_Received.
Medical interventions	The function set_Medical_Assistance simulates medical interventions to correct a patient’s health when biomarker anomalies are outside the acceptable range. Adjustments are made through defined steps, as in if(heartRate < main.heartRate_lower) heartRate += main.medicaAssistance_step; for low values and vice versa for high values. These steps are calibrated to represent the variety of real treatment options.
Biomarker monitoring	The function monitor_Biomarkers simulates regular checking of biomarkers and adjusting the severity according to set tolerance intervals. Example: if (heartRate <= (1 - main.heartRate_threshold) * main.heartRate_lower || heartRate > (1 + main.heartRate_threshold) * main.heartRate_upper) severity++;. Monitoring intervals can be effectively, flexibly adjusted to patient needs from hourly to daily checks.

State transitions

State transitions are based on well-thought-out decision trees fed by multidimensional data analyses. They are essential for the dynamic adaptability of the model to changing health data.

State transitions of the BMIS-DT model (see Figure 8):

transition1: Models the transition to medical support, controlled by the variable

‘‘naturalAssistanceFreq.’’ Overall, this transition describes the process in which a patient, who is in an ‘‘is_Unassisted," ‘‘is_Unmonitored’’ state, receives medical assistance and then transitions to the ‘‘is_Assisted’’ state.

Analysis of the in-house development of the BMIS-DT model

The BMIS-DT model underscores the transformative role of DTs and wearable technologies in health monitoring and intervention. It demonstrates how continuous and differentiated monitoring through wearables enables individual and precise medical actions based on the simulation and analysis of biomarker data. By doing so, it tackles the challenges associated with dynamic health-state modeling and the necessity for adaptive intervention strategies. The development and analysis of the BMIS-DT model highlight its potential to enhance patient care through advanced technologies and emphasize the importance of proactive health monitoring for early detection and treatment of critical conditions. Future improvements may include the integration of advanced biomarkers and the application of ML to increase the accuracy of predictions, further promoting PM.

Extension and optimization of the model for wearable-supported biomarker monitoring

In the ongoing development and enhancement of systems like the BMIS-DT model, it is prudent to expand the discussion to the optimization of underlying models, especially in terms of patient data simulation. Using such data is not only a valuable resource to demonstrate the model’s functionality and efficacy, but it also opens the possibility of significantly broadening its application scope. Through this methodological expansion allows for comprehensive validation of the system’s functionality, enabling more precise and wide-ranging health monitoring and interventions by integrating additional dimensions and parameters. These approaches promise improved diagnostics and therapy support by enabling more detailed and individually tailored data collection and analysis.

The following paragraphs summarize further model enhancement opportunities:

Algorithmic improvements: The BMIS-DT model can and should utilize advanced algorithmic approaches, including ML and generative AI, to extract meaningful patterns from a large volume of patient data. Decision trees could be designed to reflect the complexity of medical data and incorporate adaptive algorithms that evolve with the accumulation of new data, thus promoting continuous refinement of the decision-making process.

Clinical relevance: In the clinical context, the model can be enhanced to enable stratified risk assessment, predicting the likelihood of cardiovascular events in patients with hypertension. It can serve as a decision-support tool for physicians to plan and prioritize preventive measures. The model’s risk assessment strategy needs to be directly integrated into clinical workflows, enabling specific condition action based on a stratified analysis of patient data, prioritizing preventive measures for high-risk patients.

Integration with existing health systems: Discussing how the model can interact with existing electronic health records and management systems will add additional relevance. An interface architecture that uses standards like HL7, ⁹⁸ FHIR ⁹⁹ or Asset Administration Shell (AAS) ¹⁰⁰ supports the integration of the model into existing health information systems, enabling dynamic real-time data exchange and decision-making.

Biomarker specifications: The selection of biomarkers, such as heart rate and blood sugar, is based on their established role in the early detection of disease states. The sensitivity and specificity of each biomarker should be confirmed through extensive clinical studies. The biomarker selection criteria within the model are rigorous, based on their evidence-based role in the pathogenesis of diseases. Further validation through longitudinal studies assessing sensitivity and specificity in a clinical context will strengthen the model’s prognostic reliability.

Data validation: To ensure the accuracy and robustness of predictive models, patient data should undergo validation, including redundancy checks, error corrections, cross-validation, bootstrapping methods, and alignment with current clinical guidelines.

Scalability and future-proofing: Given the exponential growth of medical data, a section on the need for scalability of the model and future development paths would be enlightening. The model should be designed to keep pace with the rapid increase in medical data, pursuing solutions for processing big data to ensure long-term applicability.

Importance of data protection measures: Implementing data protection measures is critical. An in-depth consideration of ethical and legal aspects will provide added value here.