Digital Twin: Twinning Healthcare for a Brighter Future
- hiranmaydash
- May 14, 2023
- 6 min read
Updated: May 29, 2023
Introduction
The Covid-19 pandemic has not only caused global panic but has also significantly disrupted the healthcare industry. However, amidst these challenges, there is a ray of hope in the form of digital solutions. One such groundbreaking solution is the concept of a digital twin, which has the potential to reshape the healthcare industry.
For instance, the market potential for digital twin technology in only medical imaging devices is significant. According to a report by Markets and Markets, the global digital twin market is projected to grow from USD 3.1 billion in 2020 to USD 48.2 billion by 2026, at a compound annual growth rate (CAGR) of 58.9% during the forecast period. The increasing adoption of Internet of Things (IoT) technology, advances in artificial intelligence (AI), and the need for predictive maintenance are some of the key factors driving the growth of the digital twin market.
In this blog post, we will explore what digital twin is, their importance in healthcare, the different types of digital twins, and how they can be established.
What is a digital twin?
A digital twin refers to a virtual replica of a physical product, process, or system. It is a digital representation that closely mimics the characteristics and behavior of its physical counterpart. In the healthcare field, digital twins have found applications ranging from monitoring devices and predictive maintenance to complex models of human organs.
Importance of digital twin in healthcare
Digital twins offer numerous benefits in the healthcare sector. They facilitate cost optimization, improve operational efficiency, reduce downtime, and even forecast future demands. By leveraging digital twins, healthcare organizations can enable remote diagnostics, accelerate precision medicine, expedite clinical trials, enhance home healthcare, and provide superior patient care.
There are many potential benefits to using digital twin technology in healthcare. For example, digital twins can be used to:
· Improve diagnosis and treatment: By creating a virtual model of a patient's body, doctors can get a better understanding of their condition and make more informed decisions about treatment.
· Personalize medicine: Digital twins can be used to create personalized treatment plans that are tailored to each patient's individual needs.
· Reduce costs: Digital twins can be used to identify potential problems early on, preventing them from developing into costly complications.
· Improve patient outcomes: By providing doctors with a better understanding of their patient's conditions and by personalizing treatment plans, digital twins can help to improve patient outcomes.
Different types of digital twins for the healthcare system
Product twin: This type of digital twin replicates a physical product, such as a medical imaging device. It enables predictive maintenance, minimizes system failures, and ensures the availability of critical equipment when needed. It also helps in accelerating the new product launch by minimizing the design iteration and improving the 1st time right.
A digital twin of a device consists of four components: a digital representation of the device, data from that device, AI, and data analytics to identify relevant patterns, and an interface for human interaction. This can help predict when a device needs maintenance and prevent system failures (Philips Healthcare).
A simple process flow for a device twin may look like this -
Process twin: Process digital twins simulate and evaluate clinical procedures and healthcare systems. They help optimize healthcare delivery, identify areas for improvement, and enhance overall system performance. For instance, virtual twins of hospitals can be created to optimize operational strategies, staffing, and care models.
Siemens is using digital twins to improve the efficiency of its hospitals. The company has created digital twins for its hospitals in Dublin, Ireland. These digital twins are used to track the performance of the hospitals, identify potential problems, and improve the efficiency of care delivery.
Clinical twin: A digital twin of a patient integrates various measurements and data over time to create a virtual model of a body part or an entire physiological system. These personalized models can aid in diagnosis, treatment planning, and targeted therapy delivery.
Philips is using digital twins to improve the care of heart patients. The company has created a digital twin of the heart that can be used to simulate different scenarios and predict the risk of heart attacks.
Establishing a Digital Twin
To build and scale a digital twin, a seven-step approach is recommended:
1. Define the scope of the digital twin. What is the problem stamen of the digital twin? What data will be collected? What are the desired outcomes? ( create a blueprint for digital twin)
2. Collect data. This data can come from a variety of sources, such as sensors, historical records, and simulations.
3. Clean and prepare the data. The data needs to be cleaned and prepared before it can be used to create the digital twin. This may involve removing outliers, filling in missing values, and transforming the data into a format that can be used by the digital twin software.
4. Create the digital twin. There are a variety of software tools that can be used to create digital twins. The software will need to be able to collect and process data, create simulations, and visualize the results.
5. Validate the digital twin. The digital twin needs to be validated to ensure that it is accurate and reliable. This can be done by comparing the results of the digital twin to real-world data.
6. Use the digital twin. The digital twin can be used to improve the performance of a product or process. For example, it can be used to identify potential problems, optimize production, and predict maintenance needs.
7. Continuous improvement. The digital twin evolves by adding more data layers, analytics, and incorporating AI and advanced modeling techniques. This enables simulations, prescriptions, and expanded functionalities.
Challenges of using digital twin
Artificial Intelligence (AI) and Machine Learning (ML) play a crucial role in the success of digital twin implementation. These technologies enhance data analysis, and pattern recognition, and enable real-time monitoring. AI and ML algorithms help identify trends, predict maintenance needs, optimize processes, and improve decision-making based on the digital twin's insights. However, this adds additional challenges to implementing the digital twin
1. Data privacy: Digital twins require a large amount of data. Digital twins will contain sensitive patient data, and this data needs to be protected from unauthorized access. This is a major concern for healthcare organizations, and they will need to take steps to ensure that digital twins are secure.
2. Data quality: The data used to create digital twins needs to be accurate and reliable.
3. Integration: Digital twins need to be integrated with existing healthcare systems. This can be a complex and challenging process.
4. Architecture and integration with existing processes and systems: The challenge lies in integrating digital twin architecture with existing processes and systems, ensuring seamless compatibility and effective communication among different systems for enhanced operational functionality.
5. Results validation and accuracy of reusable for prediction at different boundary conditions: Digital twin models are validated to ensure that they are accurate and reliable. This is done by comparing the results of the model to known experimental data or by using statistical methods to assess the uncertainty of the model predictions. Once a digital twin model has been validated, it can be reused to make predictions at different boundary conditions. The accuracy of the predictions will depend on the accuracy of the model and the boundary condition.
6. Regulation like FDA: The FDA approval process for digital twin applications in healthcare is still evolving. The FDA has not yet issued any specific guidance on how to approve digital twins, but it has said that it will consider them on a case-by-case basis. The FDA has also started the medical device development tools (MDDT) program to prequalify AI models and digital health tools, with the hopes of expediting the approval process through standardized documentation protocols.
Despite these challenges, digital twins have the potential to transform healthcare. They have the potential to improve patient care, reduce costs, and improve outcomes. As technology continues to develop, we can expect to see even more innovative applications of digital twins in healthcare.
The next step
As digital twin technology continues to advance, its potential in healthcare remains vast. Further research and development efforts should focus on addressing challenges such as cybersecurity risks, integration with existing systems, regulation, and the capability of digital models for accurate predictions. Collaborations between healthcare organizations, technology providers, and regulatory bodies are crucial to maximize the benefits of digital twin adoption in healthcare.
Conclusion
The emergence of digital twin technology has opened new possibilities for revolutionizing healthcare. From optimizing patient care and streamlining processes to improving precision medicine and clinical outcomes, digital twins have the potential to reshape the healthcare landscape. By leveraging AI, ML, and advanced analytics.
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