## 1 Introduction  
Data are consistently generated throughout the healthcare system through various business activities and service provisions. These data are vital for clinical, operational, and individual decision-making [1]. However, healthcare data often remain inaccessible, lack standardization across different systems, and are difficult to understand, utilize, and share. While most healthcare organizations recognize the value of these data, many have not treated it as a crucial, tangible asset. Effective management of these data can empower healthcare systems to construct comprehensive patient profiles, elevate the quality of care and treatments, enhance communication, and ultimately improve health outcomes [2]. Efforts to enhance health data interoperability and standardization are ongoing, with technologies such as artificial intelligence (AI) and blockchain playing pivotal roles in addressing these issues. Recent studies have shown that AI can significantly improve the analysis and interpretation of health data, leading to more precise diagnoses, tailored treatment plans, and efficient resource allocation [3]. Blockchain technology is also seeing increased adoption in healthcare for secure data sharing, traceability, and accessibility. This decentralized data management approach fosters transparency, minimizes the risk of data breaches, and aids in standardizing health data across multiple platforms [4]. Additionally, natural language processing (NLP), a subset of AI, is gaining attention for its ability to extract meaningful insights from unstructured health records, enhance data organization, and improve communication among various healthcare systems [5]. The healthcare industry is one of the fastest-evolving sectors in the world, driven by a combination of technological advancements, shifting patient expectations, and regulatory reforms. The sector faces numerous challenges, including escalating costs, the transition to value-based care delivery models, payment reforms, data security concerns, and the integration of smart healthcare technologies [6]. Healthcare organizations have been rapidly accelerating their digital transformations in response to these challenges. According to a 2021 survey of healthcare executives, 81% indicated that their organization's digital transformation is gaining speed, while 93% reported that their organizations are focusing on innovation with greater urgency this year [7]. Central to this transformation is the effective utilization of both internal and external data. Consequently, healthcare organizations are increasingly recognizing data as a valuable asset and are willing to invest in strategies to leverage them for accelerated transformation. Digital Twin (DT) technology is one emerging concept that healthcare organizations are exploring. This technology digitally models physical environments, potentially providing real-time analytics that can aid in data-driven decision-making, risk assessment, and operational improvements. As healthcare faces unprecedented challenges, DT technology can play a critical role in centering patient care within these complex systems. The potential of DT technology is garnering interest from both industry practitioners and academic researchers. Currently used in various sectors to create accurate digital replicas of objects and simulate operational processes, DT technology is projected to gain even more traction. Gartner predicts that by 2027, over 40% of large global companies will implement DT technology in projects aimed at increasing revenue [8]. Specifically, within healthcare, Gartner forecasts that by 2025, a quarter of healthcare delivery organizations will incorporate DT initiatives into their broader digital transformation strategies [8]. Additionally, a survey by Accenture revealed that in 2021, 25% of healthcare executives reported their organizations were already experimenting with DT technology.