2025-05-05 バージニア工科大学 (VirginiaTech)
<関連情報>
- https://news.vt.edu/articles/2025/04/digital-twins-hcprovider-burnout.html
- https://ieeexplore.ieee.org/document/10962223
- https://ieeexplore.ieee.org/document/10379674
- https://ieeexplore.ieee.org/document/10365449
デジタルツインは医療システムの経済的、環境的、社会的持続可能性をどのようにサポートできるか: トリプルボトムラインに焦点を当てた系統的レビュー How Can Digital Twins Support the Economic, Environmental, and Social Sustainability of Healthcare Systems: A Systematic Review Focused on the Triple Bottom Line
Md Doulotuzzaman Xames; Taylan G. Topcu
IEEE Access Published:10 April 2025
DOI:https://doi.org/10.1109/ACCESS.2025.3559502
Abstract
Digital twins (DTs) are transforming healthcare systems (HSs) by enabling real-time, data-driven decision-making. Despite their potential, research on DTs’ role in long-term HS sustainability remains nascent. This study systematically reviews DT use cases in HSs through the lens of the triple-bottom-line framework of sustainability, identifying their economic, environmental, and social contributions. Additionally, it maps these use cases to the United Nations (UN) Sustainable Development Goals (SDGs) to assess their alignment with global sustainability policies. A systematic literature review following the PRISMA framework was conducted across four databases (Scopus, Web of Science, Engineering Village, and PubMed), identifying 81 peer-reviewed studies. DT use cases were categorized into sustainability dimensions and qualitatively mapped to UN SDGs. We identify 28 unique DT use cases supporting HS sustainability – 13 contributing to economic (e.g., precision medicine, early diagnosis), 8 to environmental (e.g., energy-efficient hospital operations, waste management), and 7 to social sustainability (e.g., provider burnout prevention, equitable access). Our mapping reveals that DTs could support 11 of 17 UN SDGs, including SDG 3 (good health and well-being), SDG 8 (economic growth), SDG 9 (innovation), and SDGs 12–15 (environmental impact mitigation), among others. This study documents the significant potential of DTs to enhance HS sustainability across economic, environmental, and social dimensions while supporting multiple SDGs. However, most existing DT studies overlook explicit sustainability linkages, with limited attention to assessing or prioritizing DTs’ impact on HS sustainability. Future research should develop standardized sustainability metrics, conduct empirical studies, and create frameworks linking DT outcomes to SDGs.
医療システムのためのデジタルツイン研究の体系的文献レビュー: 研究動向、ギャップ、実現への課題 A Systematic Literature Review of Digital Twin Research for Healthcare Systems: Research Trends, Gaps, and Realization Challenges
Md. Doulotuzzaman Xames; Taylan G. Topcu
IEEE Access Published:03 January 2024
DOI:https://doi.org/10.1109/ACCESS.2023.3349379
Abstract
Using the PRISMA approach, we present the first systematic literature review of digital twin (DT) research in healthcare systems (HSs). This endeavor stems from the pressing need for a thorough analysis of this emerging yet fragmented research area, with the goal of consolidating knowledge to catalyze its growth. Our findings are structured around three research questions aimed at identifying: (i) current research trends, (ii) gaps, and (iii) realization challenges. Current trends indicate global interest and interdisciplinary collaborations to address complex HS challenges. However, existing research predominantly focuses on conceptualization; research on integration, verification, and implementation is nascent. Additionally, we document that a substantial body of papers mislabel their work, often disregarding modeling and twinning methods that are necessary elements of a DT. Furthermore, we provide a non-exhaustive classification of the literature based on two axes: the object (i.e., product or process) and the context (i.e., patient’s body, medical procedures, healthcare facilities, and public health). While this is a testament to the diversity of the field, it implies a specific pattern that could be reimagined. We also identify two gaps: (i) considering the human-in-the-loop nature of HSs with a focus on provider decision-making and (ii) implementation research. Lastly, we discuss two challenges for broad-scale implementation of DTs in HSs: improving virtual-to-physical connectivity and data-related issues. In conclusion, this study suggests that DT research could potentially help alleviate the acute shortcomings of HSs that are often manifested in the inability to concurrently improve the quality of care, provider wellbeing, and cost efficiency.
ヒューマン・イン・ザ・ループ・システムのためのデジタル・ツインに向けて: 医療システムにおけるワークロード管理とバーンアウト予防のためのフレームワーク Toward Digital Twins for Human-in-the-loop Systems: A Framework for Workload Management and Burnout Prevention in Healthcare Systems
Md Doulotuzzaman Xames; Taylan G. Topcu
2023 IEEE 3rd International Conference on Digital Twins and Parallel Intelligence Date Added to IEEE Xplore: 26 December 2023
DOI:https://doi.org/10.1109/DTPI59677.2023.10365449
Abstract
While digital twin (DT) research matured in the past decade, DT applications in human-in-the-loop (HITL) systems are nascent. HITL systems differ from engineered systems as they are sociotechnical systems (STSs) that rely on collaborating teams of humans for making safety-critical operational decisions. Healthcare systems (HSs), an archetypical STS, have recently attracted increased attention from DT research. Nevertheless, the current literature greatly overlooks the linkage between the system and the HITL – healthcare providers (i.e., doctors and nurses) in this case – particularly regarding the influence of work demands on HITL performance. This is problematic because provider performance plays a crucial role in the overall functionality and effectiveness of HSs. Taking providers’ interests and the long-term sustainability of HSs into account, this paper lays the foundation for a conceptual HITL workload DT for HSs. We provide an overview of the complex macroergonomic factors that influence provider workload using the systems engineering initiative for patient safety (SEIPS) framework and discuss workload quantification methods to operationalize this information. We then describe how DT modeling and twinning methods can be leveraged to collect and utilize relevant operational data for proactive management of provider workload. Finally, we discuss associated challenges such as data collection, establishment of necessary feedback mechanisms, and calibration of workload quantification methods. If successful, the proposed framework has the potential to effectively address a pressing national issue: provider burnout in HSs, while extending DT research to other STSs such as air and rail traffic control.
