Digital Twins-Based Data Fabric Architecture to Enhance Data Management in Intelligent Healthcare Ecosystems

Macías, Aurora; Muñoz, David Fernández; Navarro, Elena; González, Pascual

doi:10.1007/978-3-031-21333-5_4

Cited by 1 publication

(3 citation statements)

References 18 publications

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“…We found nine articles that discuss building DTs for public health with a specific emphasis on COVID-19. The applications range from pandemic alerting [204] and predicting the virus spread [145], [155], [205] to monitoring social distancing [148] and studying the effectiveness of intervention measures [146]. A blockchain-based DT was developed for pandemic decisions e.g., alerts, quarantine, lockdown, social distancing, etc.…”

Section: B: Process Twinningmentioning

confidence: 99%

“…The SEIRS model was adapted to account for spatial variation, and whenever possible, the model parameters were derived from official COVID-19 transmission data from the UK. Very recently, a prototype DT was developed as part of an innovative smart healthcare ecosystem based on IoT to prevent the spread of the virus in a real nursing home [205]. A summary of our classification of the literature based on the object and context taxonomy with a full breakdown of articles is provided in Table V.…”

Section: B: Process Twinningmentioning

confidence: 99%

“…Additionally, a few other studies have delved into the application of Microsoft Azure in constructing DTs for various contexts. These include the creation of DTs for elderly care homes [114], [205] and the development of patient-specific DTs [191]. Other similar platforms such as Predix by General Electric, AWS IoT TwinMaker by Amazon, ThingWorx by PTC, Eclipse Ditto™ by Eclipse Foundation, and Digital Enterprise Suite by Siemens which have already found successful applications in the mechanical and manufacturing sectors [243] could be explored in the healthcare DT context.…”

Section: ) Implementation Researchmentioning

confidence: 99%

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A Systematic Literature Review of Digital Twin Research for Healthcare Systems: Research Trends, Gaps, and Realization Challenges

Xames,

Topcu

2024

IEEE Access

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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.

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