[Digital twins: an innovative and promising option to optimize care pathways for older patients]
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View abstract on PubMed
Summary
This summary is machine-generated.Digital twins (DTs) create virtual patient replicas using real-world data for predictive healthcare. In geriatrics, DTs offer personalized care and resource optimization but require addressing technological acceptance and complexity challenges.
Area Of Science
- Digital Health
- Computational Medicine
- Geriatric Care
Background
- Healthcare is undergoing digital transformation, leading to innovations like the digital twin (DT) concept.
- Digital twins, originating from self-supervised machine learning, create virtual replicas of patients or systems using real-life data for predictive analysis.
- Initially used in industry, DTs are now gaining traction in healthcare for personalized care pathways.
Purpose Of The Study
- To explore the potential of digital twins (DTs) in geriatrics.
- To identify challenges and opportunities for implementing DTs in elder care.
- To assess the role of DTs in modeling aging and optimizing healthcare for older adults.
Main Methods
- Utilizing clinical, biological, environmental, sensor, and medico-administrative data (e.g., SNDS).
- Leveraging various digital technologies for virtual replica creation and data analysis.
- Applying the digital twin concept to model functional and clinical evolution in older adults.
Main Results
- Digital twins (DTs) show promise for personalizing care plans and optimizing healthcare resources in geriatrics.
- DTs can aid in modeling the functional and clinical progression of older adults.
- Potential applications include adapting therapeutic protocols and supporting healthy aging.
Conclusions
- Digital twins (DTs) are a promising tool for enhancing healthy aging and care pathways in geriatrics.
- Successful clinical integration and social acceptability are crucial for widespread adoption.
- Key challenges include older adults' technology acceptance, multidisciplinary team building, modeling aging complexity, and environmental impact.
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