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A Conceptual Framework for Digital Twin in Healthcare: Evidence from a Systematic Meta-Review

Author

Listed:
  • Giulia Pellegrino

    (University of Salento)

  • Massimiliano Gervasi
  • Mario Angelelli

    (University of Salento)

  • Angelo Corallo

    (University of Salento)

Abstract

Digital Twin (DT) technology monitors, simulates, optimizes, models, and predicts the behavior of physical entities. Healthcare is a significant domain where a DT can be functional for multiple purposes. However, these diverse uses of DTs need a clear understanding of both general and specific aspects that can affect their adoption and integration. This paper is a meta-review that leads to the development of a conceptual framework designed to support the high-level evaluation of DTs in healthcare. Using the PRISMA methodology, the meta-review synthesizes insights from 20 selected reviews out of 1,075 studies. Based on this comprehensive analysis, we extract the functional, technological, and operational aspects that characterize DTs in healthcare. Additionally, we examine the structural (e.g., hierarchical) relationships among these aspects to address the various complexity scales in digital health. The resulting framework can promote the effective design and implementation of DTs, offering a structured approach for their assessment.

Suggested Citation

  • Giulia Pellegrino & Massimiliano Gervasi & Mario Angelelli & Angelo Corallo, 2025. "A Conceptual Framework for Digital Twin in Healthcare: Evidence from a Systematic Meta-Review," Information Systems Frontiers, Springer, vol. 27(1), pages 7-32, February.
    • Handle: RePEc:spr:infosf:v:27:y:2025:i:1:d:10.1007_s10796-024-10536-4
    DOI: 10.1007/s10796-024-10536-4
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    References listed on IDEAS

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    Cited by:

    1. Tomas Krilavičius & Lucio Tommaso De Paolis & Valerio De Luca & Josef Spjut, 2025. "eXtended Reality and Artificial Intelligence in Medicine and Rehabilitation," Information Systems Frontiers, Springer, vol. 27(1), pages 1-6, February.

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