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Lookup NU author(s): Faiza Yahya, Dr Matthew CooperORCiD, Professor Mohamad KassemORCiD, Professor Hamde NazarORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© Faiza Yahya, Matthew Cooper, Wahib Saif, Mohamad Kassem, Hamde Nazar. Background: Increasing demand on health care systems requires innovative, transformative solutions for efficient, high-quality care. One promising approach is digital twin (DT) technology, which leverages real-time data to create dynamic, virtual representations of a physical entity (individuals or space) to anticipate future scenarios and support care decisions. Although DTs have been explored in various sectors, their application in hospital at home (HaH), which delivers acute-level care in home environments, remains unexplored. Objective: This review bridges a critical knowledge gap, examining existing evidence on DT-enabling tools to manage patients with frailty in home settings. This will identify the underpinning architectural components required to inform an HaH-DT system supporting clinical decision-making. Methods: We searched 6 electronic databases and gray literature for primary English-language studies published between January 2019 and September 2025. Included studies reported on the monitoring or management of patients with frailty within their own home. Information was charted on a predefined data collection form to answer the research objectives. Review articles, protocols, and conference abstracts were excluded. Results: We included 69 reports: 54% (37/69) used quantitative approaches, and 36% (25/69) were pilot or feasibility studies. Reports were analyzed for DT-enabling tools and systematically mapped across the proposed 5-layered DT architecture: sensing, communication, storage, analytics, and visualization. DT layer taxonomies, interconnections, and classifications of data types collected (eg, about the patient, home environment, use of medical equipment) are presented. This evidence identifies DT-enabling tools for a variety of functions and a range of sensing technologies (eg, wearable-based passive sensing, active physiological sensors, ambient sensors detecting motion or environmental changes). The most prevalent communication modes were wireless and network-based (36/112, 32.1%), the majority (12/36, 33%) using Bluetooth. Better understanding of data management, particularly secure storage, is required within local health care systems. The emerging potential of predictive and prescriptive analytics for risk prediction, clinical decision-making, or activation of alert-triggered health interventions by clinicians was mapped. Analytics methods are currently largely descriptive. Advanced methods such as prescriptive analytics for recommendations of an optimal course of action and diagnostic analytics that highlight why a situation has occurred are lacking. DT-enabling tools demonstrate patient-centered benefits, including enhanced motivation, reassurance, and personalized care. Concerns include device accuracy, user acceptability, and implications for carers and organizational workflows. Conclusions: This review is among the first to systematically map DT-enabling tools to inform a potential HaH DT for patients with frailty, organized by a 5-layered conceptual model. Understanding these architectural layers provides the foundations for stakeholders to advance research and development in areas where there are knowledge gaps and consider how an HaH DT can effectively operate within current health care systems. Leveraging technology-enabled care in complex home-based settings provides great potential to deliver safer, personalized, timely care.
Author(s): Yahya F, Cooper M, Saif W, Kassem M, Nazar H
Publication type: Review
Publication status: Published
Journal: Journal of Medical Internet Research
Year: 2025
Volume: 27
Online publication date: 10/12/2025
Acceptance date: 14/11/2025
ISSN (electronic): 1438-8871
Publisher: JMIR Publications Inc.
URL: https://doi.org/10.2196/81510
DOI: 10.2196/81510
PubMed id: 41370790
Data Access Statement: The datasets generated or analyzed during this study are available from the corresponding author on reasonable request.
