Version 1
: Received: 14 September 2023 / Approved: 15 September 2023 / Online: 18 September 2023 (05:27:38 CEST)
How to cite:
Morande, S. From Fragmented to Integrated: Transforming Emergency Healthcare Delivery through Digital Twin Technology. Preprints2023, 2023091059. https://doi.org/10.20944/preprints202309.1059.v1
Morande, S. From Fragmented to Integrated: Transforming Emergency Healthcare Delivery through Digital Twin Technology. Preprints 2023, 2023091059. https://doi.org/10.20944/preprints202309.1059.v1
Morande, S. From Fragmented to Integrated: Transforming Emergency Healthcare Delivery through Digital Twin Technology. Preprints2023, 2023091059. https://doi.org/10.20944/preprints202309.1059.v1
APA Style
Morande, S. (2023). From Fragmented to Integrated: Transforming Emergency Healthcare Delivery through Digital Twin Technology. Preprints. https://doi.org/10.20944/preprints202309.1059.v1
Chicago/Turabian Style
Morande, S. 2023 "From Fragmented to Integrated: Transforming Emergency Healthcare Delivery through Digital Twin Technology" Preprints. https://doi.org/10.20944/preprints202309.1059.v1
Abstract
The prevalence of chronic diseases is dramatically increasing demand for emergency healthcare. Existing systems rely on patients self-identifying symptoms, causing dangerous delays. This study develops an AI and IoT-powered “digital twin” solution to enable continuous real-time monitoring and timely prediction of diverse medical emergencies. A digital twin is a virtual representation of an individual, modeled using multidimensional physiological data from wearable sensors. Machine learning techniques analyze patterns in this data to identify anomalies and predict emergencies like heart attacks or falls. A key contribution is an optimized ensemble algorithm combining gradient boosted trees, neural networks, and other techniques to accurately detect emergency events. Evaluation on a dataset of 9158 samples shows the digital twin identifies key emergencies with over 90% recall, enabling prevention and rapid response. It allows risk stratification and personalized interventions based on early warnings, circumventing over 2 million avoidable emergency room visits annually. This study demonstrates the feasibility of an integrated, predictive, patient-centric emergency response system enabled by digital twin technology.
Keywords
digital twin; emergency healthcare; wearable devices; machine learning; predictive analytics
Subject
Business, Economics and Management, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
