Distributed healthcare systems: Challenges, architecture, and future directions

The rapid evolution of healthcare information technology has led to the emergence of three transformative technologies reshaping modern healthcare delivery: edge computing, artificial intelligence, and the Internet of Medical Things. Edge computing brings processing capabilities closer to healthcare data sources, enabling reduced latency, enhanced privacy protection, improved resilience during network disruptions, and optimized bandwidth usage for data-intensive applications. Artificial intelligence and machine learning applications span clinical decision support, predictive analytics, medical imaging analysis, and natural language processing, fundamentally altering diagnostic capabilities and treatment planning. Meanwhile, the Internet of Medical Things creates interconnected ecosystems of wearable sensors, implantable devices, and smart hospital equipment that enable continuous patient monitoring, create intelligent clinical environments, support remote disease management, and automate clinical documentation workflows. Together, these technologies are establishing new paradigms for distributed healthcare systems that enhance both clinical outcomes and operational efficiency.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-30
DOI
https://doi.org/10.5281/zenodo.17225502
Primary Topic
Data Analysis with R
Type
article
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article

Distributed healthcare systems: Challenges, architecture, and future directions

Zenodo (CERN European Organization for Nuclear Research)
Data Analysis with R
article

Distributed healthcare systems: Challenges, architecture, and future directions

article en

Abstract

The rapid evolution of healthcare information technology has led to the emergence of three transformative technologies reshaping modern healthcare delivery: edge computing, artificial intelligence, and the Internet of Medical Things. Edge computing brings processing capabilities closer to healthcare data sources, enabling reduced latency, enhanced privacy protection, improved resilience during network disruptions, and optimized bandwidth usage for data-intensive applications. Artificial intelligence and machine learning applications span clinical decision support, predictive analytics, medical imaging analysis, and natural language processing, fundamentally altering diagnostic capabilities and treatment planning. Meanwhile, the Internet of Medical Things creates interconnected ecosystems of wearable sensors, implantable devices, and smart hospital equipment that enable continuous patient monitoring, create intelligent clinical environments, support remote disease management, and automate clinical documentation workflows. Together, these technologies are establishing new paradigms for distributed healthcare systems that enhance both clinical outcomes and operational efficiency.

Zenodo (CERN European Organization for Nuclear Research)
Clinical Research Solutions (US)
Openalex Percentile: Top 25%
Data Analysis with R
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Distributed healthcare systems: Challenges, architecture, and future directions · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS