Optimizing healthcare big data performance through regional computing
Abstract The healthcare sector is experiencing a digital transformation propelled by the Internet of Medical Things (IOMT), real-time patient monitoring, robotic surgery, Electronic Health Records (EHR), medical imaging, and wearable technologies. This proliferation of digital tools generates vast q...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87515-5 |
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| author | Tariq Alsahfi Afzal Badshah Omar Ibrahim Aboulola Ali Daud |
| author_facet | Tariq Alsahfi Afzal Badshah Omar Ibrahim Aboulola Ali Daud |
| author_sort | Tariq Alsahfi |
| collection | DOAJ |
| description | Abstract The healthcare sector is experiencing a digital transformation propelled by the Internet of Medical Things (IOMT), real-time patient monitoring, robotic surgery, Electronic Health Records (EHR), medical imaging, and wearable technologies. This proliferation of digital tools generates vast quantities of healthcare data. Efficient and timely analysis of this data is critical for enhancing patient outcomes and optimizing care delivery. Real-time processing of Healthcare Big Data (HBD) offers significant potential for improved diagnostics, continuous monitoring, and effective surgical interventions. However, conventional cloud-based processing systems face challenges due to the sheer volume and time-sensitive nature of this data. The migration of large datasets to centralized cloud infrastructures often results in latency, which impedes real-time applications. Furthermore, network congestion exacerbates these challenges, delaying access to vital insights necessary for informed decision-making. Such limitations hinder healthcare professionals from fully leveraging the capabilities of emerging technologies and big data analytics. To mitigate these issues, this paper proposes a Regional Computing (RC) paradigm for the management of HBD. The RC framework establishes strategically positioned regional servers capable of regionally collecting, processing, and storing medical data, thereby reducing dependence on centralized cloud resources, especially during peak usage periods. This innovative approach effectively addresses the constraints of traditional cloud processing, facilitating real-time data analysis at the regional level. Ultimately, it empowers healthcare providers with the timely information required to deliver data-driven, personalized care and optimize treatment strategies. |
| format | Article |
| id | doaj-art-3141c65f0da245a0860cad8eb4f25cb2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3141c65f0da245a0860cad8eb4f25cb22025-01-26T12:26:54ZengNature PortfolioScientific Reports2045-23222025-01-0115111910.1038/s41598-025-87515-5Optimizing healthcare big data performance through regional computingTariq Alsahfi0Afzal Badshah1Omar Ibrahim Aboulola2Ali Daud3Department of Information Systems and Technology, University of JeddahDepartment of Software Engineering, University of SargodhaDepartment of Information Systems and Technology, University of JeddahFaculty of Resilience, Rabdan AcademyAbstract The healthcare sector is experiencing a digital transformation propelled by the Internet of Medical Things (IOMT), real-time patient monitoring, robotic surgery, Electronic Health Records (EHR), medical imaging, and wearable technologies. This proliferation of digital tools generates vast quantities of healthcare data. Efficient and timely analysis of this data is critical for enhancing patient outcomes and optimizing care delivery. Real-time processing of Healthcare Big Data (HBD) offers significant potential for improved diagnostics, continuous monitoring, and effective surgical interventions. However, conventional cloud-based processing systems face challenges due to the sheer volume and time-sensitive nature of this data. The migration of large datasets to centralized cloud infrastructures often results in latency, which impedes real-time applications. Furthermore, network congestion exacerbates these challenges, delaying access to vital insights necessary for informed decision-making. Such limitations hinder healthcare professionals from fully leveraging the capabilities of emerging technologies and big data analytics. To mitigate these issues, this paper proposes a Regional Computing (RC) paradigm for the management of HBD. The RC framework establishes strategically positioned regional servers capable of regionally collecting, processing, and storing medical data, thereby reducing dependence on centralized cloud resources, especially during peak usage periods. This innovative approach effectively addresses the constraints of traditional cloud processing, facilitating real-time data analysis at the regional level. Ultimately, it empowers healthcare providers with the timely information required to deliver data-driven, personalized care and optimize treatment strategies.https://doi.org/10.1038/s41598-025-87515-5HealthcareHealthcare big dataInternet Of Medical Things (IoMT)Regional computing |
| spellingShingle | Tariq Alsahfi Afzal Badshah Omar Ibrahim Aboulola Ali Daud Optimizing healthcare big data performance through regional computing Scientific Reports Healthcare Healthcare big data Internet Of Medical Things (IoMT) Regional computing |
| title | Optimizing healthcare big data performance through regional computing |
| title_full | Optimizing healthcare big data performance through regional computing |
| title_fullStr | Optimizing healthcare big data performance through regional computing |
| title_full_unstemmed | Optimizing healthcare big data performance through regional computing |
| title_short | Optimizing healthcare big data performance through regional computing |
| title_sort | optimizing healthcare big data performance through regional computing |
| topic | Healthcare Healthcare big data Internet Of Medical Things (IoMT) Regional computing |
| url | https://doi.org/10.1038/s41598-025-87515-5 |
| work_keys_str_mv | AT tariqalsahfi optimizinghealthcarebigdataperformancethroughregionalcomputing AT afzalbadshah optimizinghealthcarebigdataperformancethroughregionalcomputing AT omaribrahimaboulola optimizinghealthcarebigdataperformancethroughregionalcomputing AT alidaud optimizinghealthcarebigdataperformancethroughregionalcomputing |