AI-driven data analysis in healthcare: Transforming patient care and operational efficiency

The integration of artificial intelligence in healthcare has revolutionized data analysis approaches, and the article fundamentally transforms patient care delivery and operational efficiency. It examines how AI technologies enable healthcare organizations to effectively process vast quantities of diverse clinical data, including electronic health records, medical imaging, genomic information, and monitoring device outputs. Key applications explored include clinical decision support systems that enhance predictive capabilities, advanced diagnostic tools that augment medical imaging analysis, and operational enhancements that streamline administrative functions. It also addresses significant implementation challenges, including data privacy concerns, algorithmic bias considerations, integration with legacy systems, and ethical dimensions of AI deployment in clinical settings. Despite these obstacles, AI-driven healthcare analytics demonstrate remarkable potential to improve diagnostic accuracy, personalize treatment approaches, optimize resource allocation, and reduce administrative burden—ultimately enhancing both clinical outcomes and healthcare accessibility.

Authors

Publication Details

Journal
World Journal of Advanced Engineering Technology and Sciences
Published
2025-04-08
DOI
https://doi.org/10.30574/wjaets.2025.15.1.0221
Primary Topic
Healthcare Systems and Public Health
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-driven data analysis in healthcare: Transforming patient care and operational efficiency

Meghana Bhimavarapu
World Journal of Advanced Engineering Technology and Sciences
Healthcare Systems and Public Health
article

AI-driven data analysis in healthcare: Transforming patient care and operational efficiency

Meghana Bhimavarapu
article en

Abstract

The integration of artificial intelligence in healthcare has revolutionized data analysis approaches, and the article fundamentally transforms patient care delivery and operational efficiency. It examines how AI technologies enable healthcare organizations to effectively process vast quantities of diverse clinical data, including electronic health records, medical imaging, genomic information, and monitoring device outputs. Key applications explored include clinical decision support systems that enhance predictive capabilities, advanced diagnostic tools that augment medical imaging analysis, and operational enhancements that streamline administrative functions. It also addresses significant implementation challenges, including data privacy concerns, algorithmic bias considerations, integration with legacy systems, and ethical dimensions of AI deployment in clinical settings. Despite these obstacles, AI-driven healthcare analytics demonstrate remarkable potential to improve diagnostic accuracy, personalize treatment approaches, optimize resource allocation, and reduce administrative burden—ultimately enhancing both clinical outcomes and healthcare accessibility.

World Journal of Advanced Engineering Technology and SciencesVol. 15(1)
Openalex Percentile: Top 90%
Healthcare Systems and Public Health
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AI-driven data analysis in healthcare: Transforming patient care and operational efficiency — Meghana Bhimavarapu · World Journal of Advanced Engineering Technology and Sciences (2025) | TGRS Research Map | TGRS