Predictive analytics in healthcare has revolutionized medical decision-making by enabling early disease detection, risk stratification, and personalized treatment plans. However, the implementation of predictive analytics relies on robust data engineering processes to handle the vast amounts of structured and unstructured healthcare data. The integration of electronic health records (EHRs), genomic data, and real-time patient monitoring systems presents significant challenges related to data quality, interoperability, security, and computational efficiency. This paper explores the critical role of data engineering in predictive analytics, addressing key challenges such as data acquisition, cleaning, storage, and real-time processing. Furthermore, it discusses various solutions, including data integration frameworks, cloud-based infrastructures, and artificial intelligence (AI)-driven data processing techniques. The research highlights emerging trends such as federated learning, blockchain for data security, and automated data pipelines that enhance the scalability and accuracy of predictive models. The paper concludes by emphasizing the need for standardized data governance policies, cross-institutional collaborations, and advanced machine learning algorithms to overcome data engineering challenges and improve healthcare outcomes.
Sophia White· International Journal of Art...· 0 citations
Cloud computing has transformed business operations by providing scalable, flexible, and cost-effective solutions. Cloud-Based Financial Management Systems (CFMS) enhance accounting, budgeting, auditing, compliance, and real-time reporting while overcoming the limitations of traditional financial systems, such as high infrastructure costs, restricted accessibility, and complex maintenance. By integrating accounting, ERP, payroll, taxation, procurement, and business intelligence functions into a unified platform, cloud solutions improve organizational efficiency, transparency, and collaboration. Features such as automated workflows, secure remote access, disaster recovery, and data backup support business continuity and operational effectiveness. Advanced technologies including virtualization, big data analytics, and machine learning further enable predictive financial insights and strategic decision-making. Despite benefits, challenges such as security risks, privacy concerns, regulatory compliance, vendor dependency, and integration issues remain. This study reviews the architecture, deployment models, security mechanisms, benefits, and challenges of cloud financial management systems, while proposing a conceptual framework to improve financial process automation. The findings indicate that cloud adoption significantly enhances cost optimization, data accessibility, transaction efficiency, and decision support, making cloud computing a key driver of digital financial transformation and enterprise sustainability.
Sophia White, Benjamin F. Scott· International Journal of Com...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
