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Review Article Open Access December 26, 2021

Scalable Data Warehouse Architecture for Population Health Management and Predictive Analytics

Bindu Madhavi Mangalampalli 1,*
1
Sr. BI Developer, USA
Publihed in: Current Research in Public Health (Volume 1, Issue 1, 2021)
Page(s): 1-18
DOI: 10.31586/wjcmr.2021.1378
Received
September 20, 2021
Revised
November 06, 2021
Accepted
December 20, 2021
Published
December 26, 2021
Keywords
Scalable Data Warehousing Architecture; Population Health Management Analytics; Predictive Risk Stratification Models; Healthcare Data Pipelines; Real-Time Streaming Data Processing; Cloud-Based Data Storage Systems; Supervised Machine Learning in Healthcare; Descriptive Analytics Dashboards; Big Data for Public Health; Multi-Tier Data Architecture Design; Cost-Optimized Data Infrastructure; Data Mining for Population Insights; Integrated Cloud Data Warehouses; Near-Real-Time Health Monitoring; Low-Cost Storage Technologies; Urban and Environmental Data Analytics; Migration Policy Data Modeling; Developing Countries Digital Infrastructure; Health Risk Forecasting Systems; Performance-Optimized Analytics Platforms
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2021. Published by Scientific Publications
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Cite This Article

APA Style
Mangalampalli, B. M. (2021). Scalable Data Warehouse Architecture for Population Health Management and Predictive Analytics. Current Research in Public Health, 1(1), 1-18. https://doi.org/10.31586/wjcmr.2021.1378
ACS Style
Mangalampalli, B. M. Scalable Data Warehouse Architecture for Population Health Management and Predictive Analytics. Current Research in Public Health 2021 1(1), 1-18. https://doi.org/10.31586/wjcmr.2021.1378
Chicago/Turabian Style
Mangalampalli, Bindu Madhavi. 2021. "Scalable Data Warehouse Architecture for Population Health Management and Predictive Analytics". Current Research in Public Health 1, no. 1: 1-18. https://doi.org/10.31586/wjcmr.2021.1378
AMA Style
Mangalampalli BM. Scalable Data Warehouse Architecture for Population Health Management and Predictive Analytics. Current Research in Public Health. 2021; 1(1):1-18. https://doi.org/10.31586/wjcmr.2021.1378 
@Article{crph1378,
AUTHOR = {Mangalampalli, Bindu Madhavi},
TITLE = {Scalable Data Warehouse Architecture for Population Health Management and Predictive Analytics},
JOURNAL = {Current Research in Public Health},
VOLUME = {1},
YEAR = {2021},
NUMBER = {1},
PAGES = {1-18},
URL = {https://www.scipublications.com/journal/index.php/WJCMR/article/view/1378},
ISSN = {2831-5162},
DOI = {10.31586/wjcmr.2021.1378},
ABSTRACT = {Scalable architecture principles for data warehousing are introduced to support population health management and predictive analytics. These principles are validated through the design of an accompanying Data Pipeline that allows the integration of non-traditional data sources, the use of real-time data for descriptive analytics dashboards, and support for the generation of supervised Machine Learning models. Several analytical capabilities have been implemented to exemplify the practical application of the principles, including predictive models for Risk Stratification in health care. Optimal cost-effectiveness and performance considerations ensure the practical relevance of the architectural principles and associated Data Pipeline. In recent years, the availability of Low-Cost Data Storage services and the increasing popularity of Streaming technologies opened new possibilities for the storage and processing of Streaming data on a near-real-time basis. These technologies can help Developing Countries in tackling many relevant issues such as Urban Planning, Environmental Management, Migration Policies, etc. A multi-tier approach combining Cloud-based Storage with Data Warehousing and Data Mining technologies can offer an interesting architecture to exploit Big Data related to populations.},
}
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AB  - Scalable architecture principles for data warehousing are introduced to support population health management and predictive analytics. These principles are validated through the design of an accompanying Data Pipeline that allows the integration of non-traditional data sources, the use of real-time data for descriptive analytics dashboards, and support for the generation of supervised Machine Learning models. Several analytical capabilities have been implemented to exemplify the practical application of the principles, including predictive models for Risk Stratification in health care. Optimal cost-effectiveness and performance considerations ensure the practical relevance of the architectural principles and associated Data Pipeline. In recent years, the availability of Low-Cost Data Storage services and the increasing popularity of Streaming technologies opened new possibilities for the storage and processing of Streaming data on a near-real-time basis. These technologies can help Developing Countries in tackling many relevant issues such as Urban Planning, Environmental Management, Migration Policies, etc. A multi-tier approach combining Cloud-based Storage with Data Warehousing and Data Mining technologies can offer an interesting architecture to exploit Big Data related to populations.
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