Volume 1 Issue 3 | 2024 | View PDF
Paper Id: IJMSM-V1I3P103
doi: 10.71141/30485037/V1I3P103

Real-Time ETL for Healthcare Data Management

Aakash, Rishi

Citation:
Aakash, Rishi, "Real-Time ETL for Healthcare Data Management" International Journal of Multidisciplinary on Science and Management, Vol. 1, No. 3, pp. 29-44, 2024.

Abstract:
Data-driven decisions are fundamental to quality healthcare services, which are efficient, high quality and patient-focused in the healthcare industry. Unfortunately, the dynamic nature of healthcare data presents obstacles to conventional ETL (Extract, Transform, Load) processes that are usually slow and cannot respond to the time point requirements of real time. The development and application of real-time ETL in healthcare data management are explored in this paper. In analyzing healthcare data, we identify the distinctions of healthcare data in terms of very high-frequency updates, adherence to privacy standards and across various diverse systems. Using real time ETL, health care practitioners can quickly integrate data, improve decision making ability and ultimately improve their outcomes by leveraging cutting edge data transformation techniques and cloud based ETL tools. Our work demonstrates the importance of real-time ETL in enabling a responsive, unified, coordinated, and compliant data environment that meets the essential informational needs of healthcare providers.

Keywords: Real-Time ETL, Healthcare Data Management, Data Transformation, Data Integration, Interoperability, Data Privacy.

References:
1. Vijayalakshmi Manickam, and Minu Rajasekaran Indra, “Dynamic Multi-Variant Relational SchemeBased Intelligent ETL Framework for Healthcare Management,” Soft Computing, vol. 28, pp. 1-27, 2024.
2. Tiago Marques Godinho et al., “ETL Framework for Real-Time Business Intelligence over Medical Imaging Repositories,” Journal of Digital Imaging, vol. 32, pp. 870-879, 2019.
3. Mohammed M.I. Awad, Mohd Syazwan Abdullah, and Abdul Bashah Mat Ali, “Extending ETL Framework Using Service Oriented Architecture,” Procedia Computer Science, vol. 3, pp. 110-114, 2011.
4. Ankitkumar Tejani, “Integrating Energy-Efficient HVAC Systems into Historical Buildings: Challenges and Solutions for Balancing Preservation and Modernization,” ESP Journal of Engineering & Technology Advancements, vol. 1, no. 1, pp. 83-97, 2021. 43 Aakash, and Rishi 1(3), 29-44, 2024
5. Toan C. Ong et al., “Dynamic-ETL: A Hybrid Approach for Health Data Extraction, Transformation and Loading,” BMC Medical Informatics and Decision Making, vol. 17, 2017.
6. Hemanth Gadde, “AI-Enhanced Data Warehousing: Optimizing ETL Processes for Real-Time Analytics,” Journal of Artificial Intelligence in Medicine, vol. 11, no. 1, pp. 300-327, 2020.
7. Manivasanthan R, Jonathan J, Arshard M, "Modern Accounting Systems can Support an Organization's Efficient Management: A case of A, B, and C Transportation" International Journal of Multidisciplinary on Science and Management, Vol. 1, No. 4, pp. 01-06, 2024.
8. Ronakkumar Bathani, “Optimizing Etl Pipelines for Scalable Data Lakes in Healthcare Analytics,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 9, no. 10, pp. 17-24, 2021.
9. Panos Vassiliadis et al., “A Generic and Customizable Framework for the Design of ETL Scenarios,” Information Systems, vol. 30, no. 7, pp. 492-525, 2005.
10. Vijay Panwar, “Web Evolution to Revolution: Navigating the Future of Web Application Development,” International Journal of Computer Trends and Technology, vol. 72, no. 2, pp. 34-40, 2024.
11. Ladjel Bellatreche, Selma Khouri, and Nabila Berkani, “Semantic Data Warehouse Design: From ETL to Deployment à la Carte,” Database Systems for Advanced Applications, Lecture Notes in Computer Science, vol. 7826, pp. 64-83, 2013.
12. Jayanna Hallur, “Social Determinants of Health: Importance, Benifits to communites, and Best practices for Data Collection and Utilization,” International Journal of Science and Research, vol. 13, no. 10, pp. 846- 852, 2024.
13. Safrin S, Madhu S, "Machine Learning for the Identification of Credit Card Fraud" International Journal of Multidisciplinary on Science and Management, Vol. 1, No. 4, pp. 07-14, 2024.
14. Sanjay Moolchandani, “Advancing Credit Risk Management: Embracing Probabilistic Graphical Models in Banking,” International Journal of Science and Research, vol. 13, no. 6, pp. 74-80, 2024.
15. Bharatbhai Pravinbhai Navadiya, “A Survey on Deep Neural Network (DNN) Based Dynamic Modelling Methods for Ac Power Electronic Systems,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 12, no 2, pp. 735-743, 2024.
16. Erum Mehmood, and Tayyaba Anees, “Distributed Real-Time ETL Architecture for Unstructured Big Data, Knowledge and Information Systems, vol. 64, no. 3419-3445, 2022.
17. The Architecture of ETL Processes, Sprinkle, 2024. [Online]. https://www.sprinkledata.com/blogs/thearchitecture-of-etl-processes
18. Sandeep Pushyamitra Pattyam, “Data Engineering for Business Intelligence: Techniques for ETL, Data Integration, and Real-Time Reporting,” Hong Kong Journal of AI and Medicine, vol. 1, no. 2, pp. 1-53, 2021.
19. Jayanna Hallur, “From Monitoring to Observability: Enhacing System Reliability and Team Productivity,” International Journal of science and Research, vol. 13, no. 10, pp. 602-606, 2024.
20. Praveen Borra, “Comparative Review: Top Cloud Service Providers ETL Tools -AWS vs. Azure vs. GCP,” International Journal of Computer Engineering and Technology, vol. 15, no. 3, pp. 203-208, 2024.
21. Paulami Bandyopadhyay, “Scaling Data Engineering with Advanced Data Management Architecture: A Comparative Analysis of Traditional ETL Tools Against the Latest Unified Platform,” International Journal of Computer Trends and Technology, vol. 72, no. 10, pp. 22-30, 2024.
22. Arun Kumar Ramachandran Sumangala Devi, “AI-Enabled ETL Testing Frameworks on Data Warehousing Testing automation using ML,” TechRxiv, pp. 1-4, 2024.
23. Ahmad Amjad Mir, “Optimizing Mobile Cloud Computing Architectures for Real-Time Big Data Analytics in Healthcare Applications: Enhancing Patient Outcomes through Scalable and Efficient Processing Models,” Integrated Journal of Science and Technology, vol. 1, no. 7, pp. 2024.
24. Bilal Khan et al., “An Overview of ETL Techniques, Tools, Processes and Evaluations in Data Warehousing,” Journal on Big Data, vol. 6, pp. 1-20, 2024.