The Importance of Data-Driven Decision-Making in Public Health |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-5 |
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| Year of Publication : 2024 | ||
| Authors : Keyur Patel | ||
| DOI : 10.14445/22312803/IJCTT-V72I5P103 | ||
How to Cite?
Keyur Patel, "The Importance of Data-Driven Decision-Making in Public Health," International Journal of Computer Trends and Technology, vol. 72, no. 5, pp. 27-32, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I5P103
Abstract
A large portion of the public health frameworks is taking transformations into data-centred techniques as a base for proper decision-making targeting the complicated nature of modern health issues. In recent years, the old-fashioned methods of handling big data related to public health have yet to practically encompass the amount of data emerging from the computerization of medical reporting. This article will look at how cloud-based data analytics can transform people-based decision-making into revolution and modern approaches to the current public health decisions that are being made. The objective of the report was to lay the basis of the literature on the significance of data-driven decision-making for public health, traditional viewpoint obstacles, action of cloud computing, and research studies on cloud computing-based analytics in the public health setting. Therefore, the novella goes along from being data for acquisition and preprocessing to having a model to be developed, trained, and tuned up until it is ready to be deployed and maintain sufficient control of data integrity, security, privacy compliance, including collaborative collection and sharing of information, and continuous monitoring and further update. This highlights that public health organizations are likely to build their capability of using evidence-based data that can be spread globally and in a timely manner, keeping in mind the health outcomes of the whole world's population delicately through cloud-based analytics.
Keywords
Big data, Cloud computing, Data engineering, Public health.
Reference
[1] Odunayo Josephine Akindote et al., “Comparative Review of Big Data Analytics and GIS in Healthcare Decision-Making,” World Journal of Advanced Research and Reviews, vol. 20, no. 3, pp. 1293-1302, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Anna Petrovskis et al., “The DASH Model: Data for Addressing Social Determinants of Health in Local Health Departments,” Nursing Inquiry, vol. 30, no. 1, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Joseph Bamidele Awotunde, Roseline Oluwaseun Ogundokun, and Sanjay Misra, “Cloud and IoMT-Based Big Data Analytics System during the COVID-19 Pandemic,” Efficient Data Handling for Massive Internet of Medical Things: Healthcare Data Analytics, pp. 181- 201, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Alexy Gene Castillo, Sherwin M. Telan, and Thelma Palaoag, “Cloud-Based Data Mining Framework: A Model to Improve Maternal Healthcare,” Proceedings of the 2nd International Conference on Cryptography, Security and Privacy, Guiyang China, pp. 21-28, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Nicholas Mirin et al., “Data Science in Public Health: Building Following Generation Capacity,” Arxiv, pp. 1-20, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Louis Ehwerhemuepha et al., “HealtheDataLab is a Cloud Computing Solution for Data Science and Advanced Analytics in Healthcare with Applications to Predict Multi-Center Pediatric Readmissions,” BMC Medical Informatics and Decision Making, vol. 20, pp. 1-12, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Mengchun Gong et al., “Cloud-Based System for Effective Surveillance and Control of COVID-19: Useful Experiences from Hubei, China,” Journal of Medical Internet Research, vol. 22, no. 4, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Anish Jindal et al., “Providing Healthcare-as-a-Service Using Fuzzy Rule Based Big Data Analytics in Cloud Computing,” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 5, pp. 1605-1618, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] K. Kavitha, and C. Kumuthini, Cloud-Based Data Analytics for Healthcare 5.0, Pioneering Smart Healthcare 5.0 with IoT, Federated Learning, and Cloud Security, pp. 1-13, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Fatima Khalique, Shoab A. Khan, and Irum Nosheen, “A Framework for Public Health Monitoring, Analytics, and Research,” IEEE Access, vol. 7, pp. 101309-101326, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Fernando López-Martínez et al., “A Case Study for a Big Data and Machine Learning Platform to Improve Medical Decision Support in Population Health Management,” Algorithms, vol. 13, no. 4, pp. 1-19, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Laura Mählmann et al., “Big Data for Public Health Policy-Making: Policy Empowerment,” Public Health Genomics, vol. 20, no. 6, pp. 312-320, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Patel Keyur, “Lawful and Righteous Considerations for the Use of Artificial Intelligence in Public Health,” International Journal of Computer Trends and Technology, vol. 72, no. 1, pp. 48-52, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Abraham George et al., “Use of System Dynamics Modelling for Evidence-Based Decision Making in Public Health Practice,” Systems, vol. 11, no. 5, pp. 1-17, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Ali H. Mhmood, and Murshid Reza, “Urban Health Informatics through Cloud-Based Data Integration,” Journal of Big-Data Analytics and Cloud Computing, vol. 7, no. 4, pp. 1-17, 2022.
[Google Scholar] [Publisher Link]
[16] Shah Jahan Miah, A Demand-Driven Cloud-Based Business Intelligence for Healthcare Decision-Making, Health Care Delivery and Clinical Science: Concepts, Methodologies, Tools, and Applications, pp. 964-979, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Innocent Mumba Mwila, “Geospatial, Cloud and Web-Based Model for Evidence-Based Decision-Making in Tuberculosis Prevention,” Theses and Dissertations, 2020.
[Google Scholar] [Publisher Link]
[18] Seungeun Park et al., “Impact of Data Visualization on Decision-Making and its Implications for Public Health Practice: A Systematic Literature Review,” Informatics for Health & Social Care, vol. 47, pp. 175-193, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Lisa E.M. Hopcroft et al., “Data-Driven Identification of Potentially Successful Intervention Implementations: A Proof of Concept Using Five Years of Opioid Prescribing Data from Over 7000 Practices in England,” medRxiv, pp. 1-17, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Sumanth Tatineni, “Cloud-Based Data Analytics for Smart Cities: Enhancing Urban Infrastructure and Services,” International Research Journal of Modernization in Engineering, Technology, and Science, vol. 5, no. 11, pp. 904-912, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Shuiting Zhang et al., “Anesthesia Decision Analysis Using a Cloud-Based Big Data Platform,” European Journal of Medical Research, vol. 29, no. 1, pp. 1-9, 2024.
[CrossRef] [Google Scholar] [Publisher Link]