Open Access

A Comparative Analysis of Data-Driven Decision Support Systems: Bridging Clinical Epidemiology, Public Health Informatics, And Predictive E-Commerce Analytics in The Era of Big Data

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Paul Hathaway ¹ ,

⁴ Department of Information Systems and Quantitative Analysis, University of Edinburgh, United Kingdom

Abstract

The rapid digitization of global infrastructure has necessitated the development of sophisticated frameworks for the collection, analysis, and interpretation of heterogeneous data streams. This research article explores the convergence of information systems across two critical domains: healthcare informatics and e-commerce analytics. By synthesizing health data collection methodologies across European Union member states with predictive consumer behavior models, the study identifies universal principles of reproducible, ethical, and collaborative research. The investigation delves into the implementation of Electronic Health Records (EHR) and the utilization of novel epidemiological tools like the Data Extraction for Epidemiological Research (DExtER) system to automate clinical studies. Concurrently, the paper examines the moderator effects of gender and personality traits on digital consumption, particularly during the COVID-19 pandemic, and evaluates the efficacy of machine learning in optimizing Customer Acquisition Cost (CAC) through automated cohort analysis. Through an extensive theoretical elaboration on Business Intelligence (BI) and Open Science design, the research argues that the future of evidence-based policy and commercial sustainability depends on the standardization of Key Performance Indicators (KPIs) and the mitigation of cybersecurity risks. The findings suggest that while the context of data-whether clinical or commercial-varies, the underlying requirements for data integrity, reproducibility, and dynamic behavioral analysis remain constant. This article provides a comprehensive synthesis for researchers and practitioners aiming to navigate the complexities of big data management in the 21st century.

Keywords

Health Informatics, E-Commerce Analytics, Big Data Management, Open Science

References

Abdul Hussien, F. T., Rahma, A. M. S., & Abdulwahab, H. B. (2021). An E-Commerce Recommendation System Based on Dynamic Analysis of Customer Behavior. Sustainability, 13, 10786.

Ahmed, H., Jilani, T. A., Haider, W., Abbasi, M. A., Nand, S., & Kamran, S. (2017). Establishing standard rules for choosing best KPIs for an e-commerce business based on google analytics and machine learning technique. International Journal of Advanced Computer Science and Applications, 8(5), 12-24.

Community, T. T. W. (2022). The Turing Way: A handbook for reproducible, ethical and collaborative research. Zenodo.

D’Adamo, I., González-Sánchez, R., Medina-Salgado, M. S., & Settembre-Blundo, D. (2021). Methodological Perspective for Assessing European Consumers’ Awareness of Cybersecurity and Sustainability in E-Commerce. Sustainability, 13, 11343.

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 1-25.

Du, X., Liu, B., & Zhang, J. (2019). Application of business intelligence based on big data in e-commerce data analysis. Journal of Physics: Conference Series, 1395, 012011.

Gokhale, K. M., Chandan, J. S., Toulis, K., Gkoutos, G., Tino, P., & Nirantharakumar, K. (2021). Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies. European Journal of Epidemiology, 36, 165-178.

Google Analytics Help (2021). About Cohorts. Available online: https://support.google.com/analytics/answer/6158745?hl=en#zippy=%2Cin-this-article

Hermes, A., & Riedl, R. (2021). Influence of Personality Traits on Choice of Retail Purchasing Channel: Literature Review and Research Agenda. Journal of Theoretical and Applied Electronic Commerce Research, 16, 3299-3320.

Kale, A. (2025). CAC Payback Period Optimization Through Automated Cohort Analysis. International Journal of Management and Business Development, 2(10), 15-20. https://doi.org/10.55640/ijmbd-v02i10-02

Luo, H., Cheng, S., Zhou, W., Song, W., Yu, S., & Lin, X. (2021). Research on the Impact of Online Promotions on Consumers’ Impulsive Online Shopping Intentions. Journal of Theoretical and Applied Electronic Commerce Research, 16, 2386-2404.

National Academies of Sciences, Engineering, and Medicine (2018). Open Science by Design: Realizing a Vision for 21st Century Research. The National Academies Press.

Nguyen, L., Bellucci, E., & Nguyen, L. T. (2014). Electronic health records implementation: An evaluation of information system impact and contingency factors. International Journal of Medical Informatics, 83, 779-796.

Unim, B., Mattei, E., Carle, F., Tolonen, H., Bernal-Delgado, E., Achterberg, P., et al. (2022). Health data collection methods and procedures across EU member states: findings from the InfAct Joint Action on health information. Archives of Public Health, 80(1), 17.

Zabukovšek, S. S., Tominc, P., & Bobek, S. (2020). Business Informatics Principles. In V. Pászto, C. Jürgens, P. Tominc, J. Burian (Eds.), Spationomy, Springer, Cham, Switzerland.

Zhao, Y., & Bacao, F. (2021). How Does Gender Moderate Customer Intention of Shopping via Live-Streaming Apps during the COVID-19 Pandemic Lockdown Period? International Journal of Environmental Research and Public Health, 18, 13004.

Zumstein, D., & Kotowski, W. (2020). Success Factors of E-Commerce-Drivers of the Conversion Rate and Basket Value. Proceedings of the 18th International Conference e-Society 2020 Sofia, Bulgaria.

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A Comparative Analysis of Data-Driven Decision Support Systems: Bridging Clinical Epidemiology, Public Health Informatics, And Predictive E-Commerce Analytics in The Era of Big Data

Abstract

Keywords

References

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