The Role of Data Analytics in Enhancing Decision-Making Processes in Healthcare Management
DOI:
https://doi.org/10.56294/hl2022.93Keywords:
Data Analytics, Analytics, Healthcare, Clinicians, Risk Analysis, Data-Driven CultureAbstract
Introduction: The Intro explains how data analytics is becoming increasingly widely used in the healthcare sector and how it can enhance decision-making processes. It also emphasizes the necessity for improved healthcare management decision-making, as healthcare systems are functioning under growing pressure and complexity.
Methods: The pertinent literature was completely revised to carry out the goals of this study. It covered different uses of data analytics in healthcare management, such as predictive modeling and risk analysis, along with performance evaluation.
Results: The Results tab describes the study's main findings. These include the vital role that data analytics plays in detecting patterns and trends, anticipating results, and maximizing performance in healthcare management.
Conclusions: Overall, data artificial intelligence is an upcoming trend in healthcare that will not only improve our healthcare handling but will increase the chances of any probability of being a pandemic. It can revolutionize how healthcare is provided, allowing clinicians to make decisions based on evidence and organizations to foster a data-driven culture.
References
1. Vassakis, K., Petrakis, E., & Kopanakis, I. (2018). Big data analytics: applications, prospects and challenges. Mobile big data: A roadmap from models to technologies, 3-20.
2. Benzidia, S., Makaoui, N., & Bentahar, O. (2021). The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technological forecasting and social change, 165, 120557.
3. Shah, N. D., Steyerberg, E. W., & Kent, D. M. (2018). Big data and predictive analytics: recalibrating expectations. Jama, 320(1), 27-28.
4. Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2019). Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision, 57(8), 1923-1936.
5. Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management, 54(5), 758-790.
6. Gadde, H. (2020). AI-Enhanced Data Warehousing: Optimizing ETL Processes for Real-Time Analytics. Revista de Inteligencia Artificial en Medicina, 11(1), 300-327.
7. Shahid, N., Rappon, T., & Berta, W. (2019). Applications of artificial neural networks in health care organizational decision-making: A scoping review. PloS one, 14(2), e0212356.
8. Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC medical informatics and decision making, 21, 1-23.
9. Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big data, 6(1), 1-16.
10. Endriyas, M., Alano, A., Mekonnen, E., Ayele, S., Kelaye, T., Shiferaw, M.,& Hailu, S. (2019). Understanding performance data: health management information system data accuracy in Southern Nations Nationalities and People’s Region, Ethiopia. BMC health services research, 19, 1-6.
Published
Issue
Section
License
Copyright (c) 2022 Snehanshu Dey , Chetan Kumar Sharma , Pooja Varma (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
