Data-driven decision-making in healthcare: Improving patient outcomes through predictive modeling
1 International Association of Computer Analysts and Researchers, Abuja, Nigeria.
2 OneAdvanced, UK.
3 Nigeria Inter-Bank Settlement System Plc (NIBSS), Nigeria.
4 Independent Researcher, UK.
Review
International Journal of Scholarly Research in Multidisciplinary Studies, 2024, 05(01), 059–067.
Article DOI: 10.56781/ijsrms.2024.5.1.0040
Publication history:
Received on 12 July 2024; revised on 17 August 2024; accepted on 20 August 2024
Abstract:
This review paper explores the transformative role of data-driven decision-making in healthcare, focusing on how predictive modeling enhances patient outcomes. Predictive modeling techniques have evolved significantly over the years. They are now integral to healthcare operations, aiding in early diagnosis, personalized treatment, and chronic disease management. Despite its potential, implementing predictive modeling faces challenges, including data privacy concerns, integration with existing systems, and potential biases. This paper also examines emerging trends, such as the integration of AI, real-time data from wearable devices, and advancements in genomics, that are driving the future of predictive modeling. Furthermore, the review highlights the need for ongoing research in areas like explainable AI, data interoperability, and privacy protection to realize the full benefits of predictive modeling in healthcare. Predictive modeling can play a crucial role in improving patient outcomes and advancing precision medicine by addressing these challenges and leveraging new technological advancements.
Keywords:
Predictive Modeling; Healthcare Decision-Making; Patient Outcomes; Artificial Intelligence in Healthcare; Data Privacy
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0