Improving healthcare decision-making with predictive analytics: A conceptual approach to patient risk assessment and care optimization
1 Osiri University Lincoln Nebraska, USA and Apex Home Care INC.
2 Independent Researcher, Abuja, Nigeria.
3 Department of Business Administration, Texas A&M University Commerce, Texas USA.
4 Etihuku Pty Ltd, Midrand, Gauteng, South Africa.
Review Article
International Journal of Scholarly Research in Medicine and Dentistry, 2024, 03(02), 001–010.
Article DOI: 10.56781/ijsrmd.2024.3.2.0034
Publication history:
Received on 07 October 2024; revised on 14 November 2024; accepted on 17 November 2024
Abstract:
This review paper explores the transformative potential of predictive analytics in enhancing healthcare decision-making, patient risk assessment, and care optimization. Predictive analytics utilizes advanced data-driven techniques to identify patients at risk of developing chronic conditions and to optimize treatment strategies tailored to individual needs. By integrating various data sources, including electronic health records, wearable technology, and genomic information, predictive models can provide valuable insights that significantly improve patient outcomes and operational efficiency in healthcare settings. Despite its advantages, the paper highlights critical challenges such as data privacy and security, biases in predictive models, and the necessity for robust regulatory frameworks. The review emphasizes the importance of ongoing research in refining predictive models, improving data integration, and addressing ethical considerations to ensure equitable healthcare delivery. Overall, this paper advocates for a strategic approach to harnessing predictive analytics to foster a more responsive and effective healthcare system.
Keywords:
Predictive analytics; Patient risk assessment; Healthcare decision-making; Data integration; Personalized medicine; Ethical considerations
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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