Transforming healthcare with data analytics: Predictive models for patient outcomes
1 Independent Researcher, Illinois, USA.
2 Independent Researcher, UK.
Review Article
GSC Biological and Pharmaceutical Sciences, 2024, 27(03), 025–035.
Article DOI: 10.30574/gscbps.2024.27.3.0190
Publication history:
Received on 20 April 2024; revised on 03 June 2024; accepted on 06 June 2024
Abstract:
Healthcare organizations are increasingly leveraging data analytics to improve patient outcomes and enhance the efficiency of healthcare delivery. Predictive modeling, in particular, has emerged as a powerful tool for forecasting patient outcomes based on various data sources such as electronic health records, wearable devices, and genetic information. This paper provides an overview of the transformative role of data analytics in healthcare, with a specific focus on predictive models for patient outcomes. The introduction discusses the importance of data analytics in healthcare and outlines the purpose of the paper. It highlights the evolution of data analytics in healthcare, types of healthcare data, and challenges in data collection and management. The role of predictive modeling in healthcare is then explored, emphasizing its significance in improving patient outcomes and common techniques used in predictive modeling. The paper discusses various data sources for predictive modeling, including electronic health records, wearable devices, genetic and genomic data, and social determinants of health. It also covers the process of developing predictive models, including data preprocessing, model selection, and validation techniques, as well as ethical considerations. Furthermore, the paper explores the applications of predictive models in healthcare, such as early disease detection, personalized treatment planning, hospital resource optimization, and patient engagement. Case studies and examples illustrate real-world implementations of predictive analytics in healthcare organizations. Finally, the paper addresses challenges and future directions in healthcare data analytics, including data privacy and security concerns, interpretability of predictive models, integration into clinical workflows, and emerging trends. Overall, this paper underscores the transformative potential of data analytics, particularly predictive modeling, in revolutionizing healthcare delivery and improving patient outcomes.
Keywords:
Healthcare; Data Analytics; Predictive Modeling; Patient Outcomes; Electronic Health Records; Predictive Analytics
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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
