Harnessing Predictive Analytics in Healthcare: A Pathway to Better Patient Outcomes

Authors: Manoj Kumar

DOI: https://doi.org/10.5281/zenodo.14209042

Short DOI: https://doi.org/g8rrf4

Country: USA

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Abstract: Predictive analytics has revolutionized the healthcare industry by enabling data-driven insights that enhance patient outcomes and streamline care processes (Zhang, 2020). Depending on machine learning (ML), deep learning (DL), and artificial intelligence (AI), predictive analytics models work with large sets of healthcare data such as electronic health records (EHRs), diagnostic images, and clinical outcomes to predict the potential health event and to enhance the treatment plan (Badawy et al., 2023). Besides facilitating effective disease identification at the primary stage, it enables the elimination of hospital readmissions, thereby saving costs of resources (Broekharst et al., 2023). Hypothesis models determine interactions of complicated data to help multiple industries involving healthcare facilitate proactive, informed decisions (Zhang, 2020). However, the implementation of such methods finds itself with issues like data privacy and ethical concerns to uphold patient confidence and compliance with improved rules (Nnamdi, 2024). Despite these challenges, predictive analytics keeps adding value to the healthcare sector to improve future functioning by enhancing personalized and effective service delivery.

Keywords: Predictive Analytics, Machine Learning (ML), Deep Learning (DL), Artificial Intelligence (AI), Patient Outcomes, Healthcare Optimization.

Paper Id: 231649

Published On: 2024-11-05

Published In: Volume 12, Issue 6, November-December 2024

Cite This: Harnessing Predictive Analytics in Healthcare: A Pathway to Better Patient Outcomes - Manoj Kumar - IJIRMPS Volume 12, Issue 6, November-December 2024. DOI 10.5281/zenodo.14209042

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