Adoption of Big Data Analytics in Predicting Drug Recall Trends

DOI: https://doi.org/10.63345/ijrmp.v12.i10.3

Aditya Rawal

Independent Researcher

Gujarat, India

Abstract

The rapid evolution of the pharmaceutical industry combined with increased regulatory scrutiny has made the early detection of drug quality issues more critical than ever. This study investigates the role of big data analytics in predicting drug recall trends, offering a comprehensive framework that leverages diverse data sources including adverse event reports, manufacturing data, social media feedback, and regulatory filings. By integrating predictive models and machine learning techniques, the proposed approach identifies patterns that precede recall events, thereby enhancing the capacity of regulatory bodies and companies to mitigate risk proactively. The manuscript details a mixed-method approach, incorporating both quantitative statistical analyses and qualitative literature insights up to the year 2022. A detailed statistical analysis is provided using a tabulated overview of recall frequencies over time and the performance of predictive models. Findings indicate that big data analytics not only improves recall prediction accuracy but also contributes to faster decision-making and more targeted quality assurance processes. The implications of these results suggest that with continuous improvement and integration of real-time data feeds, stakeholders in the pharmaceutical industry can significantly reduce the economic and public health impacts associated with drug recalls. This work lays the foundation for further exploration into real-time analytics applications and calls for enhanced collaboration between data scientists, regulatory bodies, and pharmaceutical manufacturers to harness the full potential of big data technologies.

Keywords

Big Data Analytics, Drug Recall, Predictive Modeling, Pharmaceutical Quality, Machine Learning

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