Data Engineering for Predictive Analytics in Healthcare: Challenges and Solutions

Abstract

Predictive analytics in healthcare has revolutionized medical decision-making by enabling early disease detection, risk stratification, and personalized treatment plans. However, the implementation of predictive analytics relies on robust data engineering processes to handle the vast amounts of structured and unstructured healthcare data. The integration of electronic health records (EHRs), genomic data, and real-time patient monitoring systems presents significant challenges related to data quality, interoperability, security, and computational efficiency. This paper explores the critical role of data engineering in predictive analytics, addressing key challenges such as data acquisition, cleaning, storage, and real-time processing. Furthermore, it discusses various solutions, including data integration frameworks, cloud-based infrastructures, and artificial intelligence (AI)-driven data processing techniques. The research highlights emerging trends such as federated learning, blockchain for data security, and automated data pipelines that enhance the scalability and accuracy of predictive models. The paper concludes by emphasizing the need for standardized data governance policies, cross-institutional collaborations, and advanced machine learning algorithms to overcome data engineering challenges and improve healthcare outcomes.