Data Integration Challenges and Opportunities in Multi-Platform Health Information Systems: A Comprehensive Review

Abstract

Background: As healthcare systems face increasing complexity due to aging populations, the prevalence of chronic diseases, and rising costs, there is an urgent need for innovative solutions to optimize care delivery. Artificial intelligence (AI) has emerged as a transformative force in predictive healthcare analytics, leveraging large datasets to forecast patient outcomes, stratify risk, and personalize treatments. This review explores the role of AI in advancing predictive healthcare analytics and its implications for clinical decision-making.

Methods: A comprehensive review of current literature was conducted, focusing on AI methodologies such as machine learning (ML) and deep learning (DL) applied to electronic health records (EHRs), imaging data, and wearable device outputs. The analysis evaluated the effectiveness of AI in diagnosing diseases, predicting clinical outcomes, and enabling personalized medicine. Attention was given to the ethical challenges and data integration complexities associated with these technologies.

Results: AI-based predictive analytics has demonstrated superior accuracy compared to traditional methods in areas such as early disease detection, patient risk assessment, and treatment optimization. Notable applications include AI-powered imaging for cancer detection, real-time monitoring of chronic conditions, and personalized treatment planning using large-scale EHR data. However, challenges such as algorithmic bias, data privacy concerns, and integration of AI systems across diverse healthcare platforms persist.

Conclusion: AI has the potential to revolutionize healthcare by enhancing the precision, efficiency, and personalization of patient care. Bridging gaps in data standardization, improving algorithmic transparency, and addressing ethical concerns are essential for realizing the full potential of AI in healthcare. Continued interdisciplinary collaboration will be pivotal in optimizing AI technologies for widespread adoption and improved patient outcomes.