Towards A Comprehensive Definition Of Big Data In Healthcare: A Literature-Driven Approach

Abstract

The rapid expansion of big data in healthcare has transformed clinical decision-making, patient care, and public health management. With the increasing digitalization of medical records, the integration of AI-driven analytics, wearable health devices, and cloud-based data management systems has enhanced diagnostic accuracy, disease surveillance, and personalized treatment planning. However, despite these advancements, challenges such as data standardization, interoperability, privacy concerns, and algorithmic bias remain significant barriers to fully leveraging big data in healthcare. This study adopts a case study approach, analyzing real-world implementations of big data analytics in 15 healthcare institutions to explore its practical applications, technological enablers, and ethical considerations. The case studies highlight the significant role of AI-driven diagnostics, predictive modeling, federated learning, and blockchain-based security solutions in improving healthcare outcomes. Findings indicate that while big data has contributed to early disease detection, hospital efficiency, and remote patient monitoring, unresolved challenges persist in data governance, regulatory compliance, and equitable AI deployment. The study also reveals that despite efforts to standardize healthcare data exchange through frameworks such as FHIR and HL7, interoperability remains a critical issue across institutions. Additionally, concerns regarding patient privacy and biases in AI-driven models present ethical dilemmas that require regulatory oversight and improved data diversity. By synthesizing insights from real-world case studies, this research provides a comprehensive understanding of the opportunities and limitations of big data in healthcare, offering evidence-based recommendations for policymakers, healthcare providers, and technology developers to optimize its potential while addressing implementation challenges.