Abstract Due to increased digitalization and the widespread use of wearable technology, smart medical equipment, and electronic health records, big data analytics has emerged as a revolutionary force in contemporary healthcare systems. Large amounts of structured, semi-structured, and unstructured data with high volume, velocity, and variety are produced by the healthcare industry. Optimal resource allocation, increased operational efficiency, and better clinical decision support are all made possible by the effective analysis of this data. In order to extract valuable insights that enable precision medicine, preventive care, and improved patient outcomes, big data analytics combines sophisticated computer models, artificial intelligence, and real-time analytics. Drug development, illness prevention, diagnosis and treatment, hospital operations, and post-care management are just a few of the important healthcare sectors where applications are found. But major issues including data quality, privacy, interoperability, storage, and economical information retrieval still exist. Large-scale healthcare data management and processing heavily rely on technologies like Hadoop, HDFS, Map Reduce, and Apache frameworks. Complex system modelling and simulations are further supported by scientific programming and sophisticated analytics. Big data analytics has enormous potential to save healthcare expenses, cut down on errors, and enhance treatment quality despite obstacles. To fully exploit its benefits in healthcare systems, more study on data governance, infrastructure improvement, and integration techniques is necessary.
Sabale et al. (2026) studied this question.