Big Data and Machine Learning in Healthcare: A Business Intelligence Approach for Cost Optimization and Service Improvement

Healthcare business intelligence advances through the combination of Big Data and Machine Learning (ML) technology which improves both cost reduction and service quality. Healthcare organizations employ predictive analysis together with AI-driven choices and real-time processing to minimize costs as global healthcare fees continue increasing while improving patient care efficiency. This paper investigates the transformation of resource distribution and predictive equipment maintenance and individual medical approaches through Big Data and ML models along with supervised learning and deep learning and anomaly detection algorithms. The research follows a quantitative approach to study both actual case examples and statistical models which predict hospital admissions while optimizing resource management to lower operational flaws. AI predictive analytics produces a 30% deduction in healthcare bills supported by studies with results showing also a 25% increase in medical service delivery efficiency. Real-time data integration allows medical professionals to detect diseases earlier and develop precise treatment plans for each patient which increases patient satisfaction rates. The study adds to existing AI-driven healthcare business intelligence research by delivering practical guidelines which healthcare administrators and policymakers and technology leaders can use. The paper requirement of data governance frameworks together with ethical AI implementation methods and scalable decision systems based on ML is necessary for achieving the complete benefits of Big Data in healthcare.