Resource Optimization and Cost Reduction for Healthcare Using Big Data Analytics
Khurshed Iqbal
Associate Professor, UCOZ Campus, BUITEMS, Department of management sciences khurshediqbalswati@gmail.com
Keywords: Big data analytics, Resource Optimization, Cost Reduction, Healthcare, Predictive Analytics
Abstract
This study explores the diverse applications of big data analytics in the healthcare sector and highlights its potential for cost savings and improved outcomes. The findings indicate that predictive analytics can be employed to forecast patient volumes, disease outbreaks, and resource requirements by analyzing historical patient data. By leveraging this information, healthcare providers can optimize staffing levels, allocate resources appropriately, and reduce unnecessary costs. Additionally, big data analytics can play a crucial role in fraud detection, enabling the identification of fraudulent activities in healthcare billing and insurance claims. Through the analysis of patterns and anomalies in vast amounts of claims data, algorithms can flag suspicious transactions, contributing to significant cost savings for healthcare payers. Moreover, the study demonstrates the value of big data analytics in supply chain optimization, real-time monitoring, personalized medicine and treatment plans, population health management, and operational efficiency. By leveraging these analytics, healthcare organizations can optimize procurement processes, prevent adverse events, tailor treatment plans to individuals, manage specific populations effectively, and improve overall operational efficiency, leading to cost savings and enhanced patient experiences.
