Data-Driven Healthcare Reform: Big Data Applications in Price Transparency, Resource Forecasting, and Pandemic Response

The healthcare sector is undergoing a profound transformation driven by the integration of big data analytics and intelligent technologies. This review explores how data-driven approaches are reforming healthcare systems, with a specific focus on three core domains: price transparency, resource forecasting, and pandemic response. By synthesizing existing literature and real-world case studies, the paper highlights the strategic importance of big data in addressing longstanding inefficiencies and inequities in healthcare delivery. In the realm of price transparency, big data enables patients, providers, and policymakers to access and interpret complex cost structures, thereby enhancing decision-making and promoting market competition. Predictive analytics and AI models contribute significantly to resource forecasting by optimizing hospital operations, managing inventory, and anticipating patient needs. These capabilities have become even more critical during global health emergencies, such as the COVID-19 pandemic, where real-time data analytics supported early detection, patient triage, and vaccine distribution. The review also examines the theoretical frameworks of artificial intelligence, business analytics, and digital transformation, elucidating how they collectively shape data-centric reform. Additionally, it delves into the ethical challenges of post-pandemic AI deployment, including data privacy, algorithmic bias, and governance concerns. Ultimately, the paper proposes strategic recommendations for policymakers, healthcare administrators, and technology stakeholders aimed at fostering organizational agility, ethical AI integration, and sustainable innovation. As healthcare moves toward a more data-intensive future, this reform is not merely a technological shift but a systemic reimagining of how care is delivered, managed, and improved.