Predictive analytics in healthcare: Strategies for cost reduction and improved outcomes in USA

Abstract

Healthcare expenses are a major issue all around the world, and the US spends the most on healthcare.  Predictive analytics, a sophisticated field of data science that incorporating machine learning, AI, and big data, is becoming more used in hospitals for predicting clinical events, improving resource use, and cutting down on wasteful costs. This study examines the mechanisms by which predictive analytics decreases healthcare expenditures.  Technology examines methods to leverage technological advancements for early disease detection, prevent hospital readmissions, reduce emergency department utilization, enhance labor efficiency and supply chain management, and identify fraudulent activities. The study conducts a thorough literature evaluation of peer-reviewed articles from 2020 to 2024, supplemented by case-based cost modeling.  Quantitative studies encompass speculative cost-saving models derived from predicted readmission prevention programs, patient triage systems, and fraud analytics initiatives.  We consider about moral concerns including being fair, being honest, and keeping information private. Evidence shows that predictive analytics can cut readmissions by as much as 25%, cut emergency department visits by 15%, save 12% in labor costs by optimizing staffing, and stop billions of dollars in fraud. The graphs and tables show ways to lower costs and improve predictive accuracy. Predictive analytics signifies a transformative shift from reactive to proactive healthcare. There are problems with interoperability, bias, and adoption barriers, but its potential to save money is clear. Globally, predictive models will be key to creating healthcare systems that are sustainable, patient-centered, and cost-effective.