Data-Driven Decision-Making in Healthcare Leveraging Predictive Analytics for Improved Patient Outcomes

Abstract

Data-driven decision-making in healthcare is transforming patient outcomes by leveraging predictive analytics to anticipate and respond to individual health needs. This research explores the application of predictive analytics in improving patient care through early diagnosis, risk assessment, personalized treatment plans, and real-time monitoring. By analyzing large datasets from electronic health records (EHRs), genetic information, and clinical data, predictive models can identify at-risk patients, optimize resource allocation, and support timely medical interventions. This paper examines current trends and case studies in predictive analytics within healthcare and discusses challenges such as data privacy, integration across systems, and model interpretability. The findings suggest that predictive analytics, when integrated with clinical workflows, holds substantial promise in enhancing healthcare quality, efficiency, and patient-centric care.