A Study to Evaluate the Impact of Predictive Analytics on Clinical Performance and Patient Safety

This paper examines the role of predictive analytics in advancing patient care and safety outcomes within hospital settings.Employing a descriptive research design, the study systematically observes and documents trends in clinical performance indicators by comparing outcomes before and after the implementation of predictive tools.The focus is on real-world hospital data to generate practical insights into how predictive models enhance patient safety, care efficiency, and overall health outcomes.A total of 170 patients are selected through simple random sampling from departments that actively utilize predictive models to manage high-risk conditions such as sepsis, heart failure, diabetes, and pneumonia.These conditions are chosen due to their prevalence and responsiveness to early intervention.Data is gathered from reliable sources including electronic health records (EHRs), clinical dashboards, and hospital performance reports, capturing both quantitative and qualitative aspects of patient care.Descriptive statistics summarize key variables while inferential statistical tests-including t-tests and chi-square tests-determine the significance of observed changes in metrics like readmission rates, medication errors, ICU transfers, and patient satisfaction.The findings offer empirical evidence on the effectiveness of predictive analytics in improving clinical outcomes and support the growing role of data-driven strategies in proactive and informed medical decision-making.This study contributes to the ongoing advancement of healthcare delivery through technology-enabled patient safety and care optimization.