INTELLIGENT AUTOMATION AND PREDICTIVE TECHNOLOGIES IN HOSPITALS: ADOPTION TRENDS, BENEFITS, AND IMPLEMENTATION CHALLENGES

Abstract

Patient care, clinical decision-making, and safety outcomes are being revolutionized by hospitals' integration of intelligent automation and predictive technologies.  The purpose of this descriptive study is to examine the current state of predictive analytics adoption, its potential advantages, and the difficulties inherent in its implementation in healthcare facilities.  Based on simple random sampling, the analysis is based on a sample of 170 patients from departments that handle high-risk illnesses like sepsis, heart failure, diabetes, and pneumonia.  Hospital performance reports, clinical dashboards, and electronic health records gather data that captures qualitative and quantitative aspects of care.  Patient demographics and baseline clinical indicators are summarized using descriptive statistics, and outcomes like readmission rates, medication errors, ICU transfers, and patient satisfaction are compared before and after the adoption of predictive models using inferential tests (t-tests and chi-square).  Statistical study, carried out using SPSS and a significance level of 0.05, offers solid proof of how predictive tools have affected healthcare delivery.  The results show that hospitals can enhance efficiency and safety, but they also mention the difficulties they have when trying to implement the changes, such as issues with data integration, staff preparedness, and workflow adaption.  In sum, the research provides a data-driven view of how predictive technologies might improve medical care systems and patient safety.