Leveraging Health Information Systems and Predictive Analytics to Improve Patient Outcomes: A Data-Driven Approach

Predictive analytics and health information systems (HIS) have become the focus of the current healthcare revolution that provides unprecedented potential to enhance patient outcomes, streamline clinical processes, and lower the increasing cost of care. Although electronic health records (EHRs) and claims-based databases are widely adopted, a number of healthcare organizations still experience difficulties in extracting practical insights off their data resources, especially when it comes to anticipating high-risk events like hospital readmissions. The present research uses a data-intensive method to investigate the role of combined HIS infrastructures in conjunction with cutting-edge predictive modeling methods in positively influencing clinical decision-making and minimizing preventable readmissions. Based on the real-world EHRs and claims datasets, the study creates a multi-layered analytics pipeline covering data extraction, data standardization via HL7 FHIR and ICD-10 coding systems, and feature engineering, and model development with logistic regression, random forest, and gradient boosting algorithms. To assess the clinical reliability of the model, AUC, precision, recall, and F1-score were used to compare model performance. The results indicate that predictive models based on harmonized HIS data are capable of identifying high-risk patients with a high level of discriminatory power, which allows hospitals to apply timely interventions, allocate resources more effectively, and save the cost-per -patient on preventive care measures. In addition, the operational framework that is suggested in this study offers a blueprint of integration of predictive analytics to the clinical routine working process that can be scaled. The paper provides a contribution to the existing literature by connecting health information systems engineering to applied data science and providing an evidence-based roadmap of health organizations wishing to operationalize patient outcome improvement using analytics. The study presents important implications to clinical practice, hospital management as well as health policy especially in the value-based and population health care models.