TECHNOLOGY TRANSFER IN HEALTHCARE: LEVERAGING PREDICTIVE MODELS TO OPTIMIZE MEDICAL OUTCOMES

Objective: This study aims to apply a predictive model using a decision tree algorithm on real patient data to identify patterns related to hospital readmission rates, contributing to improved clinical decision-making and advancing technology transfer in healthcare. Theoretical Framework: Grounded in predictive analytics and healthcare management, the study leverages the decision tree algorithm's ability to provide interpretable outputs, enabling healthcare professionals to make informed decisions based on data-driven insights. Method: Real patient data was used to construct a decision tree model. The algorithm was trained to identify factors associated with clinical resubmission rates. Performance measures, such as accuracy, precision, and recall, were applied to validate the model's robustness and predictive capabilities. Results and Discussion: The decision tree successfully identified critical predictors of hospital readmissions, including patient characteristics and clinical factors. The model demonstrated high predictive accuracy, providing actionable insights for stratifying at-risk patients. These findings underline the model’s practical utility in supporting evidence-based interventions. Research Implications: For clinical practice, the model facilitates targeted care and resource allocation, reducing readmission rates. For healthcare management, it demonstrates the potential of technology transfer to integrate predictive models into routine workflows, enhancing efficiency and outcomes. Originality/Value: This study contributes to the growing body of research on predictive analytics in healthcare by showcasing the application of a decision tree model in real-world scenarios. Its emphasis on technology transfer highlights its practical significance, bridging the gap between research and implementation in clinical settings.