This project focuses on developing predictive models using machine learning to estimate hospital length of stay for patients undergoing hip or knee arthroplasty. By analyzing a wide range of clinical and demographic data, the models aim to provide accurate predictions, enabling more efficient patient management in high-volume surgical centers. Key insights from multivariate data analysis have highlighted factors influencing postoperative hospital stays, offering valuable information for personalized interventions.
The implementation of these models within our hospital has validated their reliability and demonstrated their potential for broader application. Integrated into electronic health record systems, these AI tools can serve as decision support aids, assisting surgeons in patient selection while complementing the shared decision-making process. This approach ensures AI augments clinical judgment rather than replacing it.
The results underscore the transformative potential of AI in orthopedics, fostering more personalized, efficient care pathways. By reducing hospital stays and improving patient outcomes, this initiative contributes to a more sustainable healthcare system with optimized resource utilization.