AI-Powered Post-Discharge Monitoring to Prevent Patients Readmissions and Reduce Workforce Burden

Abstract Background Patient readmission poses a significant burden on healthcare systems, straining resources, negatively impacting patient treatment, and it is often a Public Health problem. Traditional post-discharge follow-up methods, such as phone calls, often lack efficiency and personalization. Artificial intelligence (e.g., Machine Learning (ML)) offers a transformative approach that can potentially improve post-discharge care. Methods This scoping review aimed to map existing research on AI-powered solutions for post-discharge monitoring, focusing on their application in risk stratification and their potential for reducing hospital readmissions within a public health framework. Following PRISMA-ScR guidelines, a search was conducted across four databases using keywords related to AI, post-discharge, and risk stratification. Results Studies published between 2018 and January 2024 were included if they described the development and application of an ML model in a post-discharge setting for risk stratification. Sixteen studies met the inclusion criteria. Tree-based algorithms, particularly XGBoost, were the predominant ML approach, with promising performance metrics for readmission risk prediction. Conclusions However, limitations were identified, including restricted generalizability due to data source limitations and a lack of real-time implementation in many studies. This review highlights the potential of AI in post-discharge monitoring, particularly the use of ML for risk stratification, as a promising tool for public health contact tracing. While challenges remain regarding generalizability and real-world implementation, the findings suggest AI holds immense promise for improving post-discharge care, potentially reducing readmissions, and optimizing resource allocation. Future research with broader datasets and real-time applications can further solidify the role of AI in revolutionizing post-discharge care within public health systems. Key messages • This study shows the potential of AI and risk stratification in post-discharge monitoring, improving public health outcomes by reducing hospital readmissions and optimizing the healthcare workforce. • This review highlights the value of AI in public health, particularly for improving post-discharge care through effective prediction of high-risk readmissions.