The Roles of AI Technologies in Reducing Hospital Readmission for Chronic Diseases: A Comprehensive Analysis

Farnaz Farid, Abubakar Bello, Farhad Ahamed, Fahima Hossain

Research output: Working paper


The complex progression, specialized care, and comorbidities of chronic diseases place a financial and health burden on society. In some instances, the high hospital readmission rate is also a result of chronic diseases. The readmission rates can be decreased if the quality of life for patients is increased and healthcare costs are reduced through effective management and preventive measures. As a result, healthcare professionals, service providers, and policy-makers are looking for innovative approaches to reduce healthcare costs while enhancing the quality of care. There is an emerging trend in deploying technological advancements like artificial intelligence (AI), the Internet of Things (IoT), sensors, wearables, social media, mobile apps, and genomics to decrease hospital readmissions. In some instances, predictive analytics, early warning systems, personal-ized care management, remote monitoring and Telehealth, decision support systems, patient education and engagement, and other areas of artificial intelligence and machine learning have outperformed traditional approaches in lowering hospital readmissions. Although AI effectively reduces readmission, there are potential risks if it is not combined with efficient interventions. Therefore, real-time monitoring and intervention are required for AI systems to increase patient safety and decrease hospital readmissions. To achieve autonomy, prevent harm, achieve fairness, and achieve explicability, the ethical principles relating to AI systems should be taken into ac-count. This study examines the significant impact on health and the economy of hospital read-missions for chronic diseases and the role of AI in reducing readmissions.
Original languageEnglish
Number of pages19
Publication statusPublished - 14 Jul 2023

Publication series


  • Hospital readmission reduction
  • Predictive modeling in healthcare
  • Patient-centered care and AI technologies
  • Data governance and privacy
  • Ethical considerations of AI in healthcare


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