AI Technologies in Reducing Hospital Readmission for Chronic Diseases: A Recommended Framework

The complex progression, specialised 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. 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 readmission. In some instances, predictive analytics, early warning systems, personalised 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 readmission. However, the ethical principles relating to AI systems should be considered to achieve autonomy, prevent harm, achieve fairness, and achieve explainability. To this end, this study examines the significant impact on health and the economy of hospital readmission for chronic diseases and the role of AI in reducing readmission. Moreover, we analyse the existing challenges of AI technologies hindering them from reaching their full potential. We further scrutinise these challenges and potential solutions in the context of a fictional case study to provide guidelines for future research endeavours in this context.

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Published
2024-11-20
DOI
https://doi.org/10.1109/fmlds63805.2024.00022
Citations
3
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
1.04
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article

AI Technologies in Reducing Hospital Readmission for Chronic Diseases: A Recommended Framework

Farhad Ahamed, Shaleeza Sohail, Fahima Hossain, Farnaz Farid et al.
3 citations
Machine Learning in Healthcare
1.04
article

AI Technologies in Reducing Hospital Readmission for Chronic Diseases: A Recommended Framework

Farhad Ahamed, Shaleeza Sohail, Fahima Hossain, Farnaz Farid, Fariza Sabrina, Abubakar Bello
article en
3 citations

Abstract

The complex progression, specialised 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. 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 readmission. In some instances, predictive analytics, early warning systems, personalised 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 readmission. However, the ethical principles relating to AI systems should be considered to achieve autonomy, prevent harm, achieve fairness, and achieve explainability. To this end, this study examines the significant impact on health and the economy of hospital readmission for chronic diseases and the role of AI in reducing readmission. Moreover, we analyse the existing challenges of AI technologies hindering them from reaching their full potential. We further scrutinise these challenges and potential solutions in the context of a fictional case study to provide guidelines for future research endeavours in this context.

Edge Hill University (GB), Hamdard University Bangladesh (BD), Western Sydney University (AU), Central Queensland University (AU), University of Newcastle Australia (AU)
Openalex Percentile: Top 18%
Machine Learning in Healthcare
1.04
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AI Technologies in Reducing Hospital Readmission for Chronic Diseases: A Recommended Framework — Farhad Ahamed, Shaleeza Sohail, et al. · (2024) | TGRS Research Map | TGRS