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Current Trends in Readmission Prediction: An Overview of Approaches

  • RESEARCH ARTICLE - SPECIAL ISSUE - AI based health-related Computing for COVID-19 (AIHRC)
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Abstract

Hospital readmission shortly after discharge threatens the quality of patient care and leads to increased medical care costs. In the United States, hospitals with high readmission rates are subject to federal financial penalties. This concern calls for incentives for healthcare facilities to reduce their readmission rates by predicting patients who are at high risk of readmission. Conventional practices involve the use of rule-based assessment scores and traditional statistical methods, such as logistic regression, in developing risk prediction models. The recent advancements in machine learning driven by improved computing power and sophisticated algorithms have the potential to produce highly accurate predictions. However, the value of such models could be overrated. Meanwhile, the use of other flexible models that leverage simple algorithms offer great transparency in terms of feature interpretation, which is beneficial in clinical settings. This work presents an overview of the current trends in risk prediction models developed in the field of readmission. The various techniques adopted by researchers in recent years are described, and the topic of whether complex models outperform simple ones in readmission risk stratification is investigated.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    Kareen Teo, Ching Wai Yong & Khin Wee Lai

  2. Department of Electrical Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    Joon Huang Chuah

  3. Department of Mechatronics and Biomedical Engineering, Universiti Tunku Abdul Rahman, 43000, Sungai Long, Malaysia

    Yan Chai Hum & Yee Kai Tee

  4. Changshu Institute of Technology, Changshu, 215500, Jiangsu, China

    Kaijian Xia

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Correspondence to Khin Wee Lai.

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This work was supported by the 2020 EBC-C (Extra-Budgetary Contributions from China) Project on Promoting the Use of ICT for Achievement of Sustainable Development Goals, and University Malaya under grant IF015-2021.

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Teo, K., Yong, C.W., Chuah, J.H. et al. Current Trends in Readmission Prediction: An Overview of Approaches. Arab J Sci Eng 48, 11117–11134 (2023). https://doi.org/10.1007/s13369-021-06040-5

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