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ORIGINAL RESEARCH article

Front. Public Health
Sec. Public Health Policy
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1413529
This article is part of the Research Topic Toward a Decision-Centric Precision Public Health: Clinical, Operational, and Analytical Advances View all 10 articles

Screening for Frequent Hospitalization Risk among Community-dwelling Elderly between 2016 and 2023: Machine learning-driven item selection, scoring system development, and prospective validation

Provisionally accepted
Eman Leung Eman Leung 1,2Jingjing Guan Jingjing Guan 3Qingpeng Zhang Qingpeng Zhang 4Chun Cheung Ching Chun Cheung Ching 2Hiliary Yee Hiliary Yee 5Yilin Liu Yilin Liu 2Hang Sau Ng Hang Sau Ng 3Richard Xu Richard Xu 5Hector Wing Hong Tsang Hector Wing Hong Tsang 5Albert Lee Albert Lee 2Frank Chen Frank Chen 1*
  • 1 Department of Management Sciences, College of Business, City University of Hong Kong, Kowloon, Hong Kong, SAR China
  • 2 The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong Region, China
  • 3 Other, Hong Kong, China
  • 4 Musketeers Foundation Institute of Data Science, The University of Hong Kong, Pokfulam, Hong Kong Region, China
  • 5 Department of Rehabilitation Sciences, Faculty of Health and Social Sciences, Hong Kong Polytechnic University, Kowloon, Hong Kong, SAR China

The final, formatted version of the article will be published soon.

    Screening for frequent hospitalizations in the community can help prevent super-utilizers from growing in the inpatient population. However, the determinants of frequent hospitalizations have not been systematically examined, their operational definitions have been inconsistent, and screening among community members lacks tools. Nor do we know if what determined frequent hospitalizations before COVID-19 continued to be the determinant of frequent hospitalizations at the height of the pandemic. Hence, the current study aims to identify determinants of frequent hospitalization and their screening items developed from the Comprehensive Geriatric Assessment (CGA), as our 273-item CGA is too lengthy to administer in full in community or primary care settings. The stability of the identified determinants will be examined in terms of the prospective validity of pre-COVID-selected items administered at the height of the pandemic.Comprehensive Geriatric Assessments (CGAs) were administered between 2016 and 2018 in the homes of 1611 older adults aged 65+ years. Learning models were deployed to select CGA items to maximize the classification of different operational definitions of frequent hospitalizations, ranging from the most inclusive definition, wherein two or more hospitalizations over two years, to the most exclusive, wherein two or more hospitalizations must appear during year two,

    Keywords: Public Health: Preventive Medicine, health risk assessment, COVID-19, Patient Readmission, data science, Artificial Intelligence: Machine Learning & Deep Learning, Screening for Frequent Hospitalization Risk among Community-dwelling Older Adults between 2016 and 2023: Machine learning-driven item selection, scoring system development, and prospective validation

    Received: 07 Apr 2024; Accepted: 25 Oct 2024.

    Copyright: © 2024 Leung, Guan, Zhang, Ching, Yee, Liu, Ng, Xu, Tsang, Lee and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Frank Chen, Department of Management Sciences, College of Business, City University of Hong Kong, Kowloon, Hong Kong, SAR China

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