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Applying Smart Healthcare and ESG Concepts to Optimize Elderly Health Management

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  • Feng-Yi Lin

    (College of Management, National Taipei University of Technology, Taipei 106, Taiwan)

  • Chin-Chiu Lee

    (Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan)

  • Te-Nien Chien

    (College of Management, National Taipei University of Technology, Taipei 106, Taiwan)

Abstract

As the aging population grows, ensuring effective and sustainable health management for elderly individuals has become a critical challenge. This study explores the integration of smart healthcare technologies and ESG (Environmental, Social, and Governance) principles to enhance elderly health management through data-driven strategies. Using the MIMIC-III database, this study evaluates five machine learning models (Adaboost, Bagging, Catboost, GaussianNB, and SVC) through ten-fold cross-validation to predict 3-day and 30-day mortality rates among elderly ICU patients. The Bagging model achieved the best performance with an AUROC of 0.80, demonstrating the potential of smart healthcare in mortality prediction. These technologies enhance predictive accuracy, enabling the timely identification of high-risk patients and effective intervention. Through the application of smart data integration methods, this study demonstrates how combining clinical indicators with socioeconomic factors can improve healthcare equity and efficiency. Furthermore, by aligning smart healthcare development with ESG concepts, we emphasize the importance of sustainability, social responsibility, and governance transparency in future healthcare systems. The findings offer valuable contributions toward building an interoperable and ethical health ecosystem, supporting early risk identification, improved care outcomes, and the promotion of healthy living for the elderly population.

Suggested Citation

  • Feng-Yi Lin & Chin-Chiu Lee & Te-Nien Chien, 2025. "Applying Smart Healthcare and ESG Concepts to Optimize Elderly Health Management," Sustainability, MDPI, vol. 17(13), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:13:p:6091-:d:1693711
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    References listed on IDEAS

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    1. Xinyue Zhang & Xiaolu Gao & Danxian Wu & Zening Xu & Hongjie Wang, 2021. "The Role of Big Data in Aging and Older People’s Health Research: A Systematic Review and Ecological Framework," Sustainability, MDPI, vol. 13(21), pages 1-19, October.
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