Proposing a Model for Designing Smart Health-Oriented Products and Services for the Elderly Based on Cognitive Factors

Ali   Saei; Mohammad Hossein Karimi Gavareshki; Reza  Hosnavi Atashgah; Hassan   Torabi; Mohammad Reza  Zahedi

doi:10.61838/kman.ijecs.220

Authors

Ali Saei Ph.D. student, Department of Industrial Engineering, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran Author https://orcid.org/0009-0006-2421-4144
Mohammad Hossein Karimi Gavareshki Associate Professor, Department of Industrial Engineering, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran https://orcid.org/0009-0007-2800-1119
Reza Hosnavi Atashgah Professor, Department of Industrial Engineering, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran Author https://orcid.org/0009-0006-5171-0397
Hassan Torabi Assistant Professor, Department of Industrial Engineering, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran Author https://orcid.org/0000-0002-2225-7793
Mohammad Reza Zahedi Associate Professor, Department of Industrial Engineering, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran Author https://orcid.org/0000-0001-8006-5063

DOI:

https://doi.org/10.61838/kman.ijecs.220

Keywords:

Smart Product-Service System, Elderly, Insurance System, Cognitive Factors, Thematic Analysis

Abstract

Purpose: This study aimed to design and validate a cognitive-based smart health-oriented product–service system (PSS) model tailored for elderly care in social security organizations.

Methods and Materials: A qualitative exploratory approach was employed to identify and structure the key dimensions, components, and indicators of the proposed model. Data were collected through semi-structured interviews with 12 experts, including managers, health technology specialists, university faculty, and policymakers in the field of elderly care. Thematic analysis was applied to the transcribed interview data using a two-level coding process—textual and conceptual. The final model was synthesized through iterative categorization and expert consensus validation, comprising both functional system elements and a quality evaluation framework.

Findings: The results identified four core dimensions of the smart PSS: (1) business model, (2) software (cloud) platform, (3) cognitive–biological factors, and (4) physical platform. Each dimension consists of multiple components (totaling 16) and operational parameters (61 indicators). Additionally, a multidimensional quality assessment framework was developed with four evaluation constructs: outcome quality, interaction quality, system quality, and stakeholder satisfaction. The model emphasizes user-centricity, real-time personalization, integration of digital infrastructure, and AI-driven monitoring. Elderly users were positioned as co-creators of value, with service adaptation based on continuous feedback and emotional-cognitive profiling. The model integrates both physical and cyber infrastructure to support comprehensive care delivery.

Conclusion: The proposed smart health-oriented PSS model offers a comprehensive, adaptable, and intelligent framework for improving elderly care services in social security contexts.

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References

Bai, W. (2023). Research on Harbin Smart Community-Based Elderly Care Services. Lecture Notes in Education Psychology and Public Media, 30(1), 106-111. https://doi.org/10.54254/2753-7048/30/20231590

Chen, Z., Liu, J., & Zheng, Y. (2022). Evolutionary Game Analysis of Community Elderly Care Service Regulation in the Context of “Internet +”. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.1093451

Cheng, T., Liu, C., Yang, H., Wang, N., & Liu, Y. (2022). From service capacity to spatial equity: Exploring a multi-stage decision-making approach for optimizing elderly-care facility distribution in the city centre of Tianjin, China. Sustainable Cities and Society, 85, 104076. https://doi.org/10.1016/j.scs.2022.104076

Das, S., Adhikary, A., Laghari, A. A., & Mitra, S. (2023). Eldo-care: EEG with Kinect sensor based telehealthcare for the disabled and the elderly. Neuroscience Informatics, 3(2), 100130. https://doi.org/10.1016/j.neuri.2023.100130

Fukunishi, H., & Kobayashi, Y. (2023). Care-needs level prediction for elderly long-term care using insurance claims data. Informatics in Medicine Unlocked, 41, 101321. https://doi.org/10.1016/j.imu.2023.101321

Lam, H. Y., Ho, G. T. S., Mo, D. Y., & Tang, V. (2021). Enhancing Data-Driven Elderly Appointment Services in Domestic Care Communities Under COVID-19. Industrial Management & Data Systems, 121(7), 1552-1576. https://doi.org/10.1108/imds-07-2020-0392

Li, M., Ao, Y., Peng, P., Bahmani, H., Han, L., Zhou, Z., & Li, Q. (2023). Resource allocation of rural institutional elderly care in China's new era: spatial-temporal differences and adaptation development. Public Health, 223, 7-14. https://doi.org/10.1016/j.puhe.2023.07.005

Li, Y. (2022). Social care for disabled elderly women in urban China: The roles of the community. Social Science & Medicine, 314, 115473. https://doi.org/10.1016/j.socscimed.2022.115473

Liu, R. (2024). Research and Design of One-Stop Intelligent Elderly Care Comprehensive Service Platform Based on Event-Driven Architecture. Highlights in Business Economics and Management, 29, 169-178. https://doi.org/10.54097/bfnxdv45

Nga, T. T. T. (2023). Factors Affecting the Intention to Use Digital Banking Services: A Case Study on Elderly Customers in Vietnam. Higher School of Economics Economic Journal, 27(2), 270-289. https://doi.org/10.17323/1813-8691-2023-27-2-270-289

Peng, Y., Dong, L., Liang, H., Yang, L., Jiang, S., Tuljapurkar, S., & Guo, Z. (2021). The changing trend of life expectancy for the Chinese elderly and its rural–urban disparity. China Population and Development Studies, 5(1), 25-40. https://doi.org/10.1007/s42379-021-00080-y

Thalib, B. (2022). The Use of Teledentistry and Artificial Intelligence (AI) in Dental and Oral Health Services for the Elderly in the Era of the Covid-19 Pandemic: A Systematic Review. Journal of Dentomaxillofacial Science, 7(3). https://doi.org/10.15562/jdmfs.v7i3.1406

Wang, W. (2024). A Survey on the Use of Intelligent Physical Service Robots for Elderly or Disabled Bedridden People at Home. International Journal of Crowd Science, 8(2), 88-94. https://doi.org/10.26599/ijcs.2024.9100005

Zhao, Y. (2024). Opportunities and Challenges of Integrating Artificial Intelligence in China's Elderly Care Services. Scientific reports, 14(1). https://doi.org/10.1038/s41598-024-60067-w

Zhou, H.-X., Zeng, G.-Q., Ren, Z., Han, X. F., & Tang, J. (2022). Research on the Sustainable Development Matrix of Home-Based Elderly Care Services in Free Trade Ports From the Perspective of Artificial Intelligence. https://doi.org/10.2991/978-94-6463-108-1_14

Zhou, L., Ju, P., Li, Y., Liu, B., Wang, Y., Zhang, X., & Yin, H. (2023). Preventive health behaviors among the middle-aged and elderly in China: Does social capital matter? Preventive Medicine Reports, 35, 102329. https://doi.org/10.1016/j.pmedr.2023.102329

Zhou, X., & Cao, K. (2023). Spatial multi-objective optimization of institutional elderly-care facilities: A case study in Shanghai. International Journal of Applied Earth Observation and Geoinformation, 122, 103436. https://doi.org/10.1016/j.jag.2023.103436

Zhou, Z., Wang, L., & Dong, Y. (2023). Research on innovative design of community mutual aid elderly care service platform based on Kano model. Heliyon, 9(5). https://doi.org/10.1016/j.heliyon.2023.e15546