A NASSS-based framework for AI-driven prevention of non-communicable diseases in Iran: a qualitative study.
7 affiliations
- 1Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran.
- 2Health Policy and Management Research Center, Department of Health Management and Economics, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran. [email protected].
- 3Department of Health in Disasters and Emergencies, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
- 4Minimally Invasive Surgery Research Center, Iran University of Medical Sciences, Tehran, Iran.
- 5IUMS cellular and molecular research center, Tehran, Iran.
- 6School of medicine and dentistry, Griffin university, Gold coast, Logan, 4222, QLD, Australia.
- 7Health Human Resources Research Center, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran. [email protected].
Abstract
Background
Non-Communicable Diseases (NCDs) are recognized as the leading cause of mortality worldwide, accounting for approximately 74% of all deaths. In Iran, this figure exceeds 80%, underscoring the critical need for innovative preventive strategies. Artificial Intelligence (AI), with its capabilities in early detection, risk prediction, and the design of targeted interventions, holds significant potential for mitigating the burden of NCDs. However, the successful integration of AI into a health system necessitates a thorough understanding of local cultural, organizational, and structural contexts. Grounded in the NASSS (Non-adoption, Abandonment, Scale-up, Spread, and Sustainability) theoretical model, this study aimed to design and validate a localized framework for the application of AI in NCD prevention within the Iranian health system.Method
This study employed an exploratory-sequential qualitative design to develop and validate a localized framework for the application of AI in preventing Non-Communicable Diseases (NCDs) within the Iranian health system. In the initial phase, data from 34 semi-structured interviews with experts in health, technology, ethics, and policy were analyzed using a hybrid inductive-deductive thematic analysis. In the subsequent phase, the preliminary framework was refined and finalized using a modified Delphi method and two focus group discussions with 13 experts, yielding a high overall content validity index of 0.91.Results
Analysis guided by the NASSS model identified five principal themes and 19 initial subthemes. Following Delphi validation and framework refinement, the final framework comprised five major dimensions and 18 operational components tailored to the Iranian health system. The framework emphasizes algorithmic transparency, data standardization, digital health literacy, and workforce empowerment as key requirements for successful AI implementation in NCD prevention.Conclusions
The finalized framework provides a locally validated model for the ethical, sustainable, and context-sensitive integration of AI into NCD prevention. It offers practical guidance for policymakers, health system managers, and researchers in Iran and other countries with similar healthcare contexts. The framework may assist decision-makers in prioritizing digital infrastructure investments, strengthening data governance, and implementing context-sensitive AI strategies for NCD prevention.
Related
- Community-driven interventions for the prevention of non-communicable diseases (NCDs) in urban South Asia: a scoping review
- Comparative policy analysis of national frameworks for the prevention of non-communicable diseases and injuries in children and adolescents
- Moving towards integrated people-centred primary healthcare for non-communicable diseases in Bangladesh: a qualitative study
- Exploring the potential of mRNA vaccines in combatting communicable and non-communicable diseases in low-income and lower-middle-income countries
- Burden of Non-Communicable Diseases Attributable to Polygenic Risk and Its Association with Healthcare Utilization in Taiwan
BibTeX
@article{hadian2026nasssbased,
title = {{A NASSS-based framework for AI-driven prevention of non-communicable diseases in Iran: a qualitative study.}},
author = {Marziye Hadian and Aziz Rezapour and Asgar Aghaei Hashjin and Nasrin Abolhasanbeigi Gallehzan and Mohammadreza Jabbari Khanbebin and Tahereh Shafaghat and Mohsen Nouri and Ali Sarabi Asiabar and Pezhman Atafimanesh and Mahan Mohammadi and Abdosaleh Jafari},
journal = {Journal of health, population, and nutrition},
year = {2026},
month = jul,
doi = {10.1186/s41043-026-01379-7},
url = {https://europepmc.org/article/MED/42928357},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - A NASSS-based framework for AI-driven prevention of non-communicable diseases in Iran: a qualitative study.
AU - Marziye Hadian
AU - Aziz Rezapour
AU - Asgar Aghaei Hashjin
AU - Nasrin Abolhasanbeigi Gallehzan
AU - Mohammadreza Jabbari Khanbebin
AU - Tahereh Shafaghat
AU - Mohsen Nouri
AU - Ali Sarabi Asiabar
AU - Pezhman Atafimanesh
AU - Mahan Mohammadi
AU - Abdosaleh Jafari
PY - 2026
DA - 2026/07/23
JO - Journal of health, population, and nutrition
DO - 10.1186/s41043-026-01379-7
UR - https://europepmc.org/article/MED/42928357
AB - Background Non-Communicable Diseases (NCDs) are recognized as the leading cause of mortality worldwide, accounting for approximately 74% of all deaths. In Iran, this figure exceeds 80%, underscoring the critical need for innovative preventive strategies. Artificial Intelligence (AI), with its capabilities in early detection, risk prediction, and the design of targeted interventions, holds significant potential for mitigating the burden of NCDs. However, the successful integration of AI into a health system necessitates a thorough understanding of local cultural, organizational, and structural contexts. Grounded in the NASSS (Non-adoption, Abandonment, Scale-up, Spread, and Sustainability) theoretical model, this study aimed to design and validate a localized framework for the application of AI in NCD prevention within the Iranian health system.Method This study employed an exploratory-sequential qualitative design to develop and validate a localized framework for the application of AI in preventing Non-Communicable Diseases (NCDs) within the Iranian health system. In the initial phase, data from 34 semi-structured interviews with experts in health, technology, ethics, and policy were analyzed using a hybrid inductive-deductive thematic analysis. In the subsequent phase, the preliminary framework was refined and finalized using a modified Delphi method and two focus group discussions with 13 experts, yielding a high overall content validity index of 0.91.Results Analysis guided by the NASSS model identified five principal themes and 19 initial subthemes. Following Delphi validation and framework refinement, the final framework comprised five major dimensions and 18 operational components tailored to the Iranian health system. The framework emphasizes algorithmic transparency, data standardization, digital health literacy, and workforce empowerment as key requirements for successful AI implementation in NCD prevention.Conclusions The finalized framework provides a locally validated model for the ethical, sustainable, and context-sensitive integration of AI into NCD prevention. It offers practical guidance for policymakers, health system managers, and researchers in Iran and other countries with similar healthcare contexts. The framework may assist decision-makers in prioritizing digital infrastructure investments, strengthening data governance, and implementing context-sensitive AI strategies for NCD prevention.
SN - 1606-0997
SN - 2072-1315
ER -