Bridging the Digital Divide in Disaster Nursing: A Systematic Review of AI and Telehealth Adoption in Low-Resource Settings

Abstract

The integration of Artificial Intelligence (AI) and telehealth has significantly transformed disaster response capabil- ities. Nonetheless, a pronounced "digital divide" poses a risk of exacerbating health inequities, particularly in Low- and Middle-Income Countries (LMICs), where disaster vulnerability is most pronounced. Objective: This systematic review seeks to examine the adoption of digital health technologies in disaster nursing, identifying socio-technical barriers and facilitators through the application of the NASSS (Non-adoption, Abandonment, Scale-up, Spread, and Sustainability) framework. Methods: In accordance with PRISMA 2020 guidelines, a systematic search was conducted across Scopus, Web of Science, PubMed, and CINAHL for articles published between 2020 and 2025. Studies focusing on nursing roles in disaster contexts were included. The quality of the studies was assessed using the Mixed Methods Appraisal Tool (MMAT). Results: A total of 42 studies were synthesized. The review revealed a stark dichotomy: High-Income Countries (HICs) prioritized AI-driven predictive modeling and data privacy, whereas LMICs concentrated on basic connectivity and mHealth solutions. Key barriers in low-resource settings included infrastructural deficits (unstable power/internet), lack of digital literacy among frontline nurses, and unsustainable pilot projects. Conclusion: While digital health holds immense potential, its current implementa- tion is inequitable. To bridge the digital divide, future interventions must prioritize "frugal innovation" resilient, offline-capable technologies designed for resource-constrained environments rather than uncritically importing complex systems from developed nations. Policy frameworks must also address the foundational digital literacy of the nursing workforce.

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