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Enablers, Challenges, and Lessons Learned From a Digital Health Intervention (Sehatmandi App) in Afghanistan: Qualitative Study

Background: In low- and middle-income countries, maternal, newborn, and child health face significant challenges due to infrastructure limitations, access disparities, and service delivery inefficiencies. The Sehatmandi mobile health (mHealth) app was deployed in 2018 to address these issues across 189 health facilities in Afghanistan's Bamyan and Badakhshan provinces. This app aims to enhance service provision through real-time data monitoring, improved accountability, and performance-based health system strengthening. Objective: This study aims to explore the enablers, challenges, and lessons learned for the sustainability of the Sehatmandi mHealth intervention from the perspective of key stakeholders to inform the future scaling of digital health tools in fragile and resource-constrained settings. Methods: A qualitative study was conducted between June and July 2024 involving 24 in-depth interviews with stakeholders, including health facility managers, administrators, and high-level decision-makers. Participants were selected using stratified purposive sampling to ensure diverse facility representation. Interviews were conducted in person or virtually by using a semistructured guide, recorded, transcribed and translated into English. Thematic content analysis was performed using NVivo version 11 software. Ethics approval was obtained, and informed consent was secured from all the participants. Results: Stakeholders reported that Sehatmandi improved health system responsiveness by enhancing performance monitoring, accountability, timely reporting, and data-driven decision-making. Offline data entry was identified as a critical feature, enabling data collection in remote areas without internet access and ensuring synchronization when connectivity was resumed. However, several barriers affected the long-term sustainability: poor internet connectivity, electricity shortages,inadequate technical support, and high staff turnover, which disrupted functionality and data quality. Training gaps and insufficient supervision further hampered consistent and effective use. Participants emphasized the need for structured capacity building, regular follow-up, and sustainable funding to maintain the intervention. Integration with national health information systems and alignment with broader digital health strategies were also seen as prerequisites for scaling and institutionalization. Conclusions: The Sehatmandi mHealth intervention demonstrated enhanced performance monitoring and accountability across health care facilities in Afghanistan’s conflict-affected settings. However, for digital health interventions to be sustainable and scalable in low- and middle-income countries, foundational investments in digital infrastructure, continuous training and monitoring, system-level integration, and long-term funding are essential. These findings provide actionable insights for governments, implementers, and donors aiming to strengthen health systems through digital innovation in fragile settings.

Nurses’ Perspectives on Evidence Dissemination Barriers and Large Language Model–Based Support: Qualitative Study Using Focus Groups and Nominal Group Technique

Background: Current evidence dissemination methods fall short of meeting clinical nurses’ needs, hindering the implementation of evidence-based nursing practice. Large language models (LLMs), with their advanced natural language processing capabilities, offer potential as innovative tools to facilitate evidence dissemination. However, general-purpose LLMs typically lack domain-specific knowledge, are insufficient to support effective evidence dissemination in clinical contexts. It is essential to develop artificial intelligence tools tailored to nurses’ needs and preferences to enhance evidence dissemination. Objective: The aim of this study is to identify the challenges and barriers clinical nurses face in disseminating evidence, examine their perspectives on the use of existing LLMs to support evidence dissemination, and explore their needs and preferences regarding an LLM-based nursing evidence question-answering system. Methods: This qualitative study used a combined method of focus group discussions and the nominal group technique (NGT). Using purposive sampling, nurses with diverse specialties, professional titles, and years of experience were recruited, resulting in a total of 22 clinical nurses who completed the entire study. A total of 2 focus group discussions were conducted online via Tencent Meeting between November and December 2024 to explore the challenges and barriers nurses face in disseminating evidence, as well as their perspectives on using existing LLMs to support evidence dissemination. The data were analyzed using qualitative content analysis following the approach of Graneheim and Lundman. Subsequently, the NGT was used between March and April 2025 to identify nurses’ needs and preferences for the system to be developed. To overcome geographical constraints and participants’ busy schedules, the NGT was conducted entirely online, using online questionnaires and WeChat groups. Overall, 2 rounds of voting were conducted to determine the priority ranking of the functionalities. Results: The focus group yielded 3 main themes and 7 subthemes. Three main themes were identified as (1) pathways for evidence dissemination among nurses, (2) barriers that hinder the effective dissemination of evidence, and (3) advantages and limitations of using LLMs to support evidence dissemination. The limitations of current LLMs served as the foundation for nurses’ subsequent reflections in the nominal group discussions on the desired functions of a newly developed LLM. The NGT sessions ultimately identified 9 desired functions. After prioritization, the top 3 ranked functions were evidence-based, high-quality question-answering, evidence source provision, and personalized evidence recommendation. Conclusions: The current evidence dissemination process faces multiple barriers. LLMs hold promise as innovative tools to support evidence dissemination, but require further refinement. Clinical nurses have identified key functional needs, guiding the development of LLMs specifically tailored to clinical nursing practice.
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