AI Insight
This study evaluated ChatMRPT, a large language model-assisted tool designed to help Nigerian malaria program officers prioritize distribution of insecticide-treated nets to high-risk populations. Through workshops and evaluations with 34 state-level malaria program officers from 28 Nigerian states, researchers found that 58% of participants had both sufficient digital competency and workplace resources to use the tool effectively, while digital competency appeared more critical than workplace resources alone for successful tool adoption. The tool was positively evaluated after incorporating user requirements such as contextual guidance, operational decision support, and analytical assistance.
Why it matters
This research demonstrates the feasibility of integrating AI-assisted decision support tools into public health planning in resource-limited settings, particularly for malaria control in Nigeria, which has the world's highest malaria burden. The findings suggest that with targeted capacity building and basic infrastructure improvements like stable internet access, LLM tools could enhance evidence-based allocation of limited health resources in low- and middle-income countries.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Background In Nigeria, the country with the greatest global malaria burden, funding constraints increasingly require insecticide-treated net (ITN) reprioritization to target those at highest risk. Large language models (LLM)- assisted decision-support tools may facilitate risk-informed ITN planning by supporting malaria programme officers in navigating analyses, interpreting outputs, and translating evidence into operational decisions. We developed ChatMRPT, an LLM-assisted ITN allocation planning tool based on user requirements, and analyzed post-interaction feedback, examined user digital competencies and workplace resources, and identified institutionalization pathways for LLM-assisted intervention planning. Methods A two-phase mixed-methods study began with software requirements gathering workshops (using a prototype) involving representatives from the National Malaria Elimination Programme (NMEP), State Malaria Elimination Programmes (SMEPs), and implementing partners. Phase 2two evaluated ChatMRPT through surveys, guided exercises, and focus group discussions with 34 SMEP officers from 28 Nigerian states. Quantitative data were analyzed using descriptive statistics and profile-based comparisons, while qualitative data were analyzed using reflexive thematic analysis to synthesize user experiences of ChatMRPT and identify institutionalization pathways. Findings Fifty-eight percent (19/33) of participants demonstrated both higher digital competency and adequate workplace resources; the remainder exhibited limitations in one or both domains ([4/33] higher competency/constrained resources; [6/33] higher resources/lower competency). Participants with higher digital competency but constrained workplace resources reported user experiences comparable to those with higher competency and adequate resources, whereas workplace resources alone did not appear to compensate for lower digital competency. Key software requirements included contextual guidance for malaria risk interpretation, operational decision support, and embedded analytical support. Following iterative incorporation of these requirements, ChatMRPT was positively evaluated across participant profiles. Participants viewed institutionalization as dependent on integration into routine malaria planning and adaptability to evolving programme priorities. Interpretation Many malaria programme officers may already have the foundational competency for LLM-assisted decision support. However, there is room to further strengthen digital competencies while facilitating access to basic workplace resources such as stable internet. Institutionalization of LLM tools may depend on addressing these capacity and infrastructural constraints alongside designing explainable, integrated, and flexible systems. Future research should evaluate long-term integration, sustainability, and effectiveness in routine malaria planning. Funding This work was funded by the Bill and Melinda Gates Foundation (INV-036449) and the Center for Health Outcomes and Informatics Research (CHOIR), Loyola University Chicago. The funders had no role in the study design, data analysis, interpretation of findings, or preparation of the manuscript.