Institutionalizing LLM-assisted decision support for malaria risk-focused ITN reprioritization in Nigeria: Digital competency, workplace resource profiles, experiences, and pathways to routine integration
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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 two 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.
Research in context
Evidence before this study
We searched PubMed and Google Scholar for studies published from 2022 onwards using combinations of terms related to LLMs, decision support, malaria planning, implementation, and insecticide-treated nets. Previous studies have integrated epidemiological, environmental, socioeconomic, and operational data to support malaria risk mapping, intervention targeting, and resource allocation, including under resource constraints.
Added value of this study
We make three contributions to the evidence based on the use of LLM-assisted tools for malaria intervention planning. First, we describe variation in digital competency and workplace resources and how these relate to user experiences with LLM-assisted tools. Second, we elucidate software design requirements for enhancement of interpretability, contextual exploration, workflow integration, and operational decision support. Third, we identify organizational, technical, and governance conditions shaping institutionalization, including interoperability, leadership support, workflow integration, and adaptability across implementation contexts. Together, these findings inform the design and integration of LLM-assisted decision-support tools for routine malaria programme planning.
Implications of all the available evidence
Successful implementation of LLM-assisted risk-informed ITN planning requires stakeholders to interpret and apply analytical outputs within routine planning systems. While previous studies have focused on predictive modelling, optimization, and risk mapping, our findings highlight interpretive support, workflow integration, and stakeholder interaction in translating analytical outputs into planning decisions. Institutionalization further requires stakeholder-centred design, interoperability, organizational support, and capacity strengthening, building on foundational capacity already present within many malaria programmes.
