CHNRI-B: integrating bibliometric, patent and public-funding intelligence into health research priority setting - a methodological framework for capacity-sensitive priority setting

Technical Summary: CHNRI-B Framework

Problem Statement
Health research priority setting (RPS) faces a critical gap between transparent scoring mechanisms and the transparency of the evidence preparation that precedes them. While the Child Health and Nutrition Research Initiative (CHNRI) method is widely used for its structured expert scoring and stakeholder weighting, standard exercises often suffer from variable scoping, incomplete evidence retrieval, opaque expert selection, and weak ex ante assessments of implementation readiness. These weaknesses reduce the reproducibility of priority lists and make it difficult for funders and ministries to judge the robustness of investment decisions. Furthermore, existing reporting guidelines like REPRISE improve documentation quality but do not prescribe a reproducible pre-elicitation evidence build, leaving teams vulnerable to relying on implicit or incomplete landscape data.

Methodology
The paper proposes CHNRI-B, a methodological framework that augments the standard CHNRI workflow with a structured "research-intelligence" layer. This is not a clinical trial or an empirical evaluation of a specific priority-setting exercise, but a framework-development paper synthesizing established standards (CHNRI guidance, REPRISE, PRISMA-S, FAIR principles, responsible metrics, bibliometrics, patent analytics, and funding portfolio analytics).

The framework introduces a Stage-0 phase preceding expert scoring and a post-ranking monitoring phase. The core methodology involves three specific evidence streams:

1. Bibliometric Corpus: Retrieval of publications and reviews from at least two complementary databases, utilizing controlled vocabularies and free-text terms, with strict deduplication and author/institution disambiguation.
2. Patent Corpus: Retrieval and classification of patent families using IPC or CPC schemes to serve as conservative indicators of translational readiness.
3. Public Funding Corpus: Compilation of project data from national award registries, international programs, and verified funding acknowledgments.

These streams feed into the creation of a Context and Capacity Dossier, which serves as the central knowledge product. This dossier informs the definition of the topic scope, the long-listing of candidate topics, the sampling of experts (ensuring diversity beyond just published authors), and the preparation of evidence briefs. Crucially, the framework preserves the core CHNRI logic: experts still score independently, and stakeholders still weight criteria. The innovation lies in the inputs to that judgment.

Key Contributions and Results
The paper outlines a four-phase workflow:

1. Protocolized Scope: Defining boundaries, time windows, and governance to reduce analytic flexibility and ensure auditability.
2. Landscape Intelligence: Generating science maps, collaboration networks, patent-readiness indicators, and deduplicated funding portfolios.
3. CHNRI Calibration: Using the dossier to inform context definition, expert sampling, and evidence briefs, thereby reducing information asymmetry.
4. Scoring and Evaluation: Conducting standard CHNRI scoring followed by two new evaluation layers:
   • Capacity Concordance Analysis: Comparing final priority ranks against aggregate capacity indicators to identify mismatches (e.g., high priority but low capacity).
   • Portfolio-Coverage Benchmarking: Classifying priorities based on existing public investment (heavily covered, adequately covered, or under-covered).

The primary result is the Priority-to-Action Matrix, which translates a single priority score into distinct policy responses based on readiness and coverage. For example, a "High priority, low capacity, under-covered" topic triggers a recommendation to invest in workforce and infrastructure, whereas a "High priority, high capacity, heavily covered" topic suggests a need for coordination and synthesis rather than new fragmented research.

Significance and Claims
The authors position CHNRI-B as a tool to make health research prioritization more reproducible, auditable, and implementable without replacing deliberative judgment. Its significance lies in:

• Closing the Evidence Gap: It operationalizes the "upstream" evidence build (context, capacity, funding) that standard CHNRI often leaves implicit.
• Distinguishing Priority from Readiness: It explicitly separates the value of a research topic from the capacity to execute it, allowing decision-makers to distinguish between "ready-to-scale" priorities and "invest-to-build-capacity" priorities.
• Addressing Structural Inequities: Particularly for Low- and Middle-Income Countries (LMICs), the framework makes structural capacity constraints visible without automatically deprioritizing high-burden, low-capacity topics. Instead, it flags them for enabling investments.
• Enhancing Transparency: By requiring pre-registered protocols for data retrieval and preserving search strings and code, it addresses the reproducibility crisis in RPS.

The paper remains modest in its claims, noting that it is a proposed framework requiring prospective comparative evaluations to test whether it improves convergence, robustness, equity, and stakeholder confidence in real-world applications. It acknowledges limitations regarding data availability, language bias in databases, and the risk of metric capture, emphasizing that indicators are descriptive tools for capacity and coverage, not substitutes for normative judgment on public value.