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A peer-review decision support tool for research funding decision-making using the analytic hierarchy process
Hasina Al Harthi1, Maryam Al Nabhani1, Sulaiman Al Sabei2
1Department of Training and Studies, Royal Hospital, Ministry of Health, Muscat, Oman.
Plos One
|August 12, 2026
Summary
This study developed an objective scoring system to improve research funding decisions. Ethical Standards, Scientific Merit, and Novelty and Innovation were prioritized, enhancing transparency and impact in healthcare innovation.
Area of Science:
- Healthcare innovation
- Medical research funding
- Peer review processes
Background:
- Research funding drives healthcare innovation, but current peer review is subjective and inconsistent.
- Reviewer biases can lead to inefficiencies in selecting high-impact research projects.
- A need exists for an objective and transparent system for research funding allocation.
Purpose of the Study:
- To develop an objective, transparent, and structured scoring system for research funding decisions.
- To improve the efficiency and fairness of the peer-review process in allocating research grants.
- To enhance the selection of high-impact healthcare innovation projects.
Main Methods:
- A mixed-methods approach including a scoping review, surveys, Delphi methodology, and the analytic hierarchy process (AHP).
- Identified funding criteria from national/international agencies and surveyed researchers/decision-makers.
- Used Delphi method for expert validation and AHP for criterion weighting.
Main Results:
- Identified 10 key funding criteria: scientific merit, ethical standards, novelty, significance, feasibility, impact, budget, team expertise, sustainability, and partnerships.
- The Analytic Hierarchy Process (AHP) model prioritized Ethical Standards (21.7%), Scientific Merit (19.1%), and Novelty and Innovation (16.7%).
- These top three criteria constitute over half of the total decision weight, indicating their critical importance.
Conclusions:
- The proposed scoring model offers a structured, evidence-based framework for consistent and transparent research funding decisions.
- The model aligns funding with institutional priorities and emphasizes tangible societal/healthcare benefits through impact, feasibility, and sustainability criteria.
- Future research should focus on real-world implementation and the potential of AI-driven tools to further refine research funding.
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