A peer-review decision support tool for research funding decision-making using the analytic hierarchy process

Background: Research funding is a crucial driver of healthcare innovation and improved patient outcomes. However, the peer-review process utilised during funding allocation is often subjective, inconsistent, and prone to reviewer biases, leading to inefficiencies in selecting high-impact projects. This study aimed to develop an objective, transparent, and structured scoring system to improve research funding decisions. Methods: A mixed-methods approach was used, incorporating a scoping review, surveys, Delphi methodology, and the analytic hierarchy process (AHP). The scoping review identified funding criteria used by national and international agencies. A survey of researchers and decision-makers at the Royal Hospital in Muscat, Oman, gathered insights into funding priorities. Experts refined and validated criteria using the Delphi method, while weighted importance to each criterion as per the AHP was assigned through pairwise comparisons. Results: The study identified 10 key criteria for funding decisions: scientific merit, ethical standards, novelty and innovation, significance and strategic alignment, feasibility, impact and knowledge transfer, budget and cost-efficiency, team expertise and collaboration, sustainability, and partnerships in funding. Based on AHP weighting, the final model prioritised Ethical Standards (21.7%), Scientific Merit (19.1%), and Novelty and Innovation (16.7%) as the highest-weighted criteria, together accounting for more than half of the overall decision weight. Conclusion: The proposed scoring model provides a structured, evidence-based framework to enhance research funding decisions by enhancing consistency and transparency during peer review. It ensures alignment with institutional and national research priorities, while the inclusion of impact, feasibility, and sustainability criteria reflects a shift towards funding research that yields tangible societal and healthcare benefits. Future studies should evaluate implementation of the model in real-world settings and explore the role of artificial intelligence-driven decision-support tools to further refine research funding processes.