AI-Augmented Clearinghouse to Facilitate Evidence-Based Decision-Making and Social Spending: a Conceptual Framework

Social spending refers to public or private funds aimed at helping vulnerable groups. Evidence-based decision-making supports social spending by applying existing evidence to inform funding and involves integrating relevant information from various sources, such as experimental studies and community feedback. Online clearinghouses support evidence-based decision-making by synthesizing evidence on what works, though manually updating literature is incomplete. In addition, passively summarizing evaluations is insufficient for end-users to implement preventive solutions that achieve population impacts. A responsive platform and a dissemination plan are needed to encourage the uptake of equitable and culturally relevant preventive interventions grounded in transparency and rigorous evidence of effectiveness. A two-part conceptual framework is proposed for a clearinghouse platform that integrates stepwise, interactive, AI-driven capabilities safeguarded by human touchpoints. First, clearinghouses should adopt a "living" review to automatically update evaluations-an approach embraced globally by organizations such as the World Health Organization, Cochrane Collaboration, and the U.N.'s Pan American Health Organization. The second component involves adding a chatbot to support assessment and implementation guidance and make recommendations on (1) the provision of all evidence-based preventive interventions (EBPIs) and their key activities, (2) EBPIs shown to achieve equitably distributed outcomes, (3) culturally relevant EBPIs that align with the values and needs of a target population, (4) implementation support, such as materials, training, and fidelity measures, and (5) delivery costs. The resulting platform will ethically expedite the translational process of identifying and scaling EBPIs, leading to a more complete, comprehensive, and accessible body of evidence on effective preventive strategies.