In Prevention Science, Nova Scholar Diana Fishbein and coauthors propose using artificial intelligence to modernize online clearinghouses that help decision-makers identify effective social programs.
Online clearinghouses synthesize research on programs that work. They help funders and policymakers make informed spending decisions. However, current clearinghouses face limitations. Manual literature updates are slow and incomplete. Users have difficulty navigating platforms. Information on cultural relevance and health equity is often missing.
The authors propose a two-part framework to address these challenges. First, clearinghouses should adopt “living” reviews. AI tools would automatically monitor new research and update evidence continuously. Machine learning would help identify relevant studies and extract key data. This approach is already embraced by the World Health Organization and other global organizations.
Second, clearinghouses should add AI chatbots to provide personalized guidance. These tools would help users select programs that match community needs. They would offer implementation support and connect users to training resources. The chatbot would answer questions in real-time and provide culturally relevant recommendations.
The framework includes important safeguards against AI risks. Human oversight remains central to all decisions. Community review boards would ensure ethical alignment. Technical measures would detect and prevent bias. The goal is to augment—not replace—human expertise while making evidence more accessible and actionable for communities, practitioners, and policymakers.
