Validating the Use of Natural Language Processing and Text Mining for Hospital-Based Violence Intervention Programs and Criminal Justice Articles

Hospital-based violence intervention programs (HVIPs) are a form of community violence intervention designed to address trauma resulting from violent injuries. This public health approach has been implemented across the United States since the 1990s, with numerous qualitative and quantitative studies evaluating its effectiveness. Manual systematic reviews by domain experts have helped identify major themes and research gaps. While these reviews are valuable for synthesizing the existing literature, thisprocess can be time-consuming and labor-intensive, given the vast amount of research in public health and criminal justice. To meet the urgent need for accessible insights into the violence-related literature, more efficient methods are essential. Recent advances in artificial intelligence (AI) offer promising tools to streamline this process. This study applies AI, specifically natural language processing techniques, to analyze recurring themes in the HVIP-related literature at the intersection of criminal justice and public health. The findings indicate that text-mining methods can enhance and accelerate the systematic review process, while also revealing new insights. The results underscore the potential of AI-driven tools to support evidence-based practices and highlight the importance of interdisciplinary collaboration to improve the effectiveness and implementation of HVIPs.