Bridging prediction and action: An integrative review of frameworks, strategies, and enablers for translating machine learning outputs into public health interventions

Machine learning applications now generate predictions across multiple domains of public health practice, yet the conversion of these outputs into sustained interventions continues to occur unevenly. This integrative review examined how predictive outputs become actionable within public health systems and identified the mechanisms, conditions, and tensions that shape this process. Following Whittemore and Knafl’s methodology (2005), a search across four databases yielded 25 studies that were synthesized through thematic analysis and cross-literature integration. The review found that actionability depends on more than technical performance; it arises from the interplay between model characteristics and the organizational, cognitive, and contextual environments in which outputs are received. Structured implementation pathways and human-AI collaboration appeared as recurring translation mechanisms, while governance, workforce readiness, and feedback systems functioned as key enablers. Interconnected technical, organizational, and human barriers consistently constrained translation efforts. A Prediction-to-Action Framework was developed to integrate these elements and to surface ongoing tensions, particularly around human oversight, standardization, and governance scope. The findings indicate that effective translation requires coordinated attention to socio-technical conditions rather than technical performance in isolation.