Background/Purpose: Public health professionals make complex decisions amid competing priorities, rapid change, limited resources, and expanding data. However, more data does not consistently lead to better decisions. Decision Intelligence (DI) is a framework that integrates data, human judgment, and context to improve decision quality. DI Maps relationships between actions and outcomes, enabling faster learning compared with testing one intervention at a time. This presentation introduces two DI interventions and their results. Methods: A DI framework was applied within two continuous quality improvement projects. Participants included public health staff and clinical partners. Procedures included elicitation sessions to develop Causal Decision Diagrams, a DI tool that maps decision-making by identifying goals, outcomes, external factors, actionable levers, and intermediates. One project focused on increasing referral capture within a practice, while the second examined conversion of referrals into scheduled visits. Data sources included referral volume, provider participation, and scheduling outcomes tracked over time. Analysis involved iterative PDSA cycles, trend monitoring, and refinement of decision levers to improve system performance. Results: The first of two DI projects is more mature, with initial results showing meaningful improvements in outcomes. Referrals increased from a baseline of 30% to 34% in Q4 2025 and was sustained at 33% in Q1 2026, with average monthly referrals increasing from 12.5 to approximately 14–14.25. The number of referring providers increased from three to six. These outcomes, alongside the associated Fussing Hard campaign, generated interest from Family Connects International for nationwide scaling. The second DI project, focused on conversion of referrals to scheduled visits, is newer, with preliminary results expected in September. Implications: DI helps public health professionals move faster and with greater clarity in complex systems. Rather than relying on slow, trial-and-error approaches, DI makes it possible to identify high-impact actions earlier and focus effort where it matters most.
