Business Intelligence Applications for Mental Health Resource Allocation and Public Health Program Accountability

This study examines the application of business intelligence (BI) tools for improving mental health resource allocation and strengthening accountability within public health programs. Growing demand for mental health services, coupled with constrained funding and fragmented data systems, has intensified the need for evidence driven decision making. Business intelligence offers an integrated analytical approach that transforms diverse clinical, administrative, and community data into actionable insights for planners, managers, and policymakers. The study adopts a conceptual and systems oriented approach, synthesizing literature from public health informatics, health economics, and program evaluation to outline a BI enabled framework for mental health governance. Core BI functions including data integration, dashboard visualization, descriptive and predictive analytics, and performance monitoring are mapped to key allocation and accountability challenges. These functions support needs based budgeting, service demand forecasting, workforce optimization, and outcome tracking across community, primary, and specialized mental health services. From a resource allocation perspective, BI applications enable the identification of geographic and demographic service gaps, high burden populations, and inefficiencies in service utilization. Predictive models support proactive planning by anticipating caseload trends, crisis events, and resource pressure points. This facilitates equitable distribution of funding, personnel, and infrastructure while reducing waste and duplication. For program accountability, BI dashboards provide transparent, real time monitoring of service coverage, quality indicators, expenditure patterns, and outcome metrics aligned with public health objectives. The study further highlights governance considerations including data quality management, interoperability across health and social systems, ethical use of sensitive mental health data, and capacity building for BI adoption. By embedding accountability indicators within BI platforms, public health agencies can strengthen performance management, demonstrate value for money, and enhance public trust. The paper concludes that business intelligence applications represent a critical enabler for responsive, transparent, and outcome oriented mental health systems. Integrating BI into public health program design and oversight can improve decision quality, promote accountability, and support sustainable mental health service delivery in resource constrained settings. These insights are particularly relevant for low and middle income contexts where mental health gaps persist, data fragmentation is severe, and accountability mechanisms require systematic, scalable, and policy aligned digital solutions.