INTEGRATING HUMAN RESOURCE PLANNING WITH BUDGET FORMULATION IN MEDICAL TEACHING INSTITUTIONS: AN APPLICATION OF THE WISN METHOD

Abstract

Background: Healthcare systems, particularly in public sector hospitals, face persistent challenges in aligning human resource planning with financial management. In Medical Teaching Institutions (MTIs), budgeting processes are often disconnected from actual workforce requirements, leading to staffing imbalances, inefficient resource allocation, and compromised service delivery. The Workload Indicators of Staffing Need (WISN) method offers a data-driven approach to workforce planning; however, its integration into budget formulation remains limited in practice. Methods: This study adopts a quantitative, cross-sectional design to examine the role of WISN-based human resource planning in the budgeting process of MTIs. Data were collected from 250 respondents, including administrators, HR managers, finance officers, and departmental heads, using a structured questionnaire based on a five-point Likert scale. Structural Equation Modeling (SEM) was employed to analyze the relationships among WISN-based HR planning, staffing requirements, budget allocation, efficiency, and organizational performance. Reliability and validity were assessed through Cronbach’s Alpha, Composite Reliability, and Confirmatory Factor Analysis. Results: The findings reveal that WISN-based HR planning has a significant positive effect on staffing requirements (β = 0.68, p < 0.001), which in turn influences budget allocation (β = 0.61, p < 0.001) and organizational performance (β = 0.52, p < 0.001). Efficiency was found to partially mediate the relationship between staffing and performance (β = 0.49, p < 0.001). The structural model demonstrated good fit indices (CFI = 0.94, RMSEA = 0.056, SRMR = 0.047), confirming the robustness of the proposed framework. Conclusion: The study provides strong empirical evidence that integrating WISN-based HR planning with budget formulation enhances workforce efficiency, financial accuracy, and organizational performance in MTIs. The findings highlight the need for evidence-based budgeting systems that incorporate workload-driven staffing analysis. The proposed framework offers a practical and scalable solution for improving healthcare management and policy formulation in resource-constrained environments.