Optimizing Digital Radiology Workflows: A Quantitative Correlation Analysis Between PACS Utilization Efficiency and Clinical Throughput Performance in Tertiary Diagnostic Imaging Departments

This study examined the relationship between Picture Archiving and Communication System (PACS) utilization efficiency and clinical throughput performance in tertiary diagnostic imaging departments in Batangas, Philippines, to identify significant predictors of operational efficiency and propose workflow optimization strategies. Guided by a conceptual framework linking digital system efficiency with clinical performance, the study employed a quantitative, non-experimental correlational research design involving 43 radiology personnel selected through purposive sampling and total enumeration of eligible respondents. Data were collected using a validated, structured questionnaire and analyzed through descriptive statistics, Pearson and Spearman correlation, and multiple linear regression. Findings revealed moderately high PACS utilization efficiency (M = 3.48) and high clinical throughput performance (M = 3.80). A significant positive relationship was established between PACS utilization efficiency and clinical throughput performance (Pearson r = 0.581, p < .001; Spearman ρ = 0.495, p < .001). Regression analysis indicated that PACS utilization indicators explained 38.4% of the variance in clinical throughput performance (R² = 0.384, p < .001), with system downtime and error frequency emerging as the only statistically significant predictor (β = 0.407, p = 0.005). These findings emphasize the importance of system reliability in supporting efficient diagnostic imaging operations. Recommended strategies include preventive maintenance, infrastructure enhancement, system integration, improved accessibility, staff training, and workflow management. The study supports Sustainable Development Goal (SDG) 3 (Good Health and Well-Being) and SDG 9 (Industry, Innovation and Infrastructure) by informing improvements in diagnostic service efficiency and digital healthcare infrastructure. It contributes to technological, institutional, and healthcare sustainability by providing evidence to guide reliable radiology operations, resource management, and continuous service improvement.