Optimizing Cardiac Inpatient Flow and Resource Allocation in Low‐Resource Settings Using Queuing Theory: Insights From a Tertiary Hospital in Bangladesh

ABSTRACT

Healthcare systems in low‐resource settings face persistent challenges in providing timely cardiac care. This study applies queuing theory models ( M / M / c / c and M / M / c / K ) to optimize inpatient flow and resource allocation in the cardiology department of Sylhet MAG Osmani Medical College Hospital, Bangladesh. Using data from 6277 patients treated in 2023, the study analyzes arrival patterns, lengths of stay, and bed capacities across four units: Normal Care Unit (NC), Cath Lab (CL), Coronary Care Unit (CCU), and Progressive Coronary Care Unit (PCCU). The results reveal critical shortages in resources and operational bottlenecks, particularly in the CCU and PCCU, where bed shortages frequently compromise care quality. Recommendations include reallocating underutilized resources, such as beds from the CL, and expanding capacities in the CCU from 8 to 15 beds and in the PCCU from 10 to 25 beds to accommodate unmet demand. The study also highlights the detrimental effects of overcrowding and the unregulated use of extra beds in the NCU, proposing strategies to enhance operational efficiency and patient accessibility. This research provides data‐driven strategies to reduce congestion, enhance patient outcomes, and promote healthcare equity in low‐resource settings, addressing the unique challenges of underserved populations in developing countries while contributing to the broader goal of strengthening healthcare systems and advancing global health equity.