A Cross-sectional Study of the Blood Sample Collection Center Using Queuing Theory at a Tertiary Care Hospital

Abstract Introduction: Queuing theory, originating with Agner Krarup Erlang in 1908, has evolved to address healthcare operations, notably in managing outpatient appointments and hospital services. Its application helps hospitals optimize service capacity, reduce delays, and predict congestion, ultimately improving patient care quality, staff satisfaction, and operational efficiency. Aim and Objectives: This study aimed to optimize the waiting time at the Blood Sample Collection Center (BSCC) by studying process flow, the arrival and service pattern, and factors affecting waiting time, and suggesting measures to optimize the same by applying queuing theory. Methodology: A cross-sectional study at the BSCC of a tertiary care teaching hospital. The study used a mixed-method approach, gathering data through the study of patient records, direct observation at the BSCC, and unstructured interviews with key stakeholders. Observations included queue length, arrival patterns, and service times were applied to assess workload distribution and server utilization across five-time slots. M/M/s Queuing Formula Spread Sheet Excel template. Results: The study analyzed a 1082-bed tertiary care hospital’s BSCC. The process was divided into two phases: registration and blood sample collection. The average waiting time was higher for females. The average utilization factor for Phase I was 0.74 and for Phase II was 0.57. Conclusion: Implementing priority queuing at BSCC can reduce congestion, improve service efficiency, and enhance patient satisfaction. Modifications such as dedicated counters for specific cases and addressing physical facilities can help manage peak-hour demand, reduce waiting times, and optimize the number of patients attending daily.