Using discrete event simulation to optimize nucleic acid testing process for coronavirus disease 2019 (COVID-19)

Healthcare systems face diagnostic bottlenecks during outbreaks like the SARS-CoV-2 pandemic. Prior research has shown that nucleic acid testing is a primary diagnostic method, but resource constraints limit throughput. This gap motivated the need to evaluate process optimization without increasing costs. No prior work had resolved how to balance technician workload and equipment use effectively. Existing studies focus on equipment upgrades, but this paper shifts attention to workflow adjustments. Understanding how specimen handling impacts testing speed remains a key challenge. The role of specimen tubes in reducing processing time is not well established. This paper addresses these uncertainties by modeling real-world scenarios.

This study aimed to identify ways to improve nucleic acid testing efficiency for SARS-CoV-2 without increasing costs. The specific problem is the high diagnostic burden on laboratories during the pandemic. The motivation comes from the need to maximize testing output with limited resources. The authors sought to test various operational scenarios using simulation. They focused on technician scheduling, equipment use, and specimen handling. The goal was to determine which changes would most improve testing capacity. By modeling different conditions, they aimed to find low-cost solutions. This approach allows for evaluating impacts before implementing changes.

The researchers used Discrete-Event Simulation (DES) to model the testing process. They collected data from the First Affiliated Hospital of Guangzhou Medical University. The simulation software used was Arena Simulation Software. Seven scenarios were created to represent different operational conditions. These included adding new nucleic acid extraction systems and adjusting staff schedules. They also tested the impact of using specimen tubes with guanidine isothiocyanate (GITC). The model compared total time spent and equipment consumption across scenarios. The simulation allowed for evaluating changes without real-world disruptions.

The simulation revealed that adjusting technician schedules improved efficiency without extra costs. Using GITC-containing tubes reduced testing time by 30 minutes per specimen. Adding new nucleic acid extraction systems had minimal impact on overall capacity. Shifting staff from night to morning duty improved workflow without additional expenses. The model showed that specimen handling was a key factor in testing speed. Changes in equipment or staffing did not significantly increase testing output. The cost of personal protective equipment and testing kits was also analyzed. The most effective improvements came from workflow adjustments rather than new equipment.

The authors concluded that workflow adjustments can significantly improve testing efficiency. They propose that optimizing technician schedules and specimen handling is more effective than adding equipment. Using GITC-containing tubes reduces processing time without increasing costs. The findings suggest that small operational changes can have a large impact. The study supports the use of simulation to evaluate process improvements. The results indicate that current staffing levels can handle higher testing loads with better scheduling. The authors suggest that hospitals should prioritize workflow optimization. Their findings may guide other facilities in improving diagnostic capacity.