Simulation to optimize the laboratory diagnosis of bacteremia

Abstract

Blood cultures are central to the management of patients with sepsis and bloodstream infection. Clinical decisions depend on the timely availability of laboratory information, which, in turn, depends on the optimal laboratory processing of specimens. Discrete event simulation (DES) offers insights into where optimization efforts can be targeted. Here, we generate a detailed process map of blood culture processing within a laboratory and use it to build a simulator. Direct observation of laboratory staff processing blood cultures was used to generate a flowchart of the blood culture laboratory pathway. Retrospective routinely collected data were combined with direct observations to generate probability distributions over the time taken for each event. These data were used to inform the DES model. A sensitivity analysis explored the impact of staff availability on turnaround times. A flowchart of the blood culture pathway was constructed, spanning labeling, incubation, organism identification, and antimicrobial susceptibility testing. Thirteen processes in earlier stages of the pathway, not otherwise captured by routinely collected data, were timed using direct observations. Observations revealed that specimen processing is predominantly batched. Another eight processes were timed using retrospective data. A simulator was built using DES. Sensitivity analysis revealed that specimen progression through the simulation was especially sensitive to laboratory technician availability. Gram stain reporting time was also sensitive to laboratory scientist availability. Our laboratory simulation model has wide-ranging applications for the optimization of laboratory processes and effective implementation of the changes required for faster and more accurate results.

Importance: Optimization of laboratory pathways and resource availability has a direct impact on the clinical management of patients with bloodstream infection. This research offers an insight into the laboratory processing of blood cultures at a system level and allows clinical microbiology laboratories to explore the impact of changes to processes and resources.

Keywords: blood culture; bloodstream infections; clinical microbiology; discrete event simulation.

Fig 1

The processing of blood cultures takes place in parallel to the empirical treatment of bloodstream infection and sepsis. At multiple nodes in the clinical pathway, there is an exchange of clinical and laboratory information, leading to the tailoring of antimicrobial therapy to pathogen and susceptibility testing. Our study focused on the events within the inner “Laboratory” (dark purple).

Fig 2

High-level pathway for processing of blood culture specimens within the simulation. AST, antimicrobial susceptibility testing.

Fig 3

Laboratory simulation sensitivity analysis. Panels show the time from receipt in the laboratory to four key events in the specimen journey. Each dot represents the mean blood culture event time generated from a unique laboratory simulation. All parameters are kept fixed, except for one staff’s “OnHold” wait time (x-axis), indicated by the dot color. Colored lines show the aggregate mean of the simulations. The black horizontal line is the mean event times observed in the real data set. Technicians have roles in earlier processing steps; reduced availability cascades delays in all event times. Scientists have roles in later interpretative steps; delays only have an impact on the Gram stain step.