Impact of Plasma Optimization on Work Flow

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A number of recommendations have been presented in this guide to improve plasma yield and many of these may impact workflow within Blood Establishment operations. Increasingly Blood Establishments are adopting Lean Six Sigma quality improvement initiatives to streamline their processes as they compete for limited resources with increasing operational costs. The following provides an overview of how to use Lean Six Sigma principles to assess and improve blood center operations for increased plasma recovery.  

Selecting a quality improvement initiative with Lean Six Sigma begins with a high level process diagram. An example is a SIPOC diagram that defines Suppliers, Inputs, Process, Output, and Customers as shown in Figure 13. The purpose of the SIPOC diagram is to characterize or define the process and variables that contribute to the final product quality. In this example for blood component production, the Suppliers, Inputs and Process contribute to the quality of the final Output (plasma and LR blood components). The voice of the customer also defines the quality criteria for the final product, such as if the customer is a plasma vendor, additional criteria for quality may be cell-free plasma with a minimum volume of 200 mL.

Figure 13

SIPOC Process Diagram
Plasma Optimization: Six Sigma Suppliers, Inputs, Process, Output, and Customers (SIPOC) Process

There are three areas of focus to improve plasma volume recovery; these include Suppliers, Inputs and Process when using this sample SIPOC.

Supplier choice can impact product quality (Outputs) but for the purpose of this discussion the focus will be on Input and Process variables. Input and Process variables have been previously defined and are summarized in Table 5 and Table 6.

Table 5

Input Variables That Impact Plasma Yield
Donors Blood Systems Equipment Staff
Percent hematocrit
Lower hematocrit improves plasma yield (more female donors)
One universal collection system to “lean” inventory management, collection and component processing (Leukotrap® RC system
with RC2D Filter)
Standardize collection volume using automated blood collection mixers Empty donor line tubing
Higher collection volume Leukotrap RC System with RC2D Filter provides higher plasma recovery than Leukotrap WB System Centrifuge settings:
TCF is key!
Optimal plasma recovery 2.4-2.65 x 106 TCF
Empty numbered tubing
Standardize collection volume CP2D over CPDA-1 Collection Systems Use moderate brake setting Express plasma to wye versus collection bag port
Plasma viscosity (RT processing improves plasma yields)   Filter vs. non filter during centrifugation has no impact on plasma recovery Empty plasma bag tubing
    Oval centrifuge buckets improves process with no impact on plasma recovery Empty plasma bag tubing (leave 1 segment)
      Empty plasma bag tubing
(leave 2 segments)
      Standardize FFP volume

Table 6

Process Step Variables That Impact Plasma Yield
Component Production
1. Optimized TCF
2. Moderate brake setting
3. More RT blood processing (transport
4. Collect into Leukotrap® RC System vs. Leukotrap WB System
5. Processing Leukotrap RC System vs. Leukotrap WB System
6. AS-3 vs. CPDA-1 System

Once the variables that impact plasma recovery have been identified, assign a metric to benchmark the current process and identify improvement initiatives that will produce the greatest impact on plasma yield with the maximum efficiency. A common metric to define workflow efficiency of blood component processing is time. Table 7 shows process times associated with some recommended component production process steps.

Table 7

Process Step Variables
Process Steps ~ Time (seconds)
1. Optimize TCF 751
2. Moderate brake setting Brake 2=386
Brake 7=195
Brake 9=149
3. More RT blood processing (transport) Variable depending on location and center logistics
4. Collect into Leukotrap® RC System vs. Leukotrap WB System 0
5. Processing Leukotrap RC System vs. Leukotrap WB System 150
6. AS-3 vs. CPDA-1 System 0

When using time as a metric to define workflow efficiency of blood component processing, it is important to consider that component production typically follows a batch mode process and actual time for individual steps may be misleading. Figure 14 provides two different examples of batch mode processing with different timelines due to differences in the batching methods used. In process A, step 1 (blood transport) might be identified as a bottle-neck or a rate limiting step since work cannot begin until all the blood is received. In process B where blood transport is continuous and in smaller batches the process workflow follows a more continuous or linear pattern, the overall timeline is shorter as a result of being able to run multiple batch steps earlier and in parallel.

Adding time to the slowest step in a process will have a greater impact on the timeline than adding time to a fast step. In many cases with batch mode processing, additional time can be “absorbed” by the hold time or batch process time for longer steps. There are limitations to using time as a metric for defining efficiency in batch mode processing; however, opportunities exist for adding time with minimal impact on overall timelines if baseline are established and identify bottlenecks and rate limiting steps are identified.

Figure 14

Sample Batch Mode Process Timelines

Plasma Optimization: Sample Batch Mode Process Timelines

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