Most immunohematology labs are equipped to cope with routine workloads on a daily basis. However, any deviations from the norm, such as sudden increases in sample numbers or even a global pandemic, can quickly create unmanageable time pressures, allowing error and inaccuracy to creep in. In the last few years, there has been a growing demand for all diagnostic disciplines to offer a greater repertoire of tests, coupled with a constant drive for faster turnaround times of patient results. This, combined with worldwide workforce shortages in staff at all levels, has elevated the pressure on labs, highlighting inefficiencies and fostering an unpleasant stressful working environment for all those involved. Labs often rely on stop-gap solutions to manage their workloads, but constant firefighting can prevent them from reaching their full workflow potential.

Hematology workflow analysis: the devil is in the detail

Workflow optimization is critical, not only to help avoid the impact of these variables on the smooth running of a lab, but also to allow for growth and give teams the breathing space they need to adapt to future demands. The aim of workflow optimization in any environment is ultimately to achieve the highest possible efficiency while retaining the best quality of results. For instance, car manufacturer Toyota is one of the most famous examples of a company that has employed a clear strategy – now known as lean management’ – to systematically organize its workflows to avoid waste, synchronize processes, strive for continuous improvement and, when necessary, restructure. However, decisions governing change in lab working practices have historically relied on sample statistics, trusting that statistical averages will give a clear and accurate overview of a situation. In reality, it is far more complex than simply punching in numbers to get your answer. Thorough interpretation of results and a detailed examination of your laboratory routine, including the sample journey, are crucial. 

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We are also frequently swayed by unconscious bias or limited by our own knowledge and experiences. An overview from an external perspective helps to eliminate these preconceptions and can offer novel solutions or interpretations that may not have been previously considered.

One size doesn’t always fit all

The following example demonstrates just how unique every lab is when optimizing workflows, irrespective of sample statistics. Data taken from two immunohematology labs with identical sample numbers for blood typing might ordinarily lead to the conclusion that both locations would benefit from the same, medium throughput, fully automated, blood typing instrument. However, upon further inspection of the details from each lab – the process flow, sender behavior (figures 1 and 2), workplace organization, existing methodology, and staff scheduling – it became apparent that location B (figure 2) would need a high throughput system to meet its requirements. This was largely because location B received most of its samples between 9 am and 12 pm, leaving a short window to complete testing, whereas location A was able to space testing throughout the day. This example is representative of the differences that can be found in many labs across the world. It is simply not enough to base all purchasing, workflow, or staffing decisions on sample statistics alone. Instead, these numbers should be considered against the backdrop of the setup in the lab, factoring in everything from personnel deployment plans to spatial constraints, as well as considering the future goals of the lab.

Immunohematology workflow analysis: the solution is in the detail

This is also true for transfusion medicine, where the differences in workflows are even more pronounced because guidelines around electronic or manual protocols vary from nation to nation. In some countries, for instance, crossmatching begins with a preliminary antibody screen of the recipient’s blood, followed by an electronic comparison of recipient and donor data by the laboratory software. If the antibody screen is positive, then full compatibility crossmatching is carried out either manually or using a fully automated system, depending on the equipment the lab has.    

However, in other countries such as Germany and Austria, full crossmatching is mandatory for all transfusions. This means that every crossmatch must include an antibody screen on the recipient as before, but then it must also have a full direct compatibility test between every recipient and donor, irrespective of whether the antibody screening is positive or not. This additional workload constitutes a lot of preanalytical hands-on staff intervention, lengthens the turnaround times (TAT), and has a direct impact on the efficient use of the lab’s existing resources. There is frequently room for improvement in this type of resource-heavy workflow and, while currently the answer to this lies in more staff and more pairs of hands, the near-term solution is more likely to be AI and intelligent robotics. 

The disparity between different countries clearly demonstrates that there is no one size that fits all labs. For this reason, it is essential that labs choose to work with partners that factor in such differences in their calculations and provide a holistic overview of the lab’s workflow to accurately identify areas of improvement that may be met, perhaps by employing the correct equipment or reconfiguring the workspace. The process of workflow analysis should begin with evaluating the day-to-day operation of the lab over an extended period of time and speaking to employees about their typical work routines. This intensive data collection process gives an objective assessment of the situation, with information on everything from sender behavior and sample entry sequences to personnel rotas and lab footprint (figure 3). 

Following this, the provider can work with the lab to develop the best possible solution that will optimize all activities to avoid unnecessary tasks and create extra value, all guided by the principles of lean management. The result will be a proposal that is adapted to suit the needs of each individual lab, which may, for example, include redesigning the existing workspace or integrating new systems. This can help to deploy personnel and resources to those areas that need the most focus.

A multidimensional process

Too often immunohematology labs are forced to masterfully overcome time pressures with creative compromises but, over time, this is hindering their ability to reach their full potential. A stressed workforce watching the clock is open to errors and is far from satisfied with daily work routines. The introduction of automation has been a huge milestone that has enabled labs to tackle the seemingly impossible task of handling greater sample numbers and faster turnaround times with fewer trained staff. But workflow optimization is also an essential consideration for labs that hope to grow and adapt to ever-changing demands, whether that means automating more or optimizing existing resources. The process of workflow optimization should be multidimensional, based on more than just sample statistics, but also the configuration and operation of the lab. Only this approach will provide true insights into how labs can optimize their workflows, now and for the future.