Driven by unique needs and limited by individual resources, today’s clinical laboratories fall across the continuum of automation, from full manual labor to total lab automation.
“Without being melodramatic, laboratories live or die on providing results continuously for their customers,” says Colin Hill, director of clinical lab marketing for systems and automation, Ortho Clinical Diagnostics, part of the Johnson and Johnson family of companies, Raritan, NJ. Faced with the persistent challenges of staffing shortages, tight budgets, and growing volumes, more laboratories seek solutions to this continuous drive to produce in automation.
Unfortunately, simply adding technology cannot solve a laboratory’s problems. It is important that laboratory administrators consider the entire laboratory process before plugging in to automation. “An analyzer on its own will not solve the customer’s problem. It requires a more holistic approach that looks at what the business goals are,” Hill says.
Many laboratories will find they cannot leap in with total automation anyway. Often the major obstacle is funding, but issues with infrastructure or buy-in can also create delay. The exact extent of available resources, along with the business goals, will influence at which phase of automation a lab can leap in—and where it wants to leap off.
Naturally, mature markets are further along. “Most laboratories in the mature market have moved to fully automated analyzers, but may not have moved all the way to full automation,” Hill says. “In the emerging market, manual labor is still a major player.”
In fact, it is unlikely that manual labor will go away any time soon, but semiautomated laboratories are growing more common. Smaller and smaller hospitals have been increasingly investigating their automation options.
“If you look at the emerging markets, where growth is so rapid right now, one of their biggest challenges is the pure volume of testing they’re having to do as more and more people access the health care system,” Hill says. “We’re seeing increasing numbers of requests and preliminary investigations of process automation from 200-bed and 100-bed hospitals.”
Even in regions where the labor pool is not a limitation, concerns about quality may also lead to automation. Faster, quicker, and more accurately is possible with the right technology, whatever that right technology is.
Hill describes the automation continuum, which he has seen evolve over the past 10 to 15 years, as starting at a laboratory with completely manual processes, then moving to a semiautomated facility, first with a single discipline, then multidisciplinary testing. Next, many laboratories integrate virtual, automated IT solutions, then analytical capabilities, and finally total lab automation.
MAN AROUND THE LAB
Compared with other industries, automation in the clinical laboratory area has been slow to evolve, and current processes are highly dependent on personnel. Blood collection, specimen transport, sample preparation, and complex testing continue to fall under the realm of manual labor.
“Larger-volume laboratories with a significant outreach volume find it difficult to dynamically adapt their sample management and workflow based on analyzer status/availability, the varying flux of sample types—inpatient versus outpatient—and volume into the clinical lab at any given moment during the day,” says Mike Hoang, senior product manager, clinical automation, Beckman Coulter, Brea, Calif. “Smaller-volume and space-constrained labs, where automation may not fit financially and physically, still manually manage preanalytical and postanalytical workflows with varying consistency and quality.”
Manual labor comes with the inherent potential for errors common to human operation. In medicine, the factors can become even more complex. “Accessioners have multiple responsibilities, and this can slow them down,” says Sandy Agnos, product manager, autonomous mobile robots, Swisslog Healthcare Solutions, North American headquarters, Denver. “Having to get up can be a distraction as well as increase the risk for error—using the wrong label or entering the wrong information into the computer, for example.”
Moving specimens around can impact every position within the lab. Samples may need to be split, shared, or transported between different departments and cells, such as processing/receiving, prep, and testing areas. “As [accessioners] move away from receiving to deliver the specimens, they may miss an important phone call from a doctor, nurse, or patient,” Agnos says. “Med techs [can be similarly] distracted from important testing by having to either fetch or deliver a sample.”
Preparation of specimens is another time-consuming necessity that could be better handled with automation. A lab may have a broad range of sample types for a variety of downstream tests that need to be processed. “Often a sample goes through multiple steps when entering a laboratory, starting from the transfer into a secondary tube, labeling, potential pretreatment steps, and, ultimately, into the sample-preparation workflow,” says Line Martinsen, director of automation, QIAGEN, Valencia, Calif. “If a lab has several sample prep instruments or workflows for different sample types and tests, these workflows take up precious time in daily management, staff trainings, and routine maintenance.”
A laboratory’s process is also its culture, and the introduction of automation can meet resistance as it forces paradigm shifts. “Often with a robot, staff say they ‘like’ to walk around,” says Swisslog’s Agnos. “Many labs do not realize the percentage of the day, in time, that transporting materials can take. It can truly be disruptive to productivity, TAT [turnaround time], and throughput.”
The process itself can also present a challenge. “Many automation systems are designed linearly and don’t allow for a sudden, and often unexpected, shift in workflow, which often forces laboratorians to find manual ‘work-arounds,’ ” Beckman’s Hoang says. Smaller labs may have unique protocols; critical specimens and sample reruns may require special, nonautomated treatment.
“If critical-priority samples arrive after a large batch of routine-priority samples, the laboratory staff often manually processes the critical-priority samples because the automation system is at full capacity processing the routine samples,” Agnos says. “In managing sample reruns, many automation systems don’t have the capability to retrieve samples, reroute them to a specific analyzer, and, ultimately, restore the sample without any manual intervention from lab staff.”
When work-arounds are created, they may involve new technologies. For these and other reasons, “a lab often has a ‘zoo’ of different instruments from different providers, which requires specialized knowledge and training, and significant investment is needed from the lab side in terms of time and resources to manage the interfaces between these instruments,” QIAGEN’s Martinsen says.
Data integration into existing laboratory information management systems and easy results interpretation are two areas that are time intensive. “Labs are requesting novel solutions to deal with these challenges,” Martinsen says.
Vendors are willing to help, but must stay within regulatory guidelines. Challenges lie around three main themes, according to Martinsen: the cost of revalidation associated with changing a validated workflow; consolidation of commercial- and lab-developed assays into one workflow; and selection of the right automated platform to fit with the needs of the laboratory and yield a faster time to result.
Incremental steps toward automation ease capital outlay but can complicate acquisition and integration. Looking at the big picture of the clinical laboratory’s business can help to ensure that the steps taken make sense. “Customers can stage their entry into automation with phased investment that aligns with their own growth expectations,” says Ortho Clinical’s Hill. “It’s about aligning their business goals with the solutions available to them.”
As more laboratories move toward the total lab automation end of the spectrum, more value-added tasks, such as those that fall in the pre- and postanalytic areas, are automated. This can enable the laboratory to do more with the resources it has, whether it’s the generation of increased volume, faster turnarounds, improved quality, and/or smarter management of staff.
Laboratories in mature markets are ahead in the automation of these tasks, but the emerging markets are not far behind. “As we look at this globally today, emerging markets are going through the development curve far more quickly than the mature markets did because the technology has already, to an extent, been developed,” Hill says. However, today’s labs are still spread across the automation continuum, managing their specific challenges and living or dying by their ability to produce results.
Renee Diiulio is a contributing writer for CLP. For more information, contact Editor Judy O’Rourke, at .