DECEMBER LEARNING OBJECTIVES
Upon completion of this article, the reader will be able to:
1. Recall the history of operating laboratories’ shift from manual to automated.
2. Describe modular-based automation systems and the problems that arise by their design type.
3. Discuss pre-analytical, analytical and post-analytical errors and how automated solutions resolve these errors.
4. Discuss cloud-based automation and its benefits in the modern day laboratory.
Today’s clinical laboratory is evolving to keep pace with the changing reality of global healthcare. Laboratorians are seeing their roles shift from being technologists to first responders in the patient care pathway. This shift is being enabled, in part, with laboratory automation which is increasingly being used to reduce manual work, improve turnaround time, enhance quality and increase throughput. Depending upon the size of the laboratory, its needs and abilities, automation solutions can take many forms. Regardless of the specific role it plays, however, laboratory automation is beginning to emerge as a necessity for diagnostic laboratories seeking to dramatically improve performance. Despite this trend, currently, the number of laboratories around the world that have been able to embrace automation is still small.3
In 1998, automation was loosely defined as “any device, software or process that improves the efficiency of the laboratory.”1 In 2012, Kevin Olsen, author of, The First 110 Years of Laboratory Automation: Technologies, Applications and the Creative Scientist, offered a more specific description, “a complex integration of robotics, computers, liquid handling and numerous other technologies.”2 Most important, while definitions and technologies may have changed over time, the reason organizations choose automation solutions has remained unchanged for a century; that is, “to save time and to improve performance.”2
The most popular form of automation, in the traditional sense of the word, is total laboratory automation, which is primarily used in large core or reference laboratories. Medium-sized laboratories, which are faced with challenges similar to their larger colleagues, are also trying to harness the power of automation. However, due to lack of space, low volume and economic pressures, these labs tend to use the most basic kind of automation—an integrated workcell. This certainly represents an improvement over laboratories that are manual, but it’s still a limited solution that does not allow laboratories to fully exploit the benefits of automation and thrive in today’s value-based healthcare environment.
A short history of laboratory operations
The first automated laboratory system was introduced into the chemistry laboratory by Leonard Skeggs, an American biochemist best known for inventing the AutoAnalyzer in 1956.5 It offered automatic analysis of blood from start to finish without the need for much manual intervention.6
Since that time, traditional automation has been seen as technology that helps move tubes through parts of the laboratory workflow. In 1981, Japan was first to implement total laboratory automation in its clinical laboratories.7 This solution relied on robotics and conveyer mechanisms and was referred to as systemized automation.7 Sixteen years later, in 1997, the first total laboratory automation solution was implemented in North America.1
By 1998, it was estimated that only eight percent of laboratories could afford the expense of total automation, but that 100 percent of laboratories could benefit from implementing at least some automated systems, even if on a smaller scale.4 The industry’s answer to this disparity was to offer a modular solution in the way of “consolidated analyzers, integrated analyzers, modular workcells and pre- and post-analytical automation.”4
Workstations, the “most basic unit of automation,” feature automated clinical analyzers for general chemistry or immunoassay testing, or both.4 Workcells offer integrated clusters of analyzers that perform like a single system, and can be managed by one technologist.4 Physical workcells are typically connected via small tracks with one inlet/outlet module used for loading and unloading samples, while virtual workcells are groups of systems linked digitally, by middleware.4
The problem with a modular approach
Most laboratories consider automation using an either/or approach; choosing between total laboratory automation or modular workcells, with system costs and space constraints remaining the primary drivers in the decision. For many facilities— especially small- and medium-sized laboratories—total laboratory automation remains cost- and space-prohibitive. However, workcell solutions can leave gaps that expose vulnerabilities or represent lost opportunities for offsetting cost pressures and labor shortages. For example, integrated chemistry- immunoassay workcells may handle various tasks, such as routing samples between analyzers, performing analyses and conducting results reviews, but this does nothing to address the dozens of other time-consuming steps in a laboratory’s workflow. As a result, laboratories remain overexposed to possible human error, and potential productivity gains are left unexploited. Safety isn’t improved either, with laboratory personnel left unnecessarily vulnerable to repetitive motion injuries and biohazardous substance exposures.
Single vs multiple points of sample entry
Modular automated solutions often bundle testing, and use a single point of entry for all samples. While a single point of entry may appear to help simplify operation, disruptions can occur when the sample introduction module becomes unavailable for any reason. Recognizing this design vulnerability, innovative companies have begun offering integrated solutions that feature multiple points of sample entry, helping laboratories preserve uptime with sample introduction redundancy.
Notwithstanding such design improvements, workflow slowdowns are still a risk inherent to bundled chemistry and immunoassay systems. Immunoassay preparation typically takes time. In fact, immunoassay testing can take two to five times longer than chemistry testing. Managing both immunoassay and chemistry samples with one automated track means that chemistry tubes are not released until immunoassay tests complete their cycles. In effect, access to the chemistry-only tubes is blocked by the longer cycles of tubes requiring immunoassay testing.
Insufficiencies like these highlight the need for laboratories to evaluate factors beyond the workcell when considering automation. They also emphasize the need for scalable automated solutions that are capable of optimizing workflow outside the realm of traditional automation systems. For example, independent analyzers connected by multi-lane tracks and intelligent routing enable chemistry and immunoassay tubes to be released as soon as their respective testing is completed, ensuring efficient workflow and timely results delivery.
To achieve breakthrough performance improvement and cost reduction, innovative automation solutions are needed; solutions that leverage not only robots, tracks or workcells to move tubes, but also algorithms, analytics and the cloud to move data.
Automating for improved patient care
The pre-analytical phase of testing is actually the most labor-intensive and error-prone, typically consuming 60 percent of a laboratory’s labor hours and accounting for up to 75 percent of its errors.8 Moreover, approximately 13 percent of errors may have an effect on patient health.8 Accordingly, automation innovators have come to view automation of pre-analytical activities as an effective means of significantly improving laboratory operations and, thus, patient care.
In an article written over a decade ago, Errors in Laboratory Medicine, Mario Plebani, professor of clinical biochemistry and molecular biology at the University of Padova School of Medicine and department chief of Laboratory Medicine at the University Hospital of Padova, Italy, addressed the issue of lab safety. He noted, “technological solutions for pre-analytical processes, such as order entry, barcoding identification of patient and related samples and information sharing, had the potential to make the laboratory safer.”9
With this in mind, new pre-analytical automated systems are being designed to perform comprehensive specimen inspections in just seconds, helping laboratories prevent potentially wasteful pre-analytical errors (e.g., mislabeled tubes, insufficient sample quantities and wrong tube types) from entering their analytical workflow. The most sophisticated of these emerging capabilities enable laboratories to automate identification of tube and cap color, sample volume checks, patient identification verification, spin status detection and sample tube weight, while also capturing an image of the tube. Protections for both technologists and patients are offered with these advancements. In addition, they give technologists more time to focus on high-impact patient-care activities.
Meaningfully harnessing the power of massive data in laboratories is another way laboratories can improve patient care. The most cutting-edge clinical informatics technology has begun to merge seamlessly with mechanical automation systems. As Plebani stated, “more effective integration between automation and information management is crucial for assuring process controls that allow [laboratories] to identify and improve on the critical steps in pre-, intra- and post-analytical phases.”9
Furthermore, recent advancements in cloud-based technology have greatly helped laboratories in their pursuit to optimize all operations that impact their workflows. For example, a time-consuming task for any laboratory is inventory management. When there are inefficiencies in managing inventory, operations are affected, workflow is disrupted, the ability to produce results is jeopardized and unplanned costs are incurred. By automating inventory management through cloud-based applications, however, laboratories are now able to mitigate these effects, obviate administrative tasks and devote more time to patient care.
Automation for all
In the past, some laboratories, especially smaller ones, have lacked the space and budget required for systems that automate laboratory data flow. But with industry forces making access to automation increasingly critical to successful performance, subscription-based laboratory management systems that use cloud technology to save space and reduce cost have begun to grow in popularity. These new systems bring the benefits of automation to laboratories of any size—including labs that are smaller—and offer a user-friendly, reliable solution for managing and accessing data from clinical laboratory instruments. They provide the same benefits as traditional automation, such as improved turnaround times, reduced errors and improved efficiencies—but they achieve these results by automating data flow. They have either a small or non-existent footprint, low upfront cost and require less or no involvement from IT departments.
Automating for the future—from today to tomorrow
The healthcare paradigm is shifting from one that is procedure-based, to one that is value-based—this is changing how clinical laboratories work. Laboratories are finding that to adapt to this change in the healthcare environment, they must adopt a broader, future-focused approach that enables them to achieve greater time and cost savings. This allows staff members to focus on priorities that directly contribute to improved patient care.
The focus on value-based care is not a surprise as scientists and leaders have been predicting it for years. Dr. Peter Wilding, an award-winning pathologist, declared over a quarter of a century ago in his clinical chemistry article, The Changing Role of the Clinical Laboratory Scientist: Coming Out of the Basement. “In the future, laboratory scientists must align their expectations to the demands for new technologies, medical practices and healthcare systems that will require justification for all activities, expense and personnel.”10 Today, healthcare entities have shifted their focus from products and procedures to therapeutic and value-focused solutions, and the role of laboratorians has transitioned from that of service providers to problem solvers. However, the number of problem solvers is diminishing. In 2016, the need for new medical laboratory staff workers was forecasted to be 12,000 per year, with only an estimated 5,000 new laboratory technology students graduating annually.11
The resulting labor shortage, coupled with the pressure of value-based affordable healthcare and the increasing demand for testing, is leading laboratorians to rethink how they work.
While laboratory processes can differ, a typical pre- analytical, analytical and post-analytical workflow has more than 31 distinct steps (Figure 1). In most labs—particularly in small- and medium-sized facilities—many of these steps are still performed manually. This presents a problem. Simply put, if highly trained medical technologists are occupied with executing repetitive, labor-intensive and error-prone tasks within the workflow, they will not have time to collaborate with clinicians and directly contribute to improving patient care.
|Case Study I
|Result: Reclaiming one day a month by automating inventory management: a time decrease from seven hours to five minutes.
|Mason District Hospital in Havana, IL, is a 25-bed critical-access facility, processing up to 200 billables per day with a staff of four technicians. There, lab staff members were spending an average of seven hours a month on inventory management tasks. Despite this, products were often out of stock or expired, which resulted in the amassing of unplanned expediting and repurchasing costs. By automating inventory management processes, the lab has not only reduced its inventory-related tasks to just five minutes a month, it has done so while keeping stock reliably on hand, saving costs and providing consistent, quality patient care.
|Case Study II
|Result: Achieving an 80 percent auto-verification rate with cloud-based middleware.
|Faced with significant resource challenges, Jefferson Memorial Hospital, a 37-bed acute-care facility processing 50,000 samples per year in Louisville, GA, sought to improve turnaround times (TAT) and lab efficiency. By implementing a cloud-based middleware system, the lab was quickly able to achieve an 80 percent auto-validation rate. With eight out of 10 results going to the LIS without staff intervention, the lab was able to alleviate pressure from staffing shortages. In addition, with technologists only needing to manage results by exception, the lab realized a 27 percent improvement in TAT shortly after installing the system. Excitingly, these already-impressive, auto-validation and TAT results have continued to improve as the staff has continued to tailor the system to its specific needs.
When looking at automation solutions, laboratories of all sizes should consider future capabilities and ensure platforms are designed to accommodate growing test volumes, expanding menus and static or diminishing resources. To improve performance across all aspects of their operations, laboratories should seek automation solutions that comprise the integration of reliable instruments, intelligent track systems and cloud-based clinical informatics portfolios. Rethinking automation in this way will enable technologists to take a more influential role as patient-care team members and empower laboratories to expand into new disciplines as the needs of patients and healthcare organizations change.
Automation is about more than reducing the number of process steps and expediting the physical movement of tubes. It is about improving the ability of today’s laboratories to meet increasing demands by focusing on work that yields the greatest benefits for patients and healthcare organizations. What is most beneficial to tackling today’s laboratory challenges is a total workflow-optimization solution—a multifunctional approach that integrates instruments, tube movement and data management to automate pre-, intra- and post-analytical processes, as well as the work necessary to support them. By adopting this approach to automation, laboratories may be able to overcome resource constraints, manage increasing workloads, deliver accurate results and partner with clinicians to ensure patients receive the best healthcare possible.