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Automation takes on fine details, bigger picture July 2002 Automation is shifting gears. It is now slowly succeeding in the lab as a modular, task-specific, highly targeted solution, after passing through an earlier phase emphasizing total lab automation. Evaluating the impact of an automation system of any scale means measuring its ability to improve turnaround times, reduce errors, and justify its investment costs or return on investment. In most cases, this is a function of its impact on lab FTEs. "The way we look at lab automation is as a facilitation process," says Tom Adkins, vice president, sales and marketing, centralized diagnostics, Roche Diagnostics. "We don’t view ourselves as selling lab automation; we try to understand the needs and concerns of the laboratory and recommend a solution." Facilitating this process of listening, understanding, and recommending a solution can lead to different outcomes for different laboratories. Adkins explains, "Recommended solutions vary widely, ranging from the relatively modest, focusing on preanalytic sorting or perhaps back-end archiving, to the much more comprehensive, possibly involving automating an entire test line or even several lines." LAS to LIS? The LIS can make or break an automation project of any scope; automating a lab, even in a specific, modular way, invariably requires some degree of interface between the LIS and what is sometimes referred to as the LAS (lab automation software). For example, a lab may install a postanalytic automation system that knows the exact location on the exact rack in the back-end archiving refrigerator where any given tube is stored, as well as how much specimen is left in every tube stored there. But the LAS may not have been designed to share that information with the LIS; the data may be sitting in the LAS, but not in a way that makes it readily accessible from the outside. When the LIS receives an add-on order, it needs to somehow obtain information about the tube, the specimen, and the storage location to determine whether there is adequate specimen to perform that test—ideally without involving a human being. "And that is the rub," says Hal Weiner, president of Weiner Consulting, Florence, Ore. "Even today, when many LISs do have their own specimen inventory management tracking systems, that does not mean that they can effectively interface to the robotics system to tell it to check the tube, pick the correct specimen out of the refrigerator, and put it back on the appropriate instrument." Despite the very real potential for this particular process to be completely automated, in most cases human beings still need to be involved. And while an interface can be created between the LIS and LAS for this specific purpose, custom interface design is notoriously expensive and not all that applicable to other installations, given the embryonic state of the development of standards for exchanging information between the LIS and LAS.1 In short, even successful targeted, modular automation installations in the lab may raise a host of secondary implications, questions, and issues about information exchange with the LIS. Solving these secondary issues may involve additional, costly customization or require continued human attention. The lab’s ability to answer these questions efficiently is strongly correlated to the age and design of its LIS. Because of their design, older LISs are much more likely to fall short when asked to interact with automation software. "Many of the more traditional LIS systems cannot handle automation correctly," says Bill Blair, vice president, sales and marketing, SIA Corp., which markets the Molis LIS. "Molis was designed in the 1990s, and when we visit sites in Europe that use Molis, the extent to which the LIS and automation system are integrated is striking." In these sites, Blair reports, it is not uncommon to see relatively few lab employees and to find that those who are present typically are attending to exceptions rather than handling rote chores. One simply doesn’t see a lot of people tied up at keyboards, accepting normal results. "It’s pretty amazing to watch an automated lab that is doing 6,000 or 7,000 orders in a very short period of time and see maybe one or two technologists at consoles who are only reviewing abnormals because the LIS is successfully managing all the routine tasks required to keep the line moving," he says. Yes, agrees Gilbert Hakim, much of the success of any lab automation project depends on the capabilities of the LIS. Hakim is CEO of SCC Soft Computer. "Much of the gain, with respect to automation in general, comes from LIS-driven autoverification, from the ability to post results without human intervention. And that gain depends on the ability of the LIS to tightly capture all the information produced by the instruments in the LIS." However, LIS autoverification alone is not adequate to accommodate the complex requirements of a modern laboratory environment. "An LIS should also provide a sophisticated rules capability to properly manage the LAS-LIS-LAS interaction," Hakim continues, "particularly in high-volume labs handling the work of a large multifacility health care system. The database needed to handle these robust rules-based decisions is simply too extensive to be handled by robotics or process-control software. In a large installation, it may be necessary for rules to pertain to more than one facility, since patients often move among different locations, and in some instances, even things as basic as their medical record number may change as they do. "In the future, the LAS may reach a point where it can handle perhaps 60 to 70 percent of this need," Hakim says, "but then it will encounter other information management requirements, such as the need to review the last five years of patient history with diagnosis codes and test results, or accommodate QC across instruments from different manufacturers at multiple locations. While it may be possible in some robotics systems to establish a simple rule or two (i.e. rerun a test), the breadth and depth of database needed to truly manage patient information is just not there." This is where an LIS plays a vital role. Hakim points to several SCC installations that support his perspective. For example, over the last four years, in a two-stage process directed by Ralph Dadoun, St. Mary’s Hospital Center, in Montreal, Quebec, has automated many of its preanalytical processes.2 Today, 80 percent of the 2,400 specimens processed daily at St. Mary’s require little or no human attention. Turnaround times have been sharply reduced and exhibit much lower variability than they did before the automation project. Physician phone calls to the lab have declined by about 80 percent. Standard productivity measures, including those promulgated by the College of American Pathologists, show improvements ranging between 26 and 30 percent. According to Hakim, a key factor contributing to this success is the capability of the St. Mary’s SoftLab LIS. "We control the process," he says. "We actually physically drive the robotics in terms of telling it how many aliquot tubes we need, what volume we need, what the priority of the tube is, and so on." Dadoun notes that much of the benefit of automation pertains to transition time, a general term for the hand-off intervals that take place between each step in a process. For example, a technologist who brings a rack of tubes to the centrifuge but sees that it is already processing a previous load of specimens and still has six minutes to run is likely to set the rack down and move on to another task rather than idly stand by while the current spin cycle finishes. And having moved on to another task, she or he is not likely to return to the centrifuge exactly six minutes later—which means the centrifuge itself will sit idle until the technologist returns. Automation, by contrast, can ensure this never happens, that the period of time between the end of an automation device’s cycle and the transfer of specimens to the next step in the process is never more than some predetermined transition time—often a minute or less. In short, in the absence of automation, time is unnecessarily lost in transitions, which in turn leads to increased variability, more outliers, and turnaround time problems, lowering all productivity levels. The net result at St. Mary’s is that staff time is now mainly directed at tasks such as moving racks from preanalytical stages to analyzers and performing occasional reruns or reflex testing; being liberated from mundane tasks has led to higher job satisfaction and lower turnover rates among lab employees, reports Dadoun, who is vice president, corporate and support services for the hospital. A larger perspective Focusing on step-by-step, task-specific, modular automation begs a bigger question: Can automation help solve larger health care issues? At least one laboratorian is not only raising this question, but creating real-world tests to try to answer it. "The bottom line is that by 2007 the cost of medicine is going to be $2 trillion in this country," says Robin Felder, PhD. "It’s going to be a staggering figure—essentially unaffordable." And much of that cost is attributable to the fact that human beings still do what machines and software can do better. Cost control may be the benefit that finally makes automation a priority in health care. "If I was ever sure of anything, it is that medical automation will become a major focus of the health care industry in the next five years," Dr. Felder declares. "We have to move from where we are now, which is essentially a feudal approach to medicine, to a more integrated approach." Dr. Felder is professor of pathology and director of the Medical Automation Research Center, University of Virginia.3 Certainly this is true in the lab. "Many of the cost factors in the lab, in excess of the cost of the tests themselves, arise in the handling of specimens, from the time they are taken from the patient through running them through analyzers and storing them," says Dr. Felder. Eliminate human specimen handling and you eliminate about 70 percent of the cost—and most of the error as well, he argues. As the St. Mary’s story demonstrates, much of the productivity gain from automation is linked to the transitions between steps in a process. And those transitions, those points where a human being must hand off something—a chart, a specimen, a patient, a doctor’s schedule—to the next step in the process, are ubiquitous in any health care system. Automating the individual components of the process without looking at the whole process is akin to paving a cowpath instead of building a new road. Automating isolated pieces of a process or a facility leaves gaps, handoffs, and bottlenecks—places where simple, repetitive work must still be done by human beings. That’s hardly a winning strategy for the long haul. While manufacturing clearly has much to teach health care about making this transition, there are unequivocal differences between the two. "Health care differs from manufacturing in many ways," says Dr. Felder. For one thing, medicine does not deal with standardized components, particularly with respect to patients. Moreover, the underlying objective of the automation process in medicine is, in a sense, the diametrical opposite of that of a typical assembly line. A factory seeks to assemble parts into a whole; medicine, by contrast, often must first "deconstruct" the already completed "part," the patient, through processes and techniques ranging from drawing blood to biopsies. And in this process, notes Dr. Felder, no patient should feel that he or she is simply a cog in an impersonal assembly line. "It’s important to understand that the patient not feel that he or she is in a factory sense while they are being treated," he says. What’s interesting is that Dr. Felder sees automation as a key means to this end. "What they should experience, we believe, in the highly automated hospital of the future, is a much more personal, higher-quality interaction, because automation will make it possible for the individual who is interacting with them, treating them, to have more time to spend with them and understand their problem." Automation could, for example, provide medical variations on the theme of just-in-time resource management. "Right now there is no single entity that understands where the resources are in a hospital at any point in time and how they’re being deployed," says Dr. Felder. "We need to develop the technologies that will track resources, which could be anything from the doctor, the nurse, and the receptionist to the blood pump, the x-ray machine, the glucose meter." Such technologies will also understand and track these resources, including their capabilities and the information they provide. "Provided with real-time data on what your resources are, what their capabilities are, where they are, and what state they are in-active or inactive—the challenge becomes a solvable process control software problem, that of trying to deploy resources to obtain data and deliver services in the most efficient, optimized manner," Dr. Felder says. Here’s how a solution to that challenge might look. A nurse walks into a patient’s room and picks up a glucose meter. That event automatically triggers the glucose meter to turn on and self-calibrate. When the nurse then carries the meter to the patient and holds it near him or her, the patient and nurse are automatically identified to the meter and to one another, without anyone having to enter a single keystroke, and a glucose reading is taken from the meter. The data obtained is automatically transmitted to the hospital information system, appended to the patient’s medical record, and made available to anyone—physician, pathologist, billing department—who may need access to it. Because the patient has already been identified, there is no way to test the wrong patient. If the nurse is not trained to use the meter, it simply won’t operate. "What you’ve done in this scenario," says Dr. Felder, "is perform a medical procedure with a minimum of operator interaction, and you’ve obtained a high-quality result while at the same time preventing a medical error." The hospital of the future could, in short, help create an environment that is more human and humane, not less so, one in which skilled staff and clinicians are able to work at the pace that best suits them. "People vary," says Dr. Felder. "Some are very efficient and can get a lot done in a short time, while others may take longer but deliver a slightly higher-quality product." It may also reduce the amount of time patients spend in waiting rooms, rather than being treated. A patient who appears in, say, the x-ray suite should expect to find the staff and equipment ready and waiting for him or her, so precise may be the system controlling the convergence of patient, resources, and staff. In 1995, to further explore the application of manufacturing principles in medicine, Dr. Felder founded the Medical Automation Research Center. Now employing about 25 people, MARC has transcended Dr. Felder’s roots in laboratory automation and is now focused on the larger process of bringing medical automation technology into the health care system. "We’ve taken the broad view that the laboratory ought to be a team player in the automated hospital," says Dr. Felder. "While we spent the last decade or so looking specifically at laboratory automation, we’re now taking a couple of steps back to ask ourselves how the automated laboratory will fit into a continuum of automation throughout the hospital." Even the very idea of what constitutes a hospital is likely to change, says Dr. Felder. It is likely to become a much more ubiquitous part of the community and the environment. MARC is helping create "smart home" technology for a housing project that will continually monitor a number of quality-of-life measurements in its elderly residents. "What we’re trying to do is actually forecast disease, in the home, by doing a daily assessment," Dr. Felder says. "It’s an ongoing evaluation of quality of life, which includes not only health issues, but mobility and social activities." The monitoring technology has been installed in a home near the University of Virginia campus and is connected to Dr. Felder’s facility. Underway for about nine months now, the project is moving into a second phase involving a 31-unit low-income complex. Evaluating health and quality-of-life factors in residents may eventually help delay the transition to assisted care. Ultimately, a key goal is to help shape a viable health care system by exploring how automation can control costs. "For every month we can delay the day that our aging population moves into assisted care facilities or nursing homes in this country, we save approximately $2 billion," he says. References Eric Skjei is a writer in Stinson Beach, Calif. |
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