The Great LIMS Challenge

Inventory problems, traceability issues and analysis mistakes – just some of the daily headaches facing lab managers across the land. So, can a LIMS really help with all this? You bet your hand-scribbled standard operating procedure it can says Trish Meek… 

Working in a lab can be extremely fast-paced and frenzied – leaving lab managers little time to focus on more than the day-to-day. Addressing broad, organisational efficiency challenges can seem insurmountable. This is especially true today, as shrinking budgets force many lab managers and analysts to do more with less. But there is a way forward: solving the obvious, everyday problems that stymie lab personnel each week can have tremendous payoff to the overall efficiency of a lab.

Challenge one: Inventory management Inventory varies from lab to lab, but is often fairly predictable within a single lab running certain tests and using consistent workflows. Let’s take a high-dollar consumable such as a disposable well tray. Suppose your lab routinely analyses samples for E. coli: you know what’s required for each workflow, you know how many tests are run each year, so you should know how many trays to keep in inventory. That’s the first step in the process – budgeting in advance based on historic patterns. Tracking what has been used, when and by whom is yet another critical, but often-ignored, step. Gas chromatography vials are a great example. Since these are in such high demand, lab technicians often hoard them, which disrupts the lab in costly ways. First, if inventory is depleted, it can have downstream impacts on other tests, affecting productivity. Second, the technician suddenly short of vials will likely hot-shot them to minimise disruption. This could mean paying double the price for expedited shipping. If this happened infrequently, the impact would be trivial, but it happens too often. The obvious answer is better budgeting and tracking, and this is where a laboratory information management system (LIMS) is highly effective. While it’s not easy to manage inventory – even with software – spreadsheets are simply not dynamic enough to establish an inventory management system that supports proactive planning/budgeting and up-to-the-minute accuracy.

Challenge two: Identifying analytical trends Identifying errors is hard. What’s more, the way many labs go about it is also error-prone, starting with the fact that they focus on solving errors after they occur. But it’s predicting and preventing errors – small, seemingly inconsequential ones – that should be the focus for labs. Errors that mask QA/QC problems, for example, can mushroom into much larger and systemic quality issues or create productivity gaps that eventually require costly reconfiguration. But how to know whether an experiment is out of spec – or trending that way – is especially challenging. Even the most experienced, high-achieving lab analyst is unlikely to discern subtle patterns and trends in data, especially at the scale most labs operate. And for those that can – or do – the analysis is retrospective, perhaps weeks after an experiment. Statistical quality control (SQC) absolutely must be built into whatever technology the lab uses each day. What this does is detect nonconformance trending before it reaches pre-defined thresholds, and this is the key for labs: real-time monitoring using statistical algorithms is critical for decision-making.

Challenge three: Controlling SOPs It takes time to develop and document standard operating procedures (SOPs), but failure to do so is a recipe for disaster. Laboratories cannot tolerate inconsistent application of procedures. Electronic SOPs (ESOPs) are the lab’s defense against techs “going rogue.” With SOPs defined in a LIMS there’s a rigid workflow with clearly defined technical corrective actions to ensure consistency and adherence to protocol. If these don’t exist – or the paper SOPs aren’t handy, clear or widely understood, it’s too easy for an analyst to err, even unwittingly. Calling SOPs one of the top problems to address in a lab is certainly not breaking news. Recommending how those SOPs are created, distributed and tracked for productivity and compliance purpose is a different story. Many labs still use paper, and that puts them at a distinct disadvantage. The good news, however, is that technology has evolved to a point where managing SOPs is easier and more efficient than ever.

Challenge four: Traceability A single laboratory may be responsible for hundreds of tests each week, if not more. And a test is not simply a test; it’s the sum of many parts. Defending data involves painstakingly retracing steps, many of which are so embedded in the fabric of the lab and its workflows that it may be impossible to isolate them. Imagine sorting through handwritten notes from fellow analysts and still not finding what could have gone wrong – it’s frustrating. But it’s also costly – analysts routinely spend a quarter of their productive time simply collecting data to defend a result. Thankfully, technology can do work in the background that can dramatically reduce the time, expense and aggravation associated with defensibility. Data management software enables measureable productivity gains, cutting into that 25 percent of time many must devote to defending their data. LIMS have come far from the days where labs relied on them for basic sample management and data reporting. Today, the LIMS reaches across an enterprise: it integrates with data in MRP, ERP and other enterprise systems in ways that directly impact defensibility. No more searching in multiple places; everything you need to defend your result is neatly organised for rapid analysis and reporting.

Challenge five: Instrument maintenance When many labs think of trend analysis, they don’t often associate it with instrument maintenance, but that’s a mistake. Data such as area counts, baseline conductivity and retention time provide valuable evidence that if trended and analysed can reveal much about the health of an instrument. LIMS offer capabilities that allow users to monitor instrument health so that work can be assigned more effectively on a regular maintenance schedule. Users are notified of upcoming maintenance – even of wear-part failure, so that maintenance can be schedule before failure becomes an issue. Analysts will tell you that they “get to know” their instruments, but sometimes signs are too subtle to sense the failure before it occurs and the instrument goes down. And with many labs cycling through new grads, transfers and others unfamiliar with an instrument type (or even a specific instrument), reliance on feeling or gut is risky. To understand what an instrument is telling you, you’re better off relying on data: if you set a sample point and watch for deviation, you’ve effectively given yourself an early warning system.

I visit many labs across many industries, and I can see that most still struggle with basic problems that have troubled labs since I worked on a bench years ago. And today the pace is even more frenetic and the demands on an analyst’s or lab manager’s time are even greater. So now, more than ever, it’s time to return to basics. Labs should embrace available technology to take a much more strategic, proactive and intelligent approach to what many consider routine.

The author:

Trish Meek is director of product strategy at Thermo Fisher Scientific.