Jeanne Mensingh reports on the mounting costs of everyday lab problems and how to avoid them
If you’ve worked in a lab, you know firsthand that it can be frenetic, fast-paced and, at times, overwhelming. And this is especially true today, as shrinking budgets force many lab managers and analysts to do more with less. But there’s a path forward, and it starts by looking at the lab holistically, solving the obvious, everyday problems first.
Problem one: inventory (mis)management
Inventory varies from lab to lab, but is often fairly predictable within a single lab running certain tests and using consistent workflows. You know what’s required for each workflow, you know how many tests are run each year, so you should know how many consumables 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. First, if inventory is depleted, it can have downstream impacts on other tests, affecting productivity. Second, the technician suddenly short of materials will likely hot-shot them to minimise disruption. This could mean paying double the price for expedited shipping.
The obvious answer is better budgeting and tracking, and this is where a laboratory information management system (LIMS) is highly effective. Spreadsheets are simply not dynamic enough to establish an inventory management system that supports proactive planning and budgeting. With Thermo Scientific SampleManager LIMS, for example, labs can carefully track inventory as part of a comprehensive lab management programme.
Problem two: missing 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.
Statistical quality control (SQC) must be built into whatever technology the lab uses each day. SampleManager LIMS, for example, has that functionality built into the core LIMS platform. It detects nonconformance trending before it reaches pre-defined thresholds — which is critical for decision-making.
Problem three: uncontrolled 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 defence against techs ‘going rogue’. With SOPs defined in SampleManager LIMS, for example, there’s a rigid workflow 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.
Problem four: measurement 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. LIMS have come far from the days when 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.
Problem five: misunderstood maintenance
When 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.
Many labs still struggle with basic problems that have troubled them for years. And today the pace is even more frenetic and the demands on an analyst’s or lab manager’s time are even greater. So 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.
For more information, visit www.scientistlive.com/eurolab
Jeanne Mensingh is president and founder of Labtopia Solutions, Thermo Fisher Scientific Partner.