Transforming the lab into a tightly integrated paperless environment gives users real- time access to information, automates processes and reduces manual data handling
Jeanne Mensingh reports on the mounting costs of everyday lab problems and how to avoid them
I
COST BENEFITS of LIMS
f you’ve worked in a lab, you know fi rsthand 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 fi rst.
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 workfl ows. You know what’s required for each workfl ow, you know how many tests are run each year, so you should know how many consumables to keep in inventory. T at’s the fi rst 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, aff ecting productivity. Second, the technician suddenly short of materials will likely hot-shot
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them to minimise disruption. T is could mean paying double the price for expedited shipping. T e obvious answer is better budgeting and tracking, and this is where a laboratory information management system (LIMS) is highly eff ective. Spreadsheets are simply not dynamic enough to establish an inventory management system that supports proactive planning and budgeting. With T ermo Scientifi c SampleManager LIMS, for example, labs can carefully track inventory as part of a comprehensive lab management program.
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 reconfi guration.
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-defi ned 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 defi ned in SampleManager LIMS, for example, there’s a rigid workfl ow 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.
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