Driving laboratory efficiency with LIMS

Thermo Fisher Scientific

By Lauren Davis
Tuesday, 10 December, 2019

Driving laboratory efficiency with LIMS

In an ever-changing world, how can researchers use modern technology to their advantage without compromising the scientific method? Speaking at the 2019 Australasian Laboratory Management Conference, held in Rosehill Gardens in November, Daren Cumberbatch* revealed how laboratory information management systems (LIMS) can be used to streamline scientific processes and drive operational efficiency.

Cumberbatch explained that we are in the midst of the Fourth Industrial Revolution — a digital age characterised by trends including cloud computing, the Internet of Things (IoT), big data, social media, mobility and autonomy. Many of these trends are willingly followed by young people, who according to Cumberbatch “don’t understand the concept of waiting”, because everything they need is on their smartphones — an attitude that is now starting to leak into the business world. But rather than chastising the younger generation for their impatience, Cumberbatch argued that this mindset could be valuable if taken into the lab, where tradition still reigns supreme — in many cases at the cost of efficiency.

Cumberbatch explained that scientists are currently undergoing a “reproducibility crisis” in which different research groups are unnecessarily producing the same data, either because they do not know of this data’s prior existence or they cannot easily track it down — researchers thus need a more robust way of storing and sharing data with their peers. Data sharing is also essential for collaboration, Cumberbatch said, which is itself key to innovation — but collaboration and innovation take time, which a lot of scientists simply do not have at their disposal.

So how can scientists improve their operations to better enable sharing of data and free up time for collaboration and innovation? The answer, said Cumberbatch, is to assess your workflow and ask two key questions: “How can I automate any of these steps?” and “What’s preventing me from doing this in real time?” Then, use whatever technology you have at your disposal to make your dreams of an automated workflow into a reality. Such technology is in many cases readily available, and may include the camera in a tablet, which Cumberbatch claims to work as well as any scanner; Wi-Fi, to prevent the need for physically connecting various laboratory instruments; and, of course, LIMS.

LIMS can be used for a wide variety of tasks, according to Cumberbatch — all of which are vital to smooth laboratory operations but some of which may be considered more mundane than others. For example, inventory management functions mean that a LIMS can monitor all equipment in the lab and reorder when stock is low, ensuring scientists are never caught short. The system can also automate quality assurance/quality control workflows, ensuring experiments are being carried out correctly.

The largest bottleneck in any experiment, said Cumberbatch, occurs right at the end — with the manual entry of the results, as every single piece of data is painstakingly typed out. This is where LIMS can speed things up significantly, importing all results automatically. Advanced analytics functions are meanwhile designed to make it easier for scientists to assess these results, and the system should be easily searchable, thus improving traceability.

According to Cumberbatch, the future will see businesses increasingly divided into the haves and have nots of advanced technologies — and when it comes to laboratory environments, that could mean a substantial difference in the amount of work that is carried out on a day-to-day basis. With LIMS now available to take care of all those tedious manual tasks that were until recently a necessary evil, scientists have an opportunity to spend their time and their energy on longer-term, larger-scale assignments that have the potential to truly make a difference to the world.

*Daren Cumberbatch is National Sales Manager ANZ, Digital Science, Thermo Fisher Scientific.

Image credit: ©stock.adobe.com/au/WavebreakMediaMicro

Please follow us and share on Twitter and Facebook. You can also subscribe for FREE to our weekly newsletters and bimonthly magazine.

Related Articles

The importance of quantitative cell culture for successful experiments

The human eye is not good at measuring something quantitatively. Despite this, many cell culture...

AI algorithm assists in diagnosing skin diseases

South Korean researchers have developed a deep learning-based artificial intelligence (AI)...

Machine learning used to identify quality graphene

Engineers have developed technology that should help industry identify and export high-quality...

  • All content Copyright © 2020 Westwick-Farrow Pty Ltd