LABORATORY INFORMATICS
defined processes, for example to improve security, compliance, reduce manual errors or save time.’ Then it is possible to look at how
software can do that in a commercially reasonable timeframe. ‘No one wants to sit around and look at projections and implementation plan PowerPoint slides for a year or more. They want to see progress in the short term, and demonstrate real value driver points in three to six months.’
Change and cultural challenges Another major consideration is the change in working practices that digitalisation will inevitably engender. Whether that’s moving away from the reliance on paper, pen and excel spreadsheets, or replacing existing, but outdated legacy systems with modern platforms. ‘In fact, some of the biggest challenges aren’t technical at all, but cultural,’ Curtis noted. ‘You’re asking a lab scientist who has
been trained to execute a method, design an experiment, or write a report in the same way for potentially decades, to change their working practice from the ground up. That’s quite an expectation. Even if you have technical and supervisory support, it’s important to clearly define a value-engineered roadmap that shows
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“It is generally more manageable to structure digital projects in bite- sized chunks that can be achieved in a realistic timeframe, than trying to boil the ocean”
each individual how, by changing their behaviour, they are going to impact on their own working practices, and the overall process.’ But again, do it in bite-sized chunks, and it becomes less daunting. ‘Everyone needs to understand the benefits in the long run, and people are just as much part of that transformation and journey as the software.’ Not trying to change everything in one
go – like the entire analytical operation of an organisation – also makes the process less intimidating from a user perspective. ‘Maybe start with a single laboratory that is part of that analytical engine in an organisation. Take this one step further back and you may even want to identify an analytical step within that lab, perhaps. ‘For example, how does a chemist submit essential material for
characterisation? There you have that easy-to-manage chunk but it’s still a workflow and you can map it out from end to end. How do the samples come in? How do the materials go through the workflow? What do we produce to then send on to the next group? By looking at these processes in stages, we can define what each package of data actually looks like, and that’s a really digestible piece of a problem … you’re not trying to eat the whole elephant.’ And this user experience leads to the issue of training, Curtis said. ‘It’s all very well implementing a new system, but how do you ensure that everyone knows how to use it and is comfortable doing so? One thing that the pandemic has demonstrated is that there is no longer the need to bring everyone into a classroom over multiple days and try to teach by rote in an external environment,’ he suggested. People need to be confident in their
own workspace, he continued. ‘Smart companies are investing in learning management systems, so the people using a new software or informatics platform can continue to learn as they are using it, and become confident in the workplace, which is, after all, where they are using it from day to day.’
Autumn 2020 Scientific Computing World 19
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