These days, there is so much conversation about the power of lab automation. A study published in the Journal of Healthcare found that when comparing two similar labs, one adopting a total automation system and the other not, the number of tests performed per single worker increased to an average of 1.4 and 3.7 times in the case of total automation.
Similarly, a paper published in the Association for Clinical Chemistry noted that following the installation of automation, error reduction rates surpassed 70%, while staff time per specimen collection was reduced by over 10%.
There’s also an abundance of research illustrating the general benefits of automation including improved quality of testing, better sample management processes, lower costs long-term, and more.
In this post, we walk through some of the signs that point to a need for automation and offer some suggestions to help scientists think through the implementation process.
How do you know you need to automate?
There are several key indicators that it may be time to automate some or all aspects of the lab’s workflow:
- Too much time spent on repetitive tasks. If scientists spend a lot of their time in the lab fetching samples, pipetting fluids, moving data, and recording results, automating those tasks might be a good next step. In addition to freeing scientists from non-productive tasks, automation can be more cost-effective in the long run.
- Lower productivity: If scientists have tight deadlines and a high volume of work, automating parts of the workflow may help them keep experiments on track.
- Growing project costs: If the lab’s operation costs are climbing while its output stays the same, it might be worth reviewing existing workflows to find areas where automation can be helpful.
- High error rate: Lab workflows are complex, multi-step processes and if they involve a lot of manual tasks, errors are bound to occur. Tracking all those steps and samples is daunting even for the most conscientious scientist. Automating some of those manual tasks will reduce the likelihood of errors.
What should you think about when getting ready to automate?
Deploying lab automation effectively often rests on a few key pillars.
- Mindset: Automation shouldn’t be an afterthought. It is important to think proactively about how automation can streamline lab operations and workflows. That thinking should help guide how scientists set up the lab; for example, they might use plates instead of tubes for samples. On the other hand, automation can and should evolve as your process does. So, keeping an open mindset to change will keep an automated lab working efficiently.
- Automate your highest priority first: The goal might be a fully automated lab with all the bells and whistles. But it can be a mistake to try to automate everything immediately. It may not make sense to automate everything in the lab. It’s important to first prioritize lab pain points and automate the highest priority first, then slowly integrate more automation steps over time. Starting small by choosing a few key steps to automate also eases scientists into working with the new technology rather than requiring them to make a radical shift in how they work from the start.
- Budget: While it is a significant investment, the benefits in terms of greater productivity and fewer mistakes in the lab almost always offset the expense in the long run. This also goes back to the key pillar of automating your highest priority first. Understanding which step(s) in the lab will yield the biggest bang for your buck by way of automation can help make the case for the up-front cost of automation.
5 key benefits of automation
Automating manual processes frees scientists to think and work more strategically in the lab. But that’s just one of the ways that incorporating automation benefits everyone.
- Scientists spend less time on low-value tasks and more time analyzing data and results.
- Productivity increases in the lab since automated systems can keep running experiments outside work hours.
- The potential for errors in the lab will decrease since there aren’t as many processes that require human intervention.
- Better data management is enabled through the automated capture of experimental data and metadata, which can be seamlessly transferred between systems and software in the lab.
- Automation makes it possible to connect people, equipment, and workflows in labs across research facilities or even campuses.
Typical tasks that are often automated in the lab
Thoughtful automation infrastructure should be able to evolve as lab processes change. Biosero engineers designed the Green Button Go solution to be flexible enough to support scientists’ immediate and future automation needs. Some tasks in the lab are simpler to automate than others. Knowing the difference will save a lot of time and frustration. Listed below are some good candidates for automation:
- Assays with well-established protocols and processes.
- Experiments that use the same workflows and similar samples.
- Workflows that involve a lot of data capture at different points in the process.
- Experiments and workflows that take a long time to run or need to run overnight.
- Assays that use very small and precise quantities of fluid or sample.
- Assays that involve the use of infectious samples or toxic reagents.
There may be other tasks unique to your lab goals that could benefit from automated solutions. Speaking with an automation expert can help your team figure out what tasks and workflows would be best for your needs.
What kinds of tasks should not be automated?
Automation allows for more flexibility and productivity in the lab, but it isn’t always the best option. Given the investment involved in automating a lab, it’s helpful to make a list of tasks that can and should be done manually. Here are some examples of tasks that may not be best suited for automating:
- Assays that only need to be performed once or a handful of times.
- New assays that need to be done quickly where there isn’t time to automate.
- Tests that require scientists’ expertise and feedback at different times in the process.
- Unique assays and tests where the outcomes are unknown or variable.
- Tasks that require a lot of human dexterity or nimbleness
Getting started with lab automation
Every lab has its own unique automation needs. Here are some ways to get started identifying what kind of automation a lab might need:
- List all the automation opportunities in the lab. Review the lab’s processes, workflows, and goals. Highlight areas where things could be done more efficiently and where the lab has the most ground it can make up via automation. These may be good candidates for high priority automation tasks.
- Talk to the scientists in the lab. These are the people who will actually use the automated solutions, so it’s important to get their perspectives. Get as much information as possible about their needs and where they could be more productive.
- Keep it simple. There are a variety of automation solutions available on the market. Figuring out which ones work best for the lab can be challenging. It can be helpful to start by automating one small task, seeing how that fits, and building from there.
- Evaluate the available automation solutions. The best option for the lab may be a combination of solutions rather than a single one. Reach out to different vendors to discuss your automation needs.
- Get community feedback. We also recommend speaking with colleagues in the same industry and gathering feedback about their experiences with automation.
Want to learn more?
We’ve got a few resources picked out for you.
Virtual or live tours of lab orchestration systems
Sometimes you just want to experience the thing before you buy it—like the test drive on a new car. Biosero Acceleration Labs provide you with the opportunity to test drive an automation concept before you actually bring it home. Virtual and in-person experience are available.
Get Biosero’s help launching orchestration for your lab
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