The Scientist’s Survival Guide:
Conquering steep lab system learning curves
Stuck with a lab system that doesn’t work the way you do? Keep reading to discover why steep system learning curves hold scientists back and how a platform designed for scientists can help accelerate discovery.
Your lab has been using the same LIMS for 12 months now, but you’re still not sure how to use many of the features that were promised. You still struggle to navigate core functions, and you often find yourself contacting IT to customize the system or work through an integration every time you introduce a new project or workflow. You thought a new solution would simplify your daily work, but you quickly realize this is not the case.
The steep learning curve associated with many lab informatics systems on the market is a major challenge for scientists and scientific organizations—the purpose of a LIMS or ELN is to make life easier for scientists, but many fail to fit the lab’s specific requirements and have interfaces that leave scientists wishing they could revert back to paper and spreadsheets. We covered this challenge in episode 4 of our #savethescientist video series. You can view the episode below.
So, how can your lab find an intuitive solution that delivers on its promise to make life in the lab easier for your scientists and fit into your business processes? Let’s discuss why many systems fall short when it comes to scientific usability, and what labs should look for in a solution that is built for your lab’s needs.
Why do many lab informatics systems fail to meet the needs of scientific users?
The effectiveness of a lab informatics system hinges on its ability to meet the needs of scientific users. However, many systems are plagued by a variety of shortcomings. Let’s delve into these challenges and their impact on life in the lab.
Lack of role-based customization
Many lab systems use a one-size-fits-all approach, failing to cater to the specific needs of different users. This leaves each user with an interface that fails to reflect their view of the world—forcing them to sift through tools and settings that don’t apply to their work and add complexity to the learning curve.
For example, a senior research scientist might be focused on reviewing experimental results and visualizing data, but may find the default interface cluttered with inventory management, sample tracking, and other features they don’t need—creating confusion and delays. A quality assurance specialist, on the other hand, needs to ensure that data is accurately documented and traceable, but may have to sift through complex analytical tools and sample processing workflows to get there. The result is inefficiency for everyone.
Questions to consider
- Do you often encounter features in your lab system that are not relevant to your role?
Complex user interfaces
Many systems used in modern labs were designed by and for IT specialists or lab operations team members—not for scientists. As a result, scientific users are left to navigate a solution that is overly complex and fails to work the way they do.
In turn, scientists often spend a disproportionate amount of their time looking for basic functions or struggling to complete routine tasks, and they lack the ability to configure their user experience in a way that meets their unique needs.
Furthermore, complex interfaces can stifle collaboration. The absence of shared dashboards and collaborative features forces cross-departmental discussions to shift to external platforms, complicating workflows and obscuring decision-making. Scientific teams end up working in isolation, unable to leverage their collective expertise.
Questions to consider
- Do you ever struggle to find basic features in your existing lab system?
Overly technical configuration
One source of steep learning curves has more to do with capability than usability. Discovery labs rely on a range of complex and specialized workflows, yet many systems aren’t capable of supporting these workflows from the start. This can require extensive customization, or even separate, third-party tools for seemingly standard capabilities like CRISPR design, sample tracking, and more. More often than not, extensive customization also means extensive custom code and system fragility.
Scientists without coding knowledge are forced to wait in the IT queue in order to customize the solution to their needs or work through system integrations. Furthermore, after extensive customization, many systems are never the same. The added code and APIs result in system fragility, which manifests itself with each passing upgrade.
In addition to delaying research, this obstacle also puts an outsized burden on research IT, who is left to prioritize and navigate a growing line of customization requests—all of which steal precious time and effort away from other IT priorities.
Questions to consider
- Have you ever had to contact IT to modify your lab system?
Poor documentation and training
Another problem with many lab platforms is a lack of adequate LIMS support documentation and training. It is important to remember that even the most usable LIMS is still an enterprise solution. Without thorough training, comprehensive support resources, and tools for self-help, scientists are not set up for success.
A lack of adequate upfront training and ongoing support prevents scientists from realizing the full utility of their system and requires them to spend much of their time wading through the outdated documentation they have available or contacting support every time they need answers.
Of course, the more intuitive a platform, the less scientists need to consult documentation and training resources. However, even with the most intuitive solution, scientists should feel empowered and well-supported.
Questions to consider
- Where do you go for ‘how to’ information on your LIMS?
- Can you access LIMS support easily?
How can the right platform shrink the lab system learning curve?
To #savethescientist from steep system learning curves, labs require a platform that is built for scientists and made to adapt. Here are some of the key features you should look for to ensure scientific usability is priority #1 in your platform of choice.
A platform that enables role-based customization with tailored user experience ensures that each scientist and user can readily access the tools and features that are most relevant to them. Rather than sifting through functionalities they don’t need, the scientist should be able to quickly navigate to the core capabilities that are required for their daily work.
Role-based views acknowledge that there is tremendous diversity across scientific disciplines and responsibilities. A platform cannot be truly science-aware unless it adapts to address the unique needs and realities of scientific users—even within a single organization.
For example, in a biotechnology research lab, the research scientist’s interface should readily link to a data-centric dashboard that allows them to view real-time experimental results. Conversely, the lab technician’s interface should provide streamlined access to sample storage and tracking. A bioinformatics specialist’s experience, by contrast, would deliver quick access to computational tools that are used to analyze large sets of data. With the right platform, each of these users can easily configure their experience while ensuring total data and workflow continuity organization wide.
A modern lab platform that is made for rapid scientific discovery should prioritize ease of use, with clear navigation, granular searchability, and AI-powered tools that allow scientists to quickly perform tasks and access the capabilities they need.
While some of the pillars of scientific usability translate across industries, there are certain aspects that are unique to science. For example, searchability matters in any enterprise platform. But in a lab platform, this searchability must have rich scientific context. Better yet, scientists should be able to tap into the power of AI to find what they’re looking for in seconds, then seamlessly generate powerful data visualizations that inform decisions and accelerate their work.
Ultimately, strong usability lies in knowing the user. That means a platform should work the way scientists do—with no artifacts that suggest it was originally built for IT.
For example, in an analytical chemistry lab, scientists deal with vast amounts of chromatographic data. A standard solution might effectively organize this data but leaves scientists with raw data that requires them to piece together meaning on their own. With a science-aware platform, scientists can get relevant, contextualized results, such as peak purity assessments, using simple queries in an AI-powered, chat-based interface. This empowers them to spend their time interpreting findings rather than collating information.
A true lab informatics platform is essential for fostering effective cross-departmental collaboration. By providing a centralized hub for data access and communication, these platforms enable seamless interaction among diverse teams, including research scientists, Quality Assurance (QA), lab operations, and IT.
With intuitive interfaces and real-time data sharing, respective departments can easily access the information they need while working together on the same platform. For example, a QA team can promptly review experimental results alongside research scientists, ensuring that compliance standards are met without delays. Meanwhile, lab operations can efficiently coordinate sample management with bioinformatics teams, streamlining workflows and enhancing overall productivity.
There’s nothing that promotes usability more than rich out-of-the-box capabilities that are available to the scientist with no additional customization needed. A science-aware platform should come ready to go from the start, with built-in scientific tools to support a wide variety of use cases. Tools for plasmid design, vector modification, multimodal entry registration, and other key capabilities should be built directly into the platform, enabling scientists to hit the ground running and do more in the platform (as opposed to adding on third-party tools for discrete functionality).
In addition, the right platform will empower scientists to leverage AI to set up new experiments and workflows in seconds, virtually eliminating time spent on manual experiment setup.
For example, a lab involved in antibody discovery requires tools for sample collection, characterization and cloning, protein purification, and more. With an advanced molecular biology toolkit readily available in their solution, they can start using the platform immediately, and then easily configure the solution and set up new workflows as their needs evolve.
Out-of-the-box capabilities accelerate adoption and enhance usability, but adaptability is an inherent part of research.
A true platform should empower scientists to configure their user experience and their workflows rapidly, without the need for IT intervention. With no-code configurability, scientists can make modifications with a conversational prompt in real time, then get back to their critical research. When a platform is made for configurability, even the most complex workflows can be supported, and adapting the system does not result in fragility.
Let’s view this in the context of a genomics lab that changes how their samples are tracked throughout the sequencing process. With a no-code platform, the lead researcher can simply log in to the LIMS, adjust fields for new sample types, and update notification triggers. The entire process takes just a few minutes. In the future, if the lab requires additional metadata fields to be captured, such as sample storage temperature, they can easily adapt the platform again to fit their requirements.
An effective platform should be so intuitive that training is optional. But it should also empower scientists to realize the full value of their solution through comprehensive training and support resources that help them learn and embrace new features and capabilities with ease.
Better yet, an effective solution shouldn’t turn requesting support into another item on the scientist’s to-do list. Instead, it should include built-in, AI-powered support that meets scientists right where they are. Scientific team members should have the ability to conversationally request and receive instructions in a simple, chat-based interface that exists directly within the platform.
For example, if a scientist isn’t sure how to set up a new sample in the system, they should be able to simply ask the solution’s AI chat assistant how to perform the task, then receive clear, step-by-step instructions in seconds with links to the relevant capabilities.
#savethescientist with Sapio’s science-aware platform
The best lab systems don’t just standardize data and automate workflows. They make life easier for the scientists that use them and seamlessly address the lab’s specific requirements in order to truly accelerate discovery.
Sapio’s no-code lab informatics platform was designed by scientists, for scientists. With an intuitive interface, built-in scientific tools, and AI-powered support with Sapio ELaiN, Sapio delivers the intuitive, science-aware experience scientists need to boost lab productivity without obstacles to adoption.
Ready to stop letting steep learning curves hold your lab back?
