The Digital Transformation of Lab Operations

While getting the kids ready for school, Janice took a moment to look at her phone and saw a notification that one of the instruments in her lab was down. Her group had a busy few weeks ahead, so all systems needed to be fully operational.

Revisiting the instrument’s service history while still on her phone, Janice sees a few repairs in the history log. Deciding to place a service request, she clicks through to the faults page, opens a drop-down menu, and selects “Place Service Request.” By the time she reaches the office, she’s confident the service request will be in motion, and soon, things would be running smoothly again.

With the kids dropped off, Janice arrives to find the lab sparsely populated, but there’s a flurry of activity as robots, samplers, and spectrometers plied their trade without human supervision. Here and there, she sees a few lab-coated individuals doing physical surveys of solvents or sample lines. But most of the team, she discovers, stands over computer monitors discussing last night’s test results and speculate on what process changes the development team needs to make to get everything back to spec.

At her desk, Janice finds a sticky note from her analytical lead. “Confirming that we have a notification that the service request was received, and help is on its way.” Janice smiles, marveling at how much has changed since she started with the company but also how it always came back to the synergies between people
and technology.

While certain aspects of Janice’s lab life are already present, the complete integration of instrumentation and information systems is tantalizingly close in the near future. Many have described this impending era as “Lab 4.0”¹ which stands on the sturdy foundation of the fourth Industrial Revolution.

During the first three Industrial Revolutions, mechanical advancements revolutionized labor, from manual work mechanization to mass production and automation. However, the integration of computational tools and the digitization of data ushered in the Information Age. Despite this wealth of data, information often remains isolated, and efforts to act upon it continue to be labor-intensive.

As mechanical advancements once accelerated production, the ongoing development of machine learning (ML) and artificial intelligence (AI) promises to rapidly translate data into actionable insights. These tools are poised to transform every facet of business: enhancing process efficiencies, reducing waste, increasing sustainability, lowering costs, improving profit margins, and optimizing the work experience by leveraging staff skills and aptitudes.

Transformative drivers

Several factors are influencing the fulfillment of this promise across all industries² including:

Cloud computing removes the need for companies to maintain expensive data centers, transferring that responsibility to cloud service providers who can operate at scale and provide some degree of security.
Aligned with the cloud is the growing business case for Software-as-a-Service (SaaS), where organizations can readily access and test software platforms as they refine their processes, discovering and scaling the solutions that best fit their needs. The other benefit of SaaS is that the providers maintain and upgrade the systems, ensuring access to the latest functionality.
Moving to the cloud allows organizations to take advantage of the Internet of Things (IoT), which enables technologies to connect and communicate without concern for distance. Tapping into this metaverse allows employees to access information from anywhere and collaborate within and between organizations.
Such distributed access, however, demands increased cybersecurity. As companies move from legacy systems and processes, there is a trend toward making cybersecurity part of the initial development cycle rather than adding it as a separate layer after systems are in place.
The digital revolution will also require standardization and harmonization to ensure that platforms across organizations, industries, and global regions generate compatible data that can be integrated into a fuller picture of the undertaking.
Suppose everyone quantifies outcomes using the same metrics. In that case, regulatory processes will become more streamlined as agencies will be better able to compare new submissions with previous benchmarks. Furthermore, submissions will be facilitated by more complete and transparent data audit trails and the potential for real-time, on-demand, organization-wide reviews of systems and processes.
Global desire for greater sustainability also drives the digital revolution as organizations seek, and government agencies demand, reductions in ecological footprints. Beyond simply evolving processes to reduce the use of hazardous and toxic materials, organizations are also looking for ways to optimize the life cycles of their lab equipment. Digital connectivity can ensure optimal use by measuring wear and tear in real time, facilitating maintenance as needed, and minimizing downtime.
Synergies between these advances and legacy operations are identified and exploited by the AI and ML tools in alignment with the expertise of the on-site staff, who can provide insights and practical realities that cannot yet be easily measured and digitized. In one description of the lab of the future³, the benefits of AI to the lab scientist are seen as multivariate, assisting with experimental design and execution of how data is captured and analyzed, as well as augmenting those efforts by providing new insights and suggesting new areas to explore.

Digital progress in today’s analytical labs

Digitalization is cloud-connected hardware and software that optimizes every step of the process, from samples arriving in the door to results being accessible to stakeholders throughout and, where appropriate, beyond the organization⁴. However, more than simply accessing the data, it is the ability to process, analyze, and visualize the information within the lab and with collaborators who can integrate the results with outcomes in their labs.

From an operational perspective, digitalization is also asset and resource management and scheduling where systems monitor the utilization and wear-and-tear of equipment in real time to maximize their activity and minimize downtime. Similarly, it monitors supply inventories and smart alerts to ensure the lab does not run into costly delays that impact the analytical lab and all other departments awaiting results.

More importantly, by leveraging AI and ML, digitalization facilitates real-time decision-making on everything from the adjustment of applications and workflows to the modification of upstream processes to managing downstream delivery and expectations.

From a staff perspective, digitalization allows scientists to spend more time performing science and applying their expertise rather than wasting valuable hours each day on operational troubleshooting and manual reporting.

Having clear but flexible goals

Digital transformation is a complex undertaking, if only due to the multiplicity of influencing factors. Having clear but flexible goals can help increase the likelihood of success, as will a clear strategy moving forward. External advice from those with expertise in such transformations, whether from consultants or trusted vendor partners, can be invaluable in formulating that strategy as they can offer perspectives informed by broad experience and unbiased by organizational history.

Some important considerations when developing a successful strategy include:

Know where you are: Understand your current practices, technological capabilities, and capacities. Look beyond the SOPs and protocols to document the practical realities of daily operations. Perform an in-depth survey of existing infrastructure, such as legacy instrumentation, robotics, IT resources, and physical premises. Contemplating not only existing key performance indicators for the lab but also potential future metrics you may wish to monitor as your lab evolves and/or expands to accommodate changing organizational goals is also important. With a complete picture of how the lab operates currently, you can identify opportunities to introduce new or augment existing digital approaches.

Know who is involved: A key factor in implementation failures is people unable or unwilling to use the new platforms. Looking beyond the lab’s physical resources, ensure understanding of the myriad stakeholders in lab operations and performance. Engage members of each stakeholder group to form a team that provides invaluable perspectives in system selection and implementation, serves as project champions, and, in some cases, facilitates end-user training.

Identify possible solutions: A significant challenge of identifying potential digital solutions is recognizing that you are not just digitalizing your existing operations but also trying to think ahead to future demands. Thus, while you are looking for systems that can seamlessly integrate with your processes, equipment, and infrastructure, you also want a platform that is adaptable in terms of both scale and the introduction of new technologies or regulatory requirements. This may require a solution custom-designed or -adapted by in-house experts, partner vendors, or consultants.

To lower the risk of creating digital siloes, it is also important to understand what is happening outside of your lab in the rest of the organization or at least in those parts that feed your lab or rely on the output of your lab. This can include other labs but may also involve, for example, IT, finance, and purchasing.

Prepare the field: No matter how positive the expected outcome is, change is only sometimes welcomed. Implementing new digital practices may intimidate end-users and other stakeholders who have spent years doing things the old way. Digitalization and efficiency enhancements may also elicit concerns about downsizing and job loss⁵. Thus, it is important to have a thorough and transparent communications strategy to smooth the buy-in process⁶.

People need to understand how digitalization projects will enhance their jobs and feel assured that the new systems will be accessible. Adaptive learning materials will be critical to this effort, and they may include online resources, training videos, in-person trials, and even virtual reality training. The communication strategy should also flow in both directions, allowing stakeholders to contribute to the effort and feel that their questions and concerns have been heard and addressed.

As futuristic lab manager Janice observed, successful digital transformation and Lab 4.0 ultimately come down to the synergistic connections between people and technology.

References

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Jovičić, S. Ž. and Vitkus, D. Digital transformation towards the clinical laboratory of the future. Perspectives for the next decade. Clin Chem Lab Med. 2023;61:567-569
Shute, R., and Lynch, N. The next big developments – The lab of the future. In Digital transformation of the laboratory: A practical guide to the connected lab. Eds. Zupancic, K., Pavlek, T., Erjavec, J. Wiley-VCH GmbH. 2021
Realizing the digital lab today. Lab Manager. January 19, 2023. (https://www.labmanager.com/realizing-the-digital-lab-today-30625; Accessed February 16, 2024)
Marsh, E., Vallejos, E.P., and Spence, A. The digital workplace and its dark side: An integrative review. Comp Human Behav. 2022;128:107118
Trenerry, B., Chng, S., Wang, Y., Suhaila, Z.S., Lim, S.S., Lu, H.Y., and Oh, P.H. Preparing workplaces for digital transformation: An integrative review and framework for multi-level factors. Front Psychol. 2021;12:620766