The Digital Transformation of Lab Operations
While getting the kids ready for school, Janice takes a moment to look at her phone and sees a notification alert that one of the instruments in her lab is down. Her group had a busy few weeks ahead, so it was important that all systems were fully operational.
Janice revisits the service history of the instrument while still on her phone and sees a few repairs in the history log. She decides to place a service request, so she clicks through to the faults page, opens a drop-down menu, and selects “Place Service Request”. By the time Janice reaches the office, she is confident the information system will have set the service request in motion and soon things will be running smoothly again.
Janice finds a note on her desk from her analytical lead. “Confirming that we have notification that the service request was received, and help is on its way.” Janice smiles, marvelling at how much has changed since she started with the company, but also how it always comes back to the synergies between people and technology.
Although some aspects of Janice’s lab life are already upon us, the total integration of instrumentation and information systems is tantalizingly close in the future, a future that many have described as, Lab 4.0.1 And Lab 4.0 is built on the shoulders of the fourth Industrial Revolution that is occurring as we speak.
Where the first three Industrial Revolutions were largely mechanical in nature—with mechanization of manual labor, the advent of mass production, and automation—the implementation of computational tools and the digitization of data transitioned the world to the Information Age. But even with this wealth of data, information has largely remained siloed and efforts to act on it remained labor-intensive.
Just as the mechanical advances accelerated production, however, ongoing development of machine learning (ML) and artificial intelligence (AI) promise to rapidly accelerate translation of data into action. In doing so, these tools will transform every aspect of business, improving process efficiencies and output, reducing waste, increasing sustainability, lowering costs, improving price margins, and enhancing the work experience by better utilizing staff skills and aptitudes.
Transformative drivers
Several factors are influencing the fulfillment of this promise across all industries.2
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 systems are maintained and constantly upgraded by the provider, ensuring access to the latest functionality.
Moving to the cloud allows organizations to take advantage of the Internet of Things (IoT), which allows 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.
If everyone quantifies outcomes in the same way using the same metrics, 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.
The global desire for greater sustainability is also driving 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 future,3 benefits of AI to the lab scientist are seen as multivariate, assisting with experimental design and execution to 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 organization.4 But 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 management and scheduling, where systems monitor the utilization and wear-and-tear of equipment in real-time to maximize their activity and minimize downtime. Digitalization is also monitoring supply inventories and relaying smart alerts to lab managers to let them know that supplies are low and need reordering to ensure the lab does not run into delays that may impact not only the analytical lab but all other departments awaiting results.
As importantly, by leveraging AI and ML, digitalization facilitates real-time decision making on everything from adjusting applications and workflows to modifying upstream processes to managing downstream delivery and expectations.
And from a staff perspective, digitalization allows scientists to spend more time performing science and applying their expertise rather than losing valuable hours of each day to operational troubleshooting and manual reporting.
Having clear but flexible goals
Digital transformation is a complex undertaking, if only due to the diversity 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 and technological capabilities and capacities. Look beyond the standard operating procedures (SOPs) and protocols to document the practical realities of daily operations. Perform an in-depth survey of existing infrastructure, such as legacy instrumentation, robotics, and IT resources, as well as the physical premises. And contemplate 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.
Only with a complete picture of how the lab operates currently can you hope to 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 physical resources of the lab, make sure you have a solid understanding of the myriad stakeholders in lab operations and performance. Engage members of each stakeholder group to form a team that not only provides invaluable perspectives in system selection and implementation but also serves as project champions and in some cases, facilitates end-user training.
Identify possible solutions: A significant challenge in 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 an adaptable platform 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, change is not always universally welcomed. Implementing new digital practices can 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 loss.5 So, it is important to have a thorough and transparent communications strategy to smooth the buy-in process.6
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 may include online resources, training videos, in-person trials and even training via virtual reality. 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 depend on the synergistic connections between people and technology.
References
- Comeaga, M. L. Digital transformation of the laboratories. IOP Conf Series: Mater Sci Engin. 2022;1268:012001
- 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