Orchestrating Your Laboratory’s Scientific Ecosystem

17 June 2021 | Thursday | Opinion | By Cheryl Bartel

Life science companies and solution providers gathered at the virtual Paperless Lab Academy 2021 (PLA2021) event in April to discuss the future of integrated scientific data management systems.

Thermo Fisher Scientific supports the Paperless Lab Academy and hosted a webinar as part of the PLA2021 virtual meeting. In this workshop, we discussed how the digital transformation of laboratories is streamlining scientific progress, and how Thermo Fisher’s automation and data management solutions can help laboratories to transform their work environment. The workshop has been recorded and can be viewed by registering here. We’ve also provided an overview of the key messages below.

Digital transformation streamlines laboratory science

Digitalization and automation have rapidly accelerated the efficiency, quality and throughput of laboratories in recent years. Today, new software and hardware technologies are being embraced across many sectors, and laboratories are continuously adopting new instruments and methods of data acquisition, and implementing digital systems to streamline workflows. Advanced digital solutions, like cloud-based services and data analysis software, alongside automation hardware, such as robotics, are just some of the examples of the tools modern laboratories are using to accelerate scientific progress.

The current pandemic created a tipping point for digital transformation, and 77% of CEOs reported the COVID-19 crisis accelerated their digital transformation plans. However, adding new technologies and instruments to laboratory environments can create more complexity and disrupt existing workflows, so it’s important to adopt these changes in the most effective way for each individual laboratory. To best support scientific innovation through successful digitalization, laboratories need to address these essential questions:

  • How do we improve the scientist’s experience of using hardware and software to simplify and streamline workflows?
  • How do we improve automation and implement end-to-end data management so that we can more effectively use advanced analytics and AI?

The three pillars of automated science

Increasing the automation of workflows and data management is a key step for laboratories to modernize. Automated systems enhance processes and reduce the time scientists spend on low value-add tasks, freeing up time for more meaningful work to leverage specialized training and increasing the throughput of scientific research. Automation is a complex process, but it can be broken down into three pillars:

  1. Physical automation. The implementation of physical automation technologies like robotics, automated high-throughput screening devices and smart workflows streamlines testing and analysis. To fully take advantage of this, physical technologies should integrate with digital software to automate and connect workflows from end-to-end.
  2. Digital infrastructure. Digital solutions help to connect instruments, cloud-based technologies, and centralized databases so that they can be accessed and shared between multiple devices. When done correctly, digital software should work with, not against, the laboratory so that software and physical instruments go hand in hand.
  3. Artificial intelligence (AI). AI technologies help laboratories to derive greater insight from their experimental and operational data by using advanced analytics and predictive science.

A step-by-step process to full digital transformation

The journey of digital transformation is different for every laboratory. This transformation can be challenging, and projects aimed to integrate software and hardware systems often encounter problems. For example, projects can see delays, high costs, and result in disconnected user experiences and inconsistent audit trails. To avoid this, laboratory changes should be approached systematically based on the resources, budget, and unique goals of a laboratory. While the specifics may vary between organizations and laboratories, there are three key organizational steps to implement a successful transformation:

  1. Connectivity. This begins with connecting everything in the laboratory, including the instruments, software, people, and processes. Using informatics tools like a laboratory information management system (LIMS) and electronic laboratory notebooks (ELN), laboratories can build a digital science workbench, where data is available and open, but still secure.
  2. Automation. The next step is implementing automated instruments and workflows, and connecting these systems into the digital laboratory. Laboratory automation is designed to increase productivity, standardization and reproducibility through reducing manual tasks and minimizing potential human errors.
  3. Intelligence. Finally, integrating automated physical and digital platforms to tools like e-commerce, data analytics and supply chains builds one intelligent ecosystem that provides predictive support for scientists.

Continuing digital transformation

Many laboratories see digital transformation as a vital way to increase the quality and efficiency of scientific work. However, implementing digital and physical technologies to automate and enhance parts of the scientific process is challenging as it can disrupt existing laboratory structures. As such, scientists must understand how transformation can be effectively implemented in their laboratory set-up to achieve their specific goals. Thermo Fisher’s automation and data management solutions are built to connect all aspects of the laboratory ecosystem, including people, instruments, processes and data, in ways that are fully adaptable and controllable to help accelerate digital transformation.

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