Laboratory digitization: from source to scientist


Integrating digital technologies into lab environments can ease workflow and improve data capture for researchers.

Whether it’s sending money to friends with a mobile payment app or tracking a door-to-door package delivery, digital technology is ubiquitous in the consumer world, so much so that consumers expect it. But technological advances that create a seamless customer experience have yet to catch on in the lab environment.

In a 2020 Accenture report surveying 128 life science industry leaders, 40% responded that they had not yet applied digital transformation efforts to R&D or quality control (QC) labs, and 37% more were still in pilot mode. The same survey found that 70% of respondents who have scaled or widely embraced digital transformation said they had met or exceeded anticipated business value (1). While the benefits of digital transformation are clear, there is still a long way to go.

Making this progress is essential. Laboratories are increasingly challenged to operate more efficiently. From executives who want to ensure labs operate efficiently and sustainably, to investor expectations for speed to market, to patient needs for affordable therapies, the more labs can eliminate inefficiencies in their process, the better they can meet any demands placed on them.

An additional engine for scanning comes from the bench itself. On average, scientists spend up to 42% of their time managing non-essential administrative tasks, including tasks such as procurement, inventory and equipment management (2). Every minute a scientist spends searching the reserves for a misplaced consumable is a minute their attention is diverted from discovery and innovation.

Additionally, labs also face broader trends, such as supply chain issues and sustainability. Inflation compounds the already rising costs of materials used to manufacture complex therapies. Changes in the workforce, whether it’s pressure to retain workers or shifting labs to hybrid or virtual teams, are also straining lab resources. These changes place more pressure on labs to find ways to leverage technology to perform tasks that don’t necessarily need to be handled by humans. Likewise, increased globalization – and the regulatory and logistical considerations it brings – challenges labs to operate with seamless transparency.

The frustration of fragmentation

In the world of automated manufacturing, production machines use sensors and controls to efficiently capture, filter, and package data. The data is then transferred through gateways to cloud-based analytics platforms that generate actionable data in real time, helping to create a veritable “factory of the future”. As a result, automated manufacturers can gain efficiencies, reduce time to market, and meet the ever-changing needs of their customers.

Yet in the laboratory environment, fragmentation of information and process flows persists. Experimentation platforms quickly and efficiently generate an ocean of data and results. However, the data collected is not necessarily easy to connect and integrate seamlessly. For example, scientists prepare and document their protocols differently on a wide range of tools, from paper to spreadsheets to electronic lab notebooks, and use various tools, such as literature or instrumentation, to n any step in the process. However, these tools often lack interoperability or the ability to exchange and exploit data between users.

This lack of visibility is a major challenge for laboratories. Researchers and lab managers may not be able to access timely information to identify trends or adapt to changing situations, resulting in increased costs, waste, and lead times. on the market. This can be attributed to several factors:

  • Sold outsIf a seldom-used product is suddenly needed and has not been re-ordered, or the stock of high-demand products is simultaneously exhausted by multiple research teams, this situation may interfere with or cancel valuable research work.
  • Overorders and Slow Inventory Turnover: To protect against stockouts and supply chain disruptions, many labs overorder and maintain excess inventory of products that are stored for months or longer until their expire and must be discarded.
  • Duplication of work: Teams from other labs may have run the same protocol but with no visibility, preventing other scientists from accessing this critical data. Also, they may have missed the opportunity to modify the running variants. This slows down productivity and leads to missed data opportunities.

Supporting an Integrated Digital Lab: Bridging the Gap Between Source and Scientist

Digital transformation that includes the intelligent integration of data systems can dramatically improve laboratory workflows, strengthen the supply chain, and give researchers more time to focus on science. Laboratories are already making progress in a key area: compiling instrumentation data into a system or data warehouse. By collecting this data in one accessible location, scientists can extract valuable instrumentation data as needed.

Laboratories are also increasingly embracing automation. The combination of automated technologies and data analytics on what scientists are doing and the protocols they are working on can generate real-time predictive analytics based on past trends. As a result, a lab no longer needs to wait weeks or months to identify trends or adapt to changing situations. Instead, they can quickly acquire and allocate resources, even across multiple sites, while giving researchers more time to discover and innovate.

The predictive data approach is based on historical data, such as order history, sample history, consumable consumption, and equipment usage. The next step in the evolution of laboratories will be an evolution towards prescriptive analytics. This approach combines the ability to predict based on past trends with more important factors, such as market trends or supply chain disruptions. The result is an overlay of what should be done now to offset what will likely happen in the future.

Considering some of the largest e-commerce companies in the world, it has been shown that these companies can predict consumer needs before those needs are known to the consumer. In a lab environment, this same type of digital transformation could identify, for example, an interruption in the supply of preferred test tubes – such as during the COVID-19 outbreak – and use artificial intelligence (AI) to recommend an available alternative that meets the protocols of the experiment.

Equipment is a crucial example of this, as it can be a major bottleneck if a lab does not have enough specific equipment or if many researchers in a lab want to use specific equipment at the same time. . A predictive approach to asset management begins by looking at usage history: are scientists using that asset more or less frequently? Do they favor certain types? How often does it usually break down? The answers to such questions are important for capacity planning and ensuring researchers have access to the tools they need.

In the not too distant future, a prescriptive approach in labs will expand the information needed to make recommendations, often with artificial intelligence. A prescriptive platform could, for example, recommend that a scientist change a filter on a particular mass spectrometer every three months to extend the life of the equipment from one year to potentially three years.

Along with a move toward prescriptive analytics, labs can leverage other types of technology to create more opportunities for researchers to focus on science. For example, virtual reality (VR) has the potential to act as a powerful collaboration tool. A scientist could wear a VR lens to see how an experiment should be conducted without having to be in the same physical space as another scientist. This kind of play-by-play immersion could be delivered through a recording of the experiment or in real time with a scientist in a lab around the world. The same value could be brought to equipment management, allowing a scientist with VR equipment to work with an offsite equipment technician to repair faulty equipment.

As researchers continue to innovate to improve the lives of patients around the world, technology will continue to contribute to this discovery. Whether it’s a virtual reality collaboration session with a colleague halfway around the world or AI anticipating solutions to supply chain disruptions in vaccine development, technologies digital transformation enable laboratories and their teams to bring treatments to market faster.

References

1. Accenture, Digital Transformation in the Lab: Bridging Analog Islands in a Digital Ocean, accenture.comFebruary 2020.
2. National Science Council, Reduce the administrative workload of researchers for federally funded research, www.nsf.gov(March 2014).

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