Lab Digitalization: 5 Key Trends to Watch
The modern age of technology and integration comes with some useful tools and upgrades for lab digitalization.
As technology advances, more and more aspects of our lives are moving online. The laboratory is no exception. In this article, we will discuss 5 key trends that are driving the digitization of the lab and provide the best practices for each trend so that you can get started on digitizing your own lab.
What is Lab Digitization?
Lab digitization can be defined as the utilization of digital technology to document, execute and reproduce the research data more efficiently and coherently. A digitalized lab is one that is using digital technologies to change the way the lab operates in order to optimize its business model, and ultimately provide new revenue and value-producing opportunities.
Trends for Lab Digitization and Automation
Cloud Storage and data security
One of the most important aspects of digitizing the lab is transitioning to cloud-based storage and computing. Cloud storage is a digital way to segregate and store data starting from workflows to manuscripts on the internet. The most significant benefit of this type of data storage is that you can access and share your data from anywhere in the world. Security is always a concern when it comes to storing data online. In addition, by storing your data in the cloud, you can create multiple backups so that if one copy is lost or corrupted, you will always have others to fall back on. Best practices: when choosing an ELN with cloud storage, make sure they follow ISO regulatory guidelines and work in compliance with Good Laboratory Practice (GLP).
Robotics is another area where laboratories are beginning to see the benefits of digitization. Robotics can be used in a number of ways in the lab, from automating tasks to increasing accuracy and precision. For example, an autosampler can be connected to an HPLC in order to automate the injection of samples. This not only saves time, but also increases accuracy and precision by eliminating human error.
Best practices: when choosing a robotic system for your lab, make sure it is compatible with your other equipment and software. You should also consider the ease of use and maintenance when making your decision.
AI Revolution & Big Data Analysis
A revolution is underway in the life science and biotechnology industries, harnessing AI to increase laboratory efficiency, speed up process development workflows, and ultimately, enable discoveries in cancer research, vaccine development, and much more. An AI system would be able to study massive genome sequences and predict genetic disorders within no time compared to humans.
The sheer volume and variety of data being generated in the life sciences today are becoming increasingly difficult to manage and analyze using traditional methods. AI can help by providing the means to quickly identify patterns and correlations that would otherwise be undetectable.
Best practices: when choosing lab management software, make sure it has a built-in AI and machine learning capability, that will allow you to import, process and analyze big data.
No two laboratories are the same, which is why it is important to have an electronic lab notebook (ELN) that is customizable to your labs specific needs. An ELN should be able to integrate with your other software applications and equipment so that you can optimize your workflow. Additionally, an ELN should be flexible enough to accommodate the changing needs of your lab over time.
Best practices: when choosing an ELN, make sure it offers a large range of add-ons and integrations to support your unique lab.
Sustainability is an important consideration for any business, but it is especially important for laboratories that often use large amounts of paper, plastic and other consumables. Every dollar saved by minimizing consumption through digital inventory management means less waste being burned or deposited in a landfill. The world’s labs annually produce more than 5.5 million tonnes of plastic waste alone.
Digitization can help labs minimize their environmental footprint by reducing consumption and waste. For example, by transitioning to a paperless ELN, you can eliminate the need for physical note-taking. Additionally, by using digital inventory management, you can reduce the amount of plastic and paper waste generated by your lab.
Stamenkovic is a life science research analyst collaborating with Reader’s Digest, eLabNext, Dataversity & others. Having strong molecular biology, analytical & data science modeling skills she manages project initiatives to elevate the knowledge in technology solutions for laboratories, in order to increase efficiency in the lab. She focused on all topics regarding biotech but most recently specializing in topics such as digital health, lab digitization, ELN/LIMS, AI, Machine Learning, and automation’s role in developing novel therapeutics.