One of the biggest obstacles to paperless workflows facing today’s labs when attempting to embrace digital transformation (DT) is that many of their processes operate in data silos.
Lab analysts use multiple systems, which might include a Laboratory Information Management System (LIMS), a scientific data management system (SDMS), an Electronic Lab Notebook (ELN) and other platforms designed for different purposes or processes.
No matter how effectively any one of these systems may perform the job for which it’s designed, siloed systems start creating problems when you need to get a consolidated view or create actionable insights from the data that’s residing in these different places. To do this, you need a data platform capable of performing these integrations.
This is where advanced data analytics platforms like LabVantage Analytics come into play. These systems use modern techniques such as machine learning (ML) and artificial intelligence (AI) to help solve some of the most complex problems facing today’s labs.
Leveraging Analytics
Analytics can be applied in a variety of ways. Visualizing consolidated data in the form of pictures, graphs, charts or dashboards, for example, enables more effective analysis and collaboration. On a more complex level, statistical algorithms like neural networks are designed to think intuitively like a human mind, rapidly sifting through data to uncover relationships that might otherwise remain hidden.
Additionally, tools like data science, AI, ML, and other components like computer vision can analyze video, images, spoken words, and many other types of multimedia to identify trends and make actionable predictions.
Here’s an example of how these technologies can be applied in a laboratory setting.
Let’s say that you have two chemicals, which we’ll call X and Y, that your R&D lab typically mixes in certain proportions at a certain temperature to create a compound called Z. Depending on the chemical properties and behaviors of X and Y, there may be many possible permutations and combinations a scientist could potentially use to optimize the production of Z. In addition, the final form of Z may need to behave in a certain way under different conditions, for example, in different temperatures, air pressure levels, and so on.
If your lab has historical scientific data of the chemical properties and behaviors related to this process, AI can help you develop new and improved formulas. For example, it can suggest specific combinations of chemicals and conditions that are more likely to yield the desired results. By narrowing down options, this process can save the researcher a significant amount of time, enabling him or her to work more efficiently, effectively and quickly to produce the desired result.
AI can also be applied to change the original compound or create new ones. Say you want to replace chemical Y with something else. How would using chemical A, B or C change compound Z? AI can predict each of these outcomes, narrowing down the options, saving your research team time & money and significantly increasing your chance of success.
Analytics also offers similar benefits to quality control (QC) labs. Let’s say you have a meat processing company with a QC lab responsible for ensuring products are shipped untainted by bacteria or other contaminants. One of your lab’s critical jobs is to test all the surfaces meat products can encounter for contaminants. No QC lab can check every part of every surface every day, so the operation must rely on representative samples.
Analytics can make this task more efficient and make the entire operation safer by applying AI and data science algorithms to find patterns. By analyzing historical data, the system can predict where contamination is likely to occur. For example, it can predict from which surface — or even which corner of the surface — a swab should be taken on a given day.
What to Look for in an Analytics Platform
In order to take DT to the next level, your lab’s analytics solution should feature robust capabilities in two key categories: business intelligence (BI) and AI.
Look for BI capabilities that analyze key performance indicators (KPIs) related to lab performance monitoring and efficiency improvements.
The system should be able to track the performance of individual lab analysts, overall lab performance, how much time and cost is being spent for each sample test, and so on. By monitoring this data on a daily basis, BI builds a picture that provides insights into how your lab can improve its efficiency. Best of all, your consolidated data can be represented in a visual way that’s easily understood by users with various levels of expertise.
While BI helps you to make smarter and faster decisions by visualizing your data, AI helps you to address more complex problems by making predictions and recommendations. It does this by identifying patterns in your data and determining correlations. Advanced processes that used to require hours of work by data scientists can be applied in seconds to help you make smarter decisions.
To learn more about how LabVantage Analytics can transform all your lab’s data into actionable insights, visit our analytics page or contact us today.
