The lab of the future will be more cost effective, produce higher quality results, reduce errors, and instantly comply with regulations. To take advantage of technologies that will be fundamental to these labs, you must rely on foundational elements such as instrument connections, semantic storage, and lab intelligence. I call these the pillars of the digital lab.
Role of Connected Instruments
Lab instrumentation is a complex system of hardware, software, and reagents. To make sense of the data from an experiment or sets of experiments, it is necessary to store the raw scientific data, analyzed data, and operational data needed to recreate that data. Examples of operational data from a liquid chromatography experiment include methods, analysis parameters, errors and warnings that occur during the run, and sample storage conditions. Connected instruments, which collect all data and metadata in an automated fashion remove human error from the experiment, thereby improving quality. They ensure data integrity by contemporaneously recording the data and circumstances in which it was created, and linking it to the audit trail. Scientific staff can be relieved of mundane tasks like asset and maintenance management as usage and configuration information are linked.
Semantic Storage Comes to the Rescue
Once data is sent from connected instruments, there must be a way to store it semantically, so that it can describe itself and relationships between the data and metadata can be utilized. Semantic data can be read by machines, enabling them to identify outliers, anomalies, ascertain data that is acceptable, and minimize intervention by scientists. Storing data semantically allows it to be searched in context to an experiment. Asking questions like, “what methods were used to separate a protein with a pI of 5.3?” are now possible. Scientists reclaim their time by eliminating the need for recording their experimental process in an electronic lab note book (ELN), and can focus on the conclusion and plan for the next experiment.
Visualization for Lab Intelligence
The final pillar is visualization of this data. As semantic data become more readily available, it becomes possible to build key performance indicators (KPI) that reflect the health of the digital lab in real-time. Data becomes operationalized, allowing improved visibility to lab operations and providing leading indicators to pivot points. For instance, lab data can now be fed into project portfolio management tools to make data-driven decisions on targets, compound classes, and technology. Semantic data also allows anyone to ask data-related questions with simple natural language processing. This eliminates the need for programmers to write code for data cleansing and query.
A breakthrough in healthcare technology, the digital lab will allow R&D professionals to focus on science and innovation, develop clinical lab solutions while maintaining quality and compliance that are so critical to the pharma, biotech, medical device, and clinical lab environments. Utilizing these three foundational building blocks allows companies to realize the lab of the future in a rational, stepwise manner. The result is the ability to build business cases for investment in new technology and ensure that ROI is well-defined and achieved.