Editor's note: This article appears in the February 2017 print issue of Laboratory Equipment.
Imagine a laboratory in the future where all devices and instruments communicate their status, activities and data with each other and with enterprise information systems. Data would be acquired without manual intervention. When a scientist approached a piece of equipment, communication would begin automatically, with no manual scanning or data entry required. The system would identify the person and interpret his or her motions to know what step is being performed in the context of the entire process. Work and results would be spontaneously monitored and recorded.
Intelligent cyber-physical environments that once seemed like a dream of science fiction may soon transform the way work is executed in the lab to help entire industries become more efficient. Adjacent technologies such as identification mechanisms, motion control, and augmented display have the potential to shift the paradigm for how work is executed in laboratories. To support this change, information systems must have a consistent way to identify, connect and communicate with people, instruments and materials.
Recent advances in the Internet of Things (IoT) and in open standards for the way data is represented across equipment and software validate this vision for the lab of the future. By enabling devices to communicate with each other and with information systems, we can create a completely connected lab. In such a lab, scientists will spend more time on science and less on documentation. Companies will derive more value from their data and eliminate inefficiencies as they attain a more complete understanding of their processes.
Escalating demands compel better processes
For companies in science- and process-driven industries, the lab of the future signifies much more than gee whiz gadgetry—it may be essential for staying competitive. Collaboration and exchange of data and methods have become increasingly complex as enterprises work on a global scale. Corporate directives to meet market demands while reducing costs compel lab managers to deliver results quickly with fewer resources. In highly regulated industries, stringent compliance obligations require more steps in the lab and necessitate proof of data integrity.
The productivity, performance and predictability of scientific work in laboratories have improved dramatically over the past decade, thanks to steady advances in technology. But, many challenges persist. Isolated information systems and paper-based lab processes are still dominant and make existing knowledge difficult to apply effectively. A lack of integration leads to manual steps in processes, which induce a high likelihood of errors. Disparate applications are difficult to configure and cumbersome to maintain. Due to the variety of instruments and methods in the lab, analysts must manage an assortment of unrelated data formats that cannot be easily shared or leveraged.
The underlying theme in these sources of inefficiency is the lack of a standard way for systems, applications and devices in the lab to share information with each other. When processes, methods and data are not standardized, collaboration becomes more challenging, knowledge acquired from previous activities is difficult to access, and compliance requires more effort. If data is locked away in various systems, scientists can’t learn from past experience or re-use the data for further analysis and knowledge-driven decision making. The solution to this perennial problem is closer than ever as standards to manage the control, transmission and description of data to and from devices and systems mature.
I have been writing and lecturing for years about the lab of the future and how a fully connected lab could resolve persistent challenges. Discussing this may be inspiring, but until the technology that enables it becomes mainstream it won’t become a reality. Now that is happening. As recently as three or four years ago if I asked a group of people how many knew about the Internet of Things, 5 or 10 percent of the crowd would raise their hands. Now I find that almost everybody has heard about it and many have initiatives in place.
IoT is a key supporting technology for making the lab of the future a reality. Embedding physical devices with electronics, software, sensors and network connectivity enables them to collect and exchange data automatically. According to CB Insights, it is estimated that the IoT industry received nearly 3.7 billion dollars in funding in 2016. Many corporate venture arms are among the top investors, including Intel Capital, GE Ventures and Cisco Investments.
One of the fundamental features of IoT is the ability for devices to collect and exchange data in a standard way. This enables devices to be interconnected in cyber-physical systems. Mainstream examples include smart grids, smart homes and smart cities. In a smart lab, there would be fewer errors because the system would identify equipment, materials and personnel. It would verify that the correct steps are taken, the correct materials are used and that the equipment is calibrated and fit for use. Inventory usage would be automatically documented and the chain of custody of materials would be tracked. No manual verification would be required.
Open standards are essential for this. If the ontologies and vocabularies don’t exist, the lab of the future won’t function. That’s why initiatives such as the Pistoia Alliance and Allotrope Foundation are important. In these communities, experts from companies and organizations spanning multiple analytical industry segments come together to share pre-competitive strategies for establishing a common data format and ontology.
Components of a connected lab
The Internet of Things and standardized data formats lay the foundation for adjacent technologies to revolutionize the way scientists work in labs and collaborate with partners. Technologies such as identification mechanisms, motion control and augmented display now exist at a reasonable price point.
To create a smart lab, there must be a standard way for information systems to automatically identify people, materials, devices and instruments. A biorhythmic bracelet, which is Bluetooth-enabled and has proximity capabilities, can identify a person by his or her unique biorhythmic pattern, determine where a person is in the lab and automatically communicate that information with connected devices and systems.
The way materials and equipment are identified continues to evolve. Quick-response (QR) codes improve readability and storage capacity over standard UPC barcodes. Radio-frequency identification (RFID) tags enable systems to automatically identify objects and their location. Near-field communication (NFC) technology enables devices to communicate with each other.
Integrating modern identification mechanisms with laboratory management systems that recognize IoT protocols and standard data formats can automate and streamline lab activities. If a scientist stands in front of a bench and begins weighing material on a balance, the system will know who is performing the operation, identify the materials involved, confirm that the person has the appropriate level of training and clearance, and record the outcome. All of that information can be securely recorded.
Automatic identification becomes more valuable when used in conjunction with motion control technology. Motion-sensing input devices gained popularity in the consumer world by enabling people to interact with video games using physical gestures. Early motion controllers utilized gyroscopes to detect gestures. This technology has continued to evolve and become more advanced. Recent models have servo-based video sensing capabilities that can identify individual users by their facial features and other physical characteristics. They can also detect minute actions, such as the movement of a fingertip.
Motion controllers can be trained to understand the activities of lab personnel. Just as with motion-controlled gaming, the system can recognize specific motions and gestures. When a technician weighs a sample, the system would recognize the action, automatically record the results, and share them with collaborators. These automated activities would allow scientists to record their actions autonomously, without adding extra thought to the process or interrupting the workflow.
Augmented reality (AR) supplements a person’s view of a physical environment with additional information supplied by computer-generated input. AR technology can be integrated into a head-mounted display, eyeglasses, safety goggles or contact lenses. When wearing an augmented display in a smart lab, a scientist can simply look at a piece of equipment or a vial of material to instantly obtain information about it.
AR technology that is integrated with automatic identification and motion control could intelligently guide scientists through steps in a process by displaying information that tells users when their actions are correct. A lab technician can tell the AR device to record video and verbal descriptions of work as it is performed. The system will automatically store all of this information in an electronic lab notebook and make it available for sharing via a secure network. Partners around the world can see what the technician sees in real-time or by viewing archived video.
Evolution of the laboratory
As the Internet of Things approaches mainstream adoption and standards for data exchange among devices mature, smart labs can become a reality. Because of widespread investments in technology for communication among instruments, devices and systems, lab managers no longer feel they need to invent the lab of the future themselves. The latest lab equipment is increasingly communication-ready and IoT-enabled. When evaluating laboratory management technology, companies should look for solutions that are designed to incorporate these smart devices.
Knowledge will be easier to leverage in tomorrow’s smart labs. Errors will be reduced, compliance will be simpler and collaboration will occur more naturally. Scientists will make better use of their time as data is acquired automatically. Accuracy and efficiency will be improved as laboratory activities and automated processes are monitored by intelligent information systems that integrate devices and equipment. The cyber-physical systems that will facilitate tomorrow’s smart cities and smart homes create a foundation for smart labs that can help science- and process-driven industries advance.