Proper Process Prevents Poor Lab Performance

Jan. 29, 2015
Emerging technologies plus standardized process can deliver competitive agility to lab operations

Steady advances in technology have vastly improved the productivity, performance and predictability of scientific work in laboratories for life science organizations. But many challenges remain. Fragmented manual processes, isolated information systems, inconsistencies in methodologies and reliance on paper records can lead to inefficiencies and increased compliance risk.

To stay competitive, today’s life science firms need to implement systems for standardizing the processes that are followed during development, manufacturing and quality assurance/quality control (QA/QC). Standard processes are critical for establishing efficient lab environments, fostering communication among teams, and facilitating externalization for various parts of the development and manufacturing process.

Creating standardized processes, methodologies and data sets can help life science organizations overcome many of these perennial problems. Standardization can also pave the way for critical paradigm shifts by supporting emerging adjacent technologies such as augmented display, motion control and the automatic identification of lab materials, equipment and lab personnel.

In the future, these adjacent technologies will enable new ways of working in and around the laboratory. Companies will derive more value from their data and eliminate inefficiencies as they attain a more complete understanding of their processes.

Standardization of processes and data helps companies move forward by providing a consistent way for people in separate domains to communicate with each other — including R&D, QA/QC and activities with external partners. It also helps professionals across the enterprise understand prior activities by preserving the context associated with the data via process metadata. A better understanding of data yields knowledge that can be shared internally and with critical partners.

Such best practices are not years away. They are happening today in isolated pockets. They need to be applied consistently across the product lifecycle continuum, from initial research to commercialized products. Doing so will create a foundational platform supporting new adjacent technologies that have the potential to shift the paradigm for the way laboratory work is performed.

Imagine if everything and everyone in the lab had a unique identifier that is automatically read by the information system. 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 know what step is being performed in the context of the entire process. Work and results would be automatically monitored and recorded.

There would be fewer errors because the system would identify equipment, materials and personnel and verify that the correct steps are taken, the correct materials are used and that the equipment is calibrated and accurate. Inventory usage would be automatically documented, and the chain of custody of materials would be tracked. No manual verification would be required.

Enabling technologies such as augmented display, motion control and automatic identification all come into play here. But in order for these technologies to improve lab efficiency, there must be a standardized way of understanding people, materials, processes and equipment.

In the lab of the future, the foundational platform will work in harmony with new physical devices that are advancing very rapidly. The remainder of this article will discuss some of the most compelling examples of these adjacent technologies and explain how they could be applied to the life science industry. These technologies all exist today and are not expensive.

Motion Control: 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.
As scientists and lab technicians follow processes and run experiments, they typically need to document their activities and record their observations. These activities require them to interact with physical devices such as pen and paper or a computer. The interaction takes time, interrupts workflow and introduces the potential for contamination. A worker may be wearing gloves or may be in a clean room or hood, making interaction with physical devices more difficult.

Using motion-sensing technology, a user can control the cursor on a computer screen by simply gesturing in the air without touching a mouse or keyboard. For example, in the course of weighing a sample, a technician could make a hand motion that tells the system to record the weight. It could be done intuitively, without adding extra thought to the process or breaking the workflow. Motion controllers can be trained to understand what lab personnel are doing. Just as with motion-controlled gaming, the system can recognize specific motions and gestures.

These techniques would be especially valuable when work must be performed in a ventilation hood. Usually the only things inside that hood are the sample, the person’s hands and the equipment. The scientist doesn’t want to remove his or her hands from the environment to write something down or to input data into a computer. If the steps can be recorded automatically via simple movements, the work can continue without interruption.

Similarly, work in a clean room typically requires people to interact with a computer that is on the other side of a glass wall. They can’t touch it. The cost of having the computer outside the clean room is much less than having it inside. Using motion control technology, lab professionals can interact with these computers simply by gesturing.

Identification Mechanisms: To enable the lab of the future, advanced identification methods are required beyond traditional barcodes, transcription technology or login technology. Lab professionals should not have to overtly identify themselves, their equipment or the materials they work with. This needs to happen automatically.

Biorhythmic bracelets show great potential as a way to automatically identify people in a lab. A biorhythmic bracelet that is Bluetooth-enabled and has proximity capability 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 the system.

For example, if someone stands in front of a bench and begins weighing material on a balance, the system will know who is performing the operation and will confirm that the person has the appropriate level of training and clearance. All of that information will feed into the overall process and be automatically recorded.

The way materials and equipment are identified continues to evolve. The earliest methods involved writing information on a vial or container, and many labs still require that. Barcode technologies have advanced the process of identifying materials. Lab personnel can physically scan bar codes and then access a computer to get information. But these methods require manual interaction.

New technologies can refine and automate identification methods. For example, 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. Using these identification technologies in conjunction with a motion control system would be especially powerful. When a lab technician puts material on a scale, the system will know precisely what is being done and who is performing the work. Most importantly, the system will confirm that the activity corresponds with the appropriate step in the process and will record the results.

When all people, materials and equipment in the lab are uniquely identified, their virtual representations can be connected on a network. The system will then know the location and status of everything in the lab.

Augmented Display: 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, goggles or contacts. When wearing an augmented display, a scientist can just look at a piece of equipment or a vial of material to instantly obtain information about it. Other users of the system could simultaneously see what the scientist sees.

Imagine that a chemist picks up the wrong material and is about to mix it. When the person looks at the material, the augmented display device reads a barcode, RFID tag, or even a handwritten label and informs the user that the material is incorrect for this step in the process. A red warning light might flash on the display. A warning light could also appear if someone attempts to use a piece of equipment that is not properly calibrated.

Lab personnel would get a green light when they pick up the right material, the material is not expired and the correct step in the process is undertaken. A user can follow the green light throughout the process without needing to stop to look up information about materials and equipment. The system knows what the appropriate steps are, what the appropriate materials are, that the equipment is in working order and that the person is authorized. The process circumvents errors and maintains rigorous quality-control procedures.

A user 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, which other personnel can access. They can collaborate in real-time, regardless of location, as partners see what the user in the lab sees via the augmented display.

Predictive Modeling: New ways of working in the laboratory will deliver rich empirical content that builds knowledge. That knowledge can be used to develop predictive models during development that are passed along to manufacturing and other domains. Companies can use these predictive models to streamline workflows, eliminate unnecessary steps, perform more precise tests, reduce waste, improve product safety and expedite time to market for new products. Predictive models will also enable lab professionals to adjust parameters so they can better predict outcomes such as highest yield, least expensive materials or fewest pollutants.

Establishing a baseline foundation of standardized processes and understanding data as knowledge will enable life science companies to apply emerging adjacent technologies to shift the paradigm for how work is executed in laboratories. To support this change, information systems must have a standardized way to identify, connect and communicate with people, instruments and materials. These capabilities exist today. As companies invest in technology, they should look for solutions that support these labs of the future.

Gene Tetreault is senior director of products and marketing for the Enterprise Laboratory Management Portfolio at Dassault Systèmes’ BIOVIA brand.

About the Author

Gene Tetreault | BIOVIA