Driving Operational Innovation In Life Sciences With RPA
Within the industry of life sciences, there is a continuous call to develop new medicines faster and lower cost. This demand to release new pharmaceuticals to the market while lowering the cost and time spent developing them has led to a rise in the use of technology in the life sciences field. Robotic Process Automation (RPA) enters here. Process improvements achieved in conjunction with the increased use of automation enhance these operations’ timeline and accuracy. Read on to know more about how RPA can transform the operational landscape of the life sciences industry.
Life sciences companies need to reinvent their operational landscape to an automation approach that drives value while optimizing costs. In short, the new operational transformation technique is of utmost necessity, and embracing innovative technologies is the need of the hour. RPA is a technology that can help enterprises navigate challenges and emerge prepared to optimize opportunities. RPA is poised to play a critical role in the life sciences industry.
The randomized clinical trial lifecycle is a challenging data environment that requires meticulous study and is further compounded by different technical, clinical, business, and compliance needs. It involves disparate systems, like accounting, spreadsheets, and clinical trial management systems. With manual steps and dependency on employee abilities, the challenges are multiple. They include errors, ignorance of best practices, lack of training, and cultural resistance. With so much riding on RCTs, leveraging RPA in conjunction with ML can make a difference to life sciences enterprises. Improved efficiency and success rate of RCTs are the positive effects of robotic process automation.
RPA lso has a crucial role in operational functions and maintains the sanctity of data by limiting third-party interferences. Aggregate spends reporting, salesforce effectiveness, chargeback and rebate processing, marketing fulfillment, master data management, and accounting analysis are some of the functions that can benefit immensely from the use of RPA. It can also improve the quality of the data received from third parties.
How Can Machine Vision Systems Boost RPA In Life Sciences?:
Robotics plays an ever-increasing role in life sciences applications. Robots are not only taking on monotonous tasks and streamlining processes in laboratory settings but are also leveraging advances in vision technology to support medical research.
For instance, in the cloning of bacteria, vision technology allows the robot to identify and shift the chosen bacteria into a petri dish. It provides a means of identifying the part of the bacteria colony that must be removed for additional processing and that which can be discarded.
Recognizing the number of specimens being tested on a day-to-day basis and the data collection demands on technicians, robotics provides a more accurate and efficient solution. Robotic systems possess the ability to pipette a 96 well plate in 20 seconds without even making a mistake or the need for a break. This leaves a lot of room for technicians to add value to more complex research tasks.
Robotic automation prevalent in the clinical labs utilizes machine vision to assist in managing medications and biological specimens. Robots that have the vision capabilities can handle glass medical vials and specimen samples that have colored stoppers. Robots can evaluate if biological samples are present and if the needles containing liquid are unbroken.
The on-going innovations in imaging sensor technology are also widening the possibilities for robotic machine vision applications in the life sciences. New image sensor technologies include:
• Ultra-high resolution
• Higher sensitivities for ultra-low-light applications
• Faster frame rates to capture more detail
• Specialized RGB-NIR (near-infrared) color filter array that allows for the separation and discrete analysis of the visible and NIR wavelengths
The vision capabilities of robotic systems will only expand, which means more opportunities for robotic applications.
See Also:
What Are The Challenges Faced By The Life Sciences Industry?
