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Pharma Tech Outlook | Monday, March 29, 2021
Process mapping is the first step toward PV transformation, which will help drive improvements by making end-to-end safety processes leaner and reducing redundancies.
FREMONT, CA: In a global, ever-changing regulatory environment, the challenges of maintaining complex Pharmacovigilance (PV) systems are growing by the day. Pharmaceutical companies must carefully manage PV operations in light of increasing public awareness, social media connectivity, and media scrutiny. On the one hand, cost-cutting pressures and the difficulty of finding qualified safety personnel, on the other, force one to reconsider traditional PV strategies. While technology solutions such as safety databases are now critical to pharmaceutical safety operations, using cutting-edge PV automation tools such as Artificial Intelligence (AI) is now critical for success.
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Inefficiencies cause most PV systems problems in the processes or people in charge of the systems. The industry needs process changes across the safety and risk management continuum to ensure optimal and proactive PV, including solutions to obtain and manage data from global sources while providing automation efficiencies. Process mapping is the first step toward PV transformation, which will help drive improvements by making end-to-end safety processes leaner and reducing redundancies.
Multiple levels of automation are required to achieve a full PV transformation. The basic automation of a process workflow entails tracking and monitoring tasks and the collection of continuous metrics. Robotic Process Automation (RPA) automates the entry, processing, and interpretation of data in a safety database, reducing manual labor. Additionally, Natural Language Processing (NLP) is used in cognitive automation to assist humans in making decisions, and it is often combined with RPA. Human interaction is necessary for cognitive computing to produce the desired result, with humans making final decisions, while AI requires very little or no human interaction.
Machine Learning (ML), a subset of AI, allows data scientists and analysts to create algorithms that can learn from and predict data. These algorithms are honed over time to spot patterns in large quantities of data. Deep learning extends this concept by processing data in layers, with the result/output of one layer serving as the input for the next.
Automation holds the promise of a slew of advantages:
Due to faster turnaround, increased quality, and accuracy, nearly 100 percent regulatory compliance is achieved.
Efficiencies of 20-50 percent are expected due to standardized inputs, automated case intake and processing, and higher productivity
See Also: Robotic Process Automation RPA Companies
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