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Pharma Tech Outlook | Friday, February 25, 2022
It's time for the pharmaceutical business to adapt swiftly, uncover AI and machine learning use cases quickly, and scale them to move forward on an evolutionary road map.
Fremont, CA: With the pandemic affecting every area of our lives, players in the biopharmaceutical industry and pharmaceutical corporations quickly realized that the moment had come to leverage technology's potential within their fields. It was time to embrace automation and make significant progress in increasing operational capacity. Pharma professionals quickly grasped the need and scope to harness artificial intelligence (AI) and machine learning (ML) at scale, as they were burdened by legacy clinical trial processes in pharmacovigilance and ever-increasing quantities of case processing year-on-year.
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With the pandemic affecting every area of our lives, players in the biopharmaceutical industry and pharmaceutical corporations quickly realized that the moment had come to leverage technology's potential within their fields. It was time to embrace automation and make significant progress in increasing operational capacity. Pharma professionals quickly grasped the need and scope to harness artificial intelligence (AI) and machine learning (ML) at scale, as they were burdened by legacy clinical trial processes in pharmacovigilance and ever-increasing quantities of case processing year-on-year.
Applying AI and Ml in Pharmacovigilance
Rapid delivery optimization and cross-industry collaboration
New technologies offer a collaborative environment, allowing clinical trials and medication testing to be optimized. To this aim, AI and machine learning can assist break down silos and automating 50-60 percent of easy post-marketing situations by using lessons learned from other biopharma research when the chance arises. Collaborative tendencies aid in the standardization of data and processes, as well as the optimization of signal detection techniques through the application of predictive analytics.
Support clinical trial effectiveness
Pharmacovigilance has a lot of potential for automating patient safety data and increasing the efficiency of clinical trials. Signal detection, large-scale data analysis, risk contextualization and tracking, and machine learning techniques can all be used to attain this goal. Following successful training of adverse event database information, pharmacovigilance professionals can use the combined accuracy measures to judge different vendor algorithms. Automation proves to be a cost-efficient and effective technique since it eliminates the need for manual data entry and source document annotation, both of which are time-consuming tasks.
Measure the effectiveness of core pharmaceutical systems based on consumption and experience
Patients and trial volunteers frequently share their experiences with medicines during case processing. This can often lead to members in a different group discussing similar or opposing reactions. An early warning system for hazardous medication responses could be a unified monitoring and evaluation system that tracks consumption trends. Social media also allows for the monitoring of these medication effects outside of traditional clinical studies. Several drug users frequently post their user experiences on social media in trusted networks for ailments with a social stigma attached to them. These incidents are frequently underreported in individual case safety information reports (ICSR). In such cases, data mining can significantly improve how traditional pharma systems monitor case processing.
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