Machine learning in pharmacovigilance: predicting adverse reaction faster to make drugs safer
Machine Learning jobs in pharmacovigilance are evolving prominently, and it is available globally. This is because of its ability to prevent adverse drug reactions with artificial intelligence.
Technology providers, health authorities and early pharmaceutical investors have begun piloting different technologies like machine learning and cognitive computing to achieve AI to help address the challenges associated with large data volumes. These experiences provide cautionary insights considered as cognitive and AI computing in production systems. Similar to other medical fields, pharmacovigilance is currently facing increasing volumes of data that need support from smart technologies like machine learning and cognitive computing. These technologies follow various perspectives, from regulatory authorities and cognitive IT to the pharmaceutical industry. Therefore, machine learning in pharmacovigilance is progressing and can potentially boost risk-benefit estimation through prognostic aptitudes.
Benefits of artificial intelligence in pharmacovigilance
In health care, AI is used to detect and evaluate medicinal products, and is applicable in machine learning and deep learning. AI is used mostly in processing, extrapolating and reading large, unstructured datasets: medical industries are still early in implementing and integrating AI-driven solutions. AI tools in pharmacovigilance have reduced risks in medical processes, freed up pharmacovigilance experts from routine tasks and delivered high cost and time savings. These tools help manage increasing pharmacovigilance workloads and make the best out of human assets on the available pharmacovigilance (PV) teams. When used together with natural language processing, ML will help to unearth or surface data quickly. Therefore, pharmacovigilance leaders will spend less time identifying AE patterns, including the frequency and severity of AEs. Available ML and AI tools help pharmacovigilance processes translate large datasets from one language to another. This speeds up literature searches and social media scanning for AES to transform scanned documents and automate case follow-ups.
Ways AI automation can speed up your PV processes
Automation plays an important role in different processes in pharmacovigilance. Based on a market survey, PV is expected to experience an increase of $8 billion in the next four years. With the increasing demand for better medical services and drugs safety, PV processes need to be smooth and more effective. Therefore, pharmaceutical companies are looking forward to automating PV to reduce costs, unlock growth opportunities, speed up handling adverse events, and improve patient safety. Now, companies are trying to move to proactive and preventive adverse events from reactive risk management while automating tedious processes and tasks. According to US Food and Drug Administration, adverse event reports have grown by 84% between 2014 and 2020. Therefore, global spending on pharmacovigilance is projected to grow at 11.5% from 2021 to 2028.
Automating processing data
Pharmaceutical companies work by processing data from different locations. A large pharma organization or company can only process up to 700000 cases yearly using a manual process. Therefore, about 50% of other cases will be processed in the coming year. This cannot be defined as being efficient considering the high demand to take more cases and reduce the cost associated. Data automation offers an easy solution to this challenge. Integrating AI into your data process will take advantage of cloud-based data management to link various data sources and make predictions of adverse reaction cases. Cloud-based data management offers no human error; hence you are sure the right data is included even without verification. Allowing machines to take care of processes will give chance to human intelligence to be used where human involvement is required, making data processing faster.
Discover how training data can make or break your AI projects, and how to implement the Data Centric AI philosophy in your ML projects.
Drug department in different companies is trying to reform their processes to shorten the time taken for drugs to reach the market. Unfortunately, complying with the set regulatory standards is hard yet important. Minimizing risk while maximizing profits and maintaining drug safety remains the top priority. It is very hard to achieve this with human involvement hence the need for automation.
Part of fulfilling your duty as a pharmaceutical company involves adhering to guidelines and sharing necessary information with the regulatory bodies. Therefore, there will be a need to compile and arrange this pharmacovigilance reporting process clearly and concisely. When done manually, it will take much of your team’s time, while automation will remove time wastage by eliminating the human element from most parts of the process. AI makes document automation simple; hence reports can be collected from scattered sources, compiled with all relevant information and sent to the authorities concerned. This process can be scheduled to make it even more efficient: without prompting, documents can be sent on time. Conclusion In the near future, new technologies that include artificial intelligence will be more focused on matters related to improving an individual’s life in contrast to resource-intensive manuals. This automation is achieved through different programs like risk management and benefit-risk assessment. Patient data will be analyzed safely, and trends will be detected in large quantities in an AES. This will enhance accuracy and fast processing.