Drug Safety Case Processing: What role can AI play?
A significant challenge in drug safety is the management of information. AI can help to manage a vast amount of data in an efficient and scalable manner.
The pharmaceutical industry has been exploring and developing AI for drug safety case processing for many years. The main driving factor is the need to process many case reports that can run into millions or even billions.
AI in Drug Safety Case Processing is the answer to dealing with the problems faced during manual regulatory compliance processing by automating regulatory submissions, monitoring and improving quality reporting, and reducing reporting timeframes.
Ways AI is Revolutionizing Drug Safety Case Processing
The use of AI in drug safety case processing is becoming more widespread. These AI systems help with many aspects that would otherwise require significant manual labor. They can help identify which drug has the adverse effect, the patient’s adverse impact, and where it occurred. An AI can also predict potential side effects and prevent them before they happen.
Drug safety case processing is no longer just about identifying how many people were affected by one particular drug or incident; now, it’s about the much more extensive scope of medical research and development.
Healthcare professionals are facing new challenges due to the increase in the number of people with chronic conditions, record-breaking drug prices, and skyrocketing medical bills. AI can help doctors by taking on some of their most tedious tasks.
AI helps companies in clinical trials by processing and analyzing drug safety case data. This helps in understanding clinical trial data for better research and development.
The use of AI has many benefits for clinical trials, such as providing real-time feedback to the researcher and helping reduce human bias in decision-making. It can even warn healthcare professionals based on specific symptoms or changes in the patient’s condition.
How Using AI for Drug Safety Cases Can Reduce Trial Costs And Improve Clinical Data
Clinical trial costs have been increasing in the past decade. Finding a treatment for rare diseases is difficult because not enough patients are willing to participate in the trial. Moreover, time and money constraints lead to incomplete or inaccurate data, compromising data quality.
Therefore, it is necessary to use AI in clinical trials to improve trial costs and data quality in drug safety cases. AI has helped scientists better understand drug safety cases by linking heterogeneous datasets together, modeling complex biological processes, and predicting adverse events before they happen. AI will decrease the time required for drug development while maintaining high-quality clinical data.
AI can help expedite drug safety case processing by allowing faster and more accurate data extraction from structured and unstructured sources, cutting down time in the Data entry process. This allows for a more thorough, accurate, and faster processing of adverse event reports, which will also help companies enhance their reporting of adverse events to health authorities.
Datafoundry helps with AI solution for Drug Safety Case Processing
DF mSafety AI is a modern, cloud-based SaaS that uses the power of AI/ML to deliver efficiencies and a great user experience in Safety Case Management and Signal Detection for medicinal and cosmetic products and medical devices.
Built by industry experts with decades of experience joining hands with a team of world-class data scientists and ML pioneers, mSafety is the solution to address your key challenges in Safety and Surveillance Management – from case intake and triage through medical review and regulatory submission.
The key components of DF mSafety AI below are also available as stand-alone products that can be integrated with customers’ existing Safety Databases.
- • AI-powered Case Intake from multiple sources and Case Triage
- • Signal Detection and Risk Management
- • Literature Monitoring for Safety Vigilance
Talk to our experts to know how we can help you in automating Drug safety management.
References:
- ftc.gov. https://www.ftc.gov/system/files/documents/public_comments/2015/09/00061-97080.pdf (accessed October 1, 2021).
- hera.ugr.es. https://hera.ugr.es/tesisugr/26134755.pdf (accessed October 1, 2021).