A validation study of intelligent automation for pharmacovigilance
Pharmacovigilance is the discipline of keeping an eye on the effects of pharmaceuticals to spot and assess potential side effects and provide necessary and prompt risk reduction measures.
Automating regular tasks and balancing resource consumption across safety risk monitoring and other pharmacovigilance operations are both possible with intelligent automation technology. New technologies like artificial intelligence (AI) hold enormous promise for improving pharmacovigilance because of their capacity to learn from data inputs.
However, existing validation criteria should be supplemented to test intelligent automation systems. While the fundamental requirements for validation generally stay the same, new tasks designed for intelligent automation are required to provide proof that the system is fit for its intended use.
The validation of AI-based systems
There are three types of intelligent automation systems, ranging from rule-based to dynamic AI-based, and each type requires a different validation strategy.
By building on current best practices for automated production, a risk-based strategy for artificially intelligent static systems is presented. Assistive technology solutions can be developed, applied, validated, and maintained by pharmacovigilance experts using framework.
- A successful pharmacovigilance expert must bridge the gap between business operations and technological innovation to prepare for inspections and comply with international regulatory agencies.
- A significant number of resources is devoted by pharmacovigilance departments to processing adverse event (AE) cases, and according to benchmark data, the number of AE cases is rising.
- Automating routine tasks and balancing resource use across safety risk management and other pharmacovigilance activities are both possible with intelligent automation technologies.
- Intelligent automation can improve the accuracy and reliability of case processing and evaluation, enabling a prompt evaluation of safety signals.
- The use of such technological solutions to assist with AE cases must be validated in accordance with regulations.
- Automating regular tasks and balancing resource consumption across safety risk monitoring and other pharmacovigilance operations are both possible with intelligent automation technology.
- Intelligent automation can enhance the accuracy and reliability of case processing and evaluation, enabling a prompt evaluation of safety warnings. When such technological solutions are used to assist in the handling of AE cases, pharmaceutical firms must validate this software in accordance with laws.
- Computerized system validation (CSV) is the procedure used to confirm and record that the requirements for a computerized system are continuously met from the time of design until its decommissioning and/or transfer to a new system.
- The method to be used for validation should be centred on a risk assessment that considers the system’s intended usage, the possibility that it could have an impact on human subject protection, and the dependability of trial outcomes.
- For many years pharmacovigilance has made extensive use of algorithms, rule-based software, computerized workflows, and pattern matching. Robotic process automation has been used by several businesses and suppliers more recently to help manage individual case safety reports.
- Machine learning (ML) and natural language processing (NLP) approaches are two recent fields of research based on artificial intelligence (AI) technologies that are currently being used to support pharmacovigilance procedures.
- The potential of this kind of technology to learn from data inputs offers enormous promise, but to validate intelligent automation systems, already-existing validation frameworks may need to be strengthened.
- For the system to meet its intended uses, it is necessary to perform more software development activities specifically geared toward intelligent automation.
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