AI Pharmacovigilance Workflow for Adverse Event Data Processing
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Solution Overview
Problem
Pharmacovigilance procedures are complex, requiring significant manpower and resources, and existing methods struggle with the manual analysis and processing of vast amounts of drug adverse effect data, lacking efficiency and accuracy.
Innovation Solution
An intelligent pharmacovigilance platform utilizing artificial intelligence algorithms for data processing, including OCR and NLP, to convert unstructured data into structured form, and perform tasks like case entry, quality control, and medical review, with authority-based access and visualization tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis and processing of adverse effect data is used, then human judgment and flexibility are maintained, but productivity and consistency are reduced
Solution Approach 1:
The system enables self-service through automated AI processing where the platform independently performs data collection, analysis, and reporting without requiring continuous manual intervention. The AI models automatically process adverse effect data, generate safety signals, and produce regulatory reports, allowing the system to serve itself while maintaining operational efficiency and consistency.
Solution Approach 2:
The patent replaces the mechanical manual analysis system with an AI-based automated system. Machine learning models and natural language processing algorithms substitute for human analysts in processing adverse effect reports, extracting structured data from unstructured text, and generating safety signals, thereby significantly improving processing speed and consistency.
2Measurement precision
If multiple AI models are selected and applied, then processing accuracy and task specialization are improved, but device complexity increases
Solution Approach 1:
The system segments the AI processing function into multiple specialized models, each designed for specific tasks such as data extraction, safety signal detection, causality assessment, and report generation. This segmentation allows each model to be optimized for its specific function, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent implements a universal AI platform that can select and apply different specialized models based on the specific processing task required. The system provides a unified interface where various AI models can be dynamically chosen and applied, allowing one system to perform multiple functions with high accuracy without requiring separate dedicated systems for each task.
3Reliability
If authority-based access control is implemented, then data security and information management are improved, but ease of operation is reduced
Solution Approach 1:
The system introduces an intermediary authentication and authorization layer that mediates between users and the AI processing system. This intermediary component handles identity verification, authority validation, and access control automatically, allowing users to interact with the system conveniently while maintaining strong security through automated verification mechanisms.
Solution Approach 2:
The patent implements dynamic parameter changes in access control based on user authority levels. The system automatically adjusts data access permissions, processing capabilities, and information visibility according to the authenticated user's authority parameters, providing both security and ease of operation by eliminating the need for manual permission configurations while maintaining appropriate access control.
Data Source
AI summary
Disclosed herein are a method and apparatus for providing a pharmacovigilance (PV) platform, wherein a method for operating a server may include: receiving input data from a user device; generating at least one command set from the input data by using a first artificial intelligence model that is selected to process the input data; determining whether or not a user that provides the input data has an authority to execute the at least one command set; generating a result of a task, when the user has the authority, by using a second artificial intelligence model that is selected to perform the task according to the at least one command set; generating output data that displays the result of the task by using a visualization module that is selected to visualize the result of the task; and transmitting the output data to the user device.