AI Integrated Workflow for Pharmacovigilance and Adverse Event Detection

AI-Powered Pharmacovigilance and Adverse Event Detection

1. Data Collection

1.1 Sources of Data

• Clinical trial data
• Post-marketing surveillance reports
• Electronic health records (EHR)
• Patient registries
• Social media and online forums

1.2 Tools for Data Collection

• IBM Watson for Clinical Trial Matching
• Oracle’s Siebel Pharma for EHR integration

2. Data Preprocessing

2.1 Data Cleaning

• Removing duplicates
• Standardizing data formats

2.2 Data Enrichment

• Integrating external databases (e.g., FDA, WHO)
• Utilizing natural language processing (NLP) for unstructured data

3. Adverse Event Detection

3.1 Implementation of AI Algorithms

• Machine Learning Models for pattern recognition
• Deep Learning for predictive analytics

3.2 Tools for Adverse Event Detection

• Google Cloud AutoML for predictive modeling
• Amazon Comprehend Medical for NLP tasks

4. Signal Detection

4.1 Statistical Analysis

• Bayesian data mining techniques
• Proportional reporting ratios (PRR)

4.2 AI Tools for Signal Detection

• SAP Predictive Analytics for statistical modeling
• DataRobot for automated machine learning

5. Risk Assessment

5.1 Risk Evaluation

• Quantitative risk assessment using AI models
• Qualitative assessments via expert systems

5.2 Tools for Risk Assessment

• PharmaAnalytics for risk evaluation metrics
• RiskWatch for comprehensive risk management

6. Reporting and Compliance

6.1 Regulatory Reporting

• Automated report generation for regulatory bodies
• Ensuring compliance with ICH and FDA guidelines

6.2 Tools for Reporting

• Veeva Vault for regulatory submissions
• Medidata for clinical data management

7. Continuous Monitoring and Improvement

7.1 Feedback Loops

• Implementing continuous data feedback mechanisms
• Regular updates to AI models based on new data

7.2 Tools for Continuous Monitoring

• Tableau for data visualization and monitoring
• Qlik Sense for real-time analytics