AI Driven Pharmacovigilance Workflow for Adverse Event Detection

AI-Powered Pharmacovigilance and Adverse Event Detection

1. Data Collection

1.1 Sources of Data

• Clinical trial data
• Electronic health records (EHR)
• Patient-reported outcomes
• Social media and online forums

1.2 Tools for Data Collection

• IBM Watson Health
• Oracle’s Health Sciences Cloud

2. Data Preprocessing

2.1 Data Cleaning

• Remove duplicates
• Standardize formats

2.2 Data Integration

• Combine data from multiple sources
• Use ETL (Extract, Transform, Load) processes

3. Adverse Event Detection

3.1 AI Model Development

• Train machine learning models on historical adverse event data
• Utilize Natural Language Processing (NLP) for unstructured data analysis

3.2 Tools for Adverse Event Detection

• Google Cloud AutoML
• Microsoft Azure Machine Learning

4. Signal Detection

4.1 Statistical Analysis

• Implement Bayesian models for risk assessment
• Use disproportionality analysis to identify signals

4.2 Tools for Signal Detection

• Pharmacovigilance software like VigiBase
• Data mining tools such as SAS and R

5. Reporting and Documentation

5.1 Automated Reporting

• Generate reports based on detected signals
• Utilize templates for regulatory submissions

5.2 Tools for Reporting

• Oracle’s Argus Safety
• ArisGlobal’s LifeSphere

6. Continuous Monitoring

6.1 Real-time Surveillance

• Implement AI algorithms for ongoing risk evaluation
• Utilize dashboards for real-time monitoring

6.2 Tools for Continuous Monitoring

• Tableau for data visualization
• QlikView for interactive dashboards

7. Feedback Loop

7.1 Data Feedback Mechanism

• Incorporate feedback from healthcare professionals and patients
• Adjust AI models based on new data and findings

7.2 Tools for Feedback Collection

• SurveyMonkey for patient feedback
• Qualtrics for healthcare professional surveys