AI Integrated Workflow for Real World Evidence Analysis in Healthcare

Real-World Evidence Analysis for Post-Market Surveillance

1. Data Collection

1.1 Identify Data Sources

• Electronic Health Records (EHR)
• Claims and Billing Data
• Patient Registries
• Social Media and Online Patient Communities

1.2 Implement Data Integration Tools

• AI-driven data aggregation platforms (e.g., IBM Watson Health)
• Natural Language Processing (NLP) tools for unstructured data (e.g., Google Cloud Natural Language)

2. Data Processing and Cleaning

2.1 Data Normalization

• Standardize data formats across sources
• Utilize AI algorithms to detect and correct anomalies

2.2 Data Enrichment

• Integrate demographic and clinical data using AI tools (e.g., Tableau, Microsoft Power BI)
• Use machine learning models to enhance data quality

3. Evidence Generation

3.1 Descriptive Analysis

• Employ AI analytics platforms (e.g., SAS, R) to perform statistical analysis
• Visualize findings with AI-enhanced visualization tools (e.g., QlikView)

3.2 Predictive Analysis

• Utilize machine learning algorithms to predict outcomes (e.g., TensorFlow)
• Implement risk assessment tools to identify potential safety signals

4. Reporting and Communication

4.1 Generate Reports

• Automate report generation using AI reporting tools (e.g., Domo, Sisense)
• Ensure compliance with regulatory requirements in report formatting

4.2 Stakeholder Communication

• Utilize AI chatbots for real-time communication with stakeholders
• Schedule presentations and briefings using AI scheduling tools (e.g., Calendly)

5. Continuous Monitoring and Feedback

5.1 Implement Surveillance Systems

• Deploy AI monitoring systems to track adverse events in real-time
• Utilize tools such as Oracle’s Argus Safety for ongoing surveillance

5.2 Feedback Loop

• Collect feedback from healthcare professionals and patients using AI-driven survey tools (e.g., SurveyMonkey)
• Analyze feedback to refine data collection and analysis processes