AI Driven Clinical Trial Patient Matching Workflow Explained

Clinical Trial Patient Matching Workflow

1. Patient Recruitment

1.1 Data Collection

Gather patient data from various sources including electronic health records (EHRs), genomic databases, and patient registries.

1.2 AI Tools for Data Aggregation

Utilize AI-driven tools such as IBM Watson Health and Flatiron Health to aggregate and analyze patient data efficiently.

2. Patient Profiling

2.1 Genomic Analysis

Conduct genomic sequencing and analysis to identify relevant biomarkers.

2.2 AI for Genomic Interpretation

Implement AI platforms like Deep Genomics and GRAIL to interpret genomic data and identify patient eligibility based on genetic markers.

3. Matching Algorithm Development

3.1 Criteria Definition

Define inclusion and exclusion criteria for clinical trials based on disease type, genetic profiles, and other health metrics.

3.2 AI Algorithm Implementation

Develop machine learning algorithms using tools such as TensorFlow or PyTorch to match patients with appropriate clinical trials based on the defined criteria.

4. Patient Engagement

4.1 Communication Strategy

Design a communication strategy to inform matched patients about trial opportunities.

4.2 AI-Driven Engagement Tools

Utilize AI chatbots like HealthTap to answer patient queries and provide trial information in real-time.

5. Enrollment Process

5.1 Pre-screening

Conduct pre-screening interviews to confirm patient eligibility and interest.

5.2 AI for Document Verification

Implement AI tools such as DocuSign to streamline the document verification and consent process.

6. Continuous Monitoring

6.1 Patient Tracking

Monitor patient progress and adherence to the clinical trial protocols.

6.2 AI for Data Analysis

Use AI analytics platforms like Medidata to analyze ongoing trial data and adapt protocols as necessary.

7. Outcome Reporting

7.1 Data Compilation

Compile data on patient outcomes and trial efficacy.

7.2 AI for Reporting Insights

Employ AI-driven reporting tools such as Tableau to visualize outcomes and generate insights for stakeholders.

8. Feedback Loop

8.1 Patient Feedback Collection

Collect feedback from participants regarding their experience and outcomes.

8.2 AI for Sentiment Analysis

Utilize AI tools like MonkeyLearn for sentiment analysis on feedback to improve future trials and patient matching processes.