AI Driven Workflow for Clinical Trial Participant Matching

AI-Driven Clinical Trial Participant Matching

1. Define Clinical Trial Requirements

1.1 Identify Inclusion and Exclusion Criteria

Establish specific medical, demographic, and geographic factors that define the ideal participant profile.

1.2 Set Recruitment Goals

Determine the target number of participants needed for the trial and the timeline for recruitment.

2. Data Collection and Integration

2.1 Gather Patient Data

Utilize Electronic Health Records (EHR) systems to collect data on potential participants.

• Example Tools: Epic, Cerner

2.2 Integrate External Data Sources

Incorporate data from registries, wearable devices, and patient-reported outcomes.

• Example Tools: RedCap, Medidata

3. AI Model Development

3.1 Data Preprocessing

Clean and standardize data to ensure consistency and accuracy for model training.

3.2 Feature Engineering

Identify key features that will enhance the model’s ability to predict suitable participants.

3.3 Model Selection

Choose appropriate machine learning algorithms for participant matching.

• Example Tools: TensorFlow, Scikit-learn

4. AI-Driven Matching Process

4.1 Implement Predictive Analytics

Utilize AI algorithms to analyze participant data against trial criteria.

4.2 Rank Potential Participants

Generate a ranked list of candidates based on their fit for the clinical trial.

5. Review and Validation

5.1 Manual Review of Matches

Clinical researchers review AI-generated matches to ensure appropriateness and compliance with trial criteria.

5.2 Feedback Loop

Incorporate feedback from clinical staff to refine AI algorithms and improve future matching accuracy.

6. Participant Outreach

6.1 Contact Potential Participants

Utilize automated outreach tools to engage with matched participants.

• Example Tools: Salesforce Health Cloud, Mailchimp

6.2 Schedule Screening Appointments

Facilitate appointment scheduling through integrated systems to streamline the process.

7. Continuous Monitoring and Improvement

7.1 Analyze Recruitment Metrics

Monitor recruitment effectiveness and participant engagement throughout the trial.

7.2 Update AI Models

Continuously refine AI models based on recruitment outcomes and participant feedback.