CLINICAL DATA MANAGEMENT OPERATIONS IN AI (CDAI)

Clinical Data Management Operations in AI (CDAI)

Introduction – Clinical Data Management Operations in AI

Qtech-Sol specializes in Clinical Science Training and offers a comprehensive program for aspiring and working Clinical Data Management (CDM) professionals operating in modern, AI-enabled research environments. The CDAI program blends core CDM competencies with applied AI skills used throughout the data lifecycle across pharmaceutical, biotech, and CRO settings.

The curriculum highlights the expanded role of CDMs in AI-powered trials—covering smart eCRF design, automated edit-check generation, anomaly detection, intelligent query management, AI-assisted coding, centralized data review, and DBL readiness. This hybrid program bridges traditional CDM responsibilities with practical, real-world AI applications.

This course delivers:

1. 10 Core CDM Lessons (Traditional Clinical Data Management)
2. 10 AI-Integrated Exercises (Applied Learning for AI Environments)

Each lesson includes:

1. Narrated Presentations
2. Course Reading Material
3. Practice Quizzes
4. Assessment Tests
5. Short Questions
6. Role-Based Tasks with AI Applications

SIP – Self-Paced Online with Support

CDAI-SIP Delivery – 10 Weeks / 210 Hours (Self-Paced)

To enroll in CDAI, candidates are encouraged to hold an associate or bachelor’s degree in a life-sciences or related healthcare field. Suitable majors include Biology, Biochemistry, Biotechnology, Pharmacy, Nursing, Medicine, Public Health, Clinical Research, Biomedical Engineering, Pharmacology, Toxicology, Chemistry, and Healthcare Administration.

1. Protocol & Study Build: Convert protocol requirements into structured eCRFs, edit-check specs, and a Data Validation Plan (DVP)—using AI assistants for draft generation and gap checks.
2. EDC & UAT: Configure and test EDC (e.g., mid-study updates, version control) with AI-suggested test cases and automated defect summaries.
3. External/Vendor Data: Manage lab, ECG, imaging, and ePRO data with AI-assisted mapping, spec checks, and anomaly detection.
4. Discrepancy & Query Management: Use NLP-driven tools to triage queries, flag duplicates, and auto-suggest resolutions while tracking cycle time KPIs.
5. Medical Coding: Apply MedDRA/WHO-DD with AI suggestions and QC to standardize terms consistently across studies.
6. Reconciliation & Safety: Reconcile AE/SAE, con-med, exposure, and protocol deviations with AI-generated cross-checks and exception lists.
7. Centralized Monitoring & RBQM: Partner with monitoring teams to review AI risk scores, trend dashboards, and data quality signals for focused follow-up.
8. Regulatory Compliance & Audit Readiness: Maintain audit trails, privacy controls (PII/PHI), and inspection readiness; use AI for completeness checks.
9. Reporting & DBL: Produce listings, metrics, and DBL checklists; use AI to forecast timelines, identify lingering risks, and prepare clean files for submission teams.

1. Clinical Data Manager (AI-enabled)
2. Clinical Data Scientist
3. CDM Automation Specialist
4. RBQM / Central Monitoring Analyst
5. Data Standards & Coding Specialist
6. Data Quality / Compliance Analyst

1. Understand core CDM responsibilities in compliance with GCP, 21 CFR Part 11, and sponsor/CRO SOPs.
2. Apply AI-based tools for eCRF design, edit-check generation, anomaly detection, and smart query workflows.
3. Use NLP for narratives and AI-assisted coding (MedDRA/WHO-DD) with effective QC strategies.
4. Interpret risk dashboards and centralized monitoring outputs to drive targeted data review.
5. Conduct AI-supported reconciliation, reporting, and DBL readiness activities in collaboration with cross-functional teams.

Engage with professional communities and standards bodies central to data management and submissions. Stay current with CDISC, data privacy (HIPAA/GDPR), centralized monitoring, RBQM, and EDC innovations. Follow reputable industry platforms and publications for updates on clinical trials, CDM, pharmacovigilance interfaces, and regulatory expectations.

Graduates gain access to Qtech-Sol’s Resume Marketing Services (RMS)—designed to accelerate job placement in AI-integrated data management roles.

Post-training assistance includes:

1. Resume & LinkedIn Optimization: Highlight both CDM fundamentals and AI capabilities.
2. Narrative Development: Practice interview storytelling aligned to CDM/AI scenarios.
3. Mock Interviews: Focused on data cleaning, reconciliation, coding, RBQM, and compliance.
4. Job Strategy & Market Insights: Target the right roles and understand hiring trends.
5. Direct RMS Promotion: Actively promote your resume to our employer network.

1. Foundational Clinical Data Management Lessons
2. Introduction to Clinical Data Management – Overview of CDM processes, responsibilities, and data life cycle in clinical trials.
3. Drug Development Overview for CDM – Understanding where CDM fits within preclinical and clinical phases.
4. CRF/eCRF Design Principles – Designing effective data capture tools to ensure accuracy, compliance, and efficiency.
5. EDC Systems Overview (Rave, Inform, REDCap, etc.) – End-to-end exposure to leading electronic data capture tools used in the industry.
6. Edit Checks: Design and Specification – Building robust edit-check specifications for clean, reliable datasets.
7. Data Validation Plan (DVP) & Test Scripts – Developing and executing DVPs, including validation testing and documentation.
8. Data Reconciliation (AE/SAE, Con-Med, Exposure) – Performing data consistency checks across multiple sources and safety systems.
9. Medical Coding (MedDRA, WHO-DD) – Coding of adverse events and concomitant medications with AI-enabled tools.
10. RBQM & Centralized Monitoring – Leveraging risk-based monitoring and centralized data review strategies.
11. Database Lock (DBL) Readiness & Checklist – Executing final QC, audit checks, and data freeze/lock procedures.

1. Introduction to AI & Machine Learning in Clinical Data Management – Explore automation, predictive insights, and intelligent data handling.
2. AI-Driven CRF/eCRF Design & Intelligent Edit Checks – Build smart eCRFs and auto-generate edit checks with AI tools.
3. Automating Query Management & Discrepancy Resolution – Apply NLP and automation to resolve queries efficiently.
4. Machine Learning for Data Validation & Risk Detection – Detect anomalies, outliers, and risk trends across datasets.
5. Natural Language Processing (NLP) in AE Narratives & Medical History – Structure unformatted clinical text for improved consistency.
6. AI in MedDRA & WHO-DD Coding Assistance – Implement AI-assisted coding for efficient and consistent term mapping.
7. Predictive Analytics for Risk-Based Monitoring & Trial Oversight – Identify high-risk sites and streamline monitoring.
8. AI Tools for Audit & Inspection Readiness in CDM – Automate data traceability, deviation tracking, and compliance reporting.
9. Case Studies – AI in CROs and Pharma CDM Teams – Real-world examples of AI integration in CDM operations.
10. Future Trends – AI, Cloud Integration & Digital Trials – Emerging innovations shaping the next era of CDM.

For more information, please call us at +1 732.770.4100 / +1 732.207.4564 (WhatsApp) or email qpdc@qtech-solutions.com. Our course specialists will reach out to you promptly to assist you in taking the next steps toward your career goals.