AI IN CLINICAL DATA MANAGEMENT - Qtech-Sol offers Clinical Research / Trials, Pharmacovigilance, Drug Safety, Clinical Data Management, Clinical SAS Programming and Healthcare BA Training Programs
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Analytics and Reporting
 
Duration:
6 Weeks / 110 Hours
 
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AI in Clinical Data Management

Introduction –AI in Clinical Data Management

Artificial Intelligence (AI) is transforming Clinical Data Management (CDM) by automating repetitive tasks, improving data quality, and enabling predictive insights. From intelligent query detection to automated discrepancy management and risk-based monitoring, AI is helping CDM professionals deliver faster, cleaner, and regulatory-ready data.
The CDAI program introduces learners to the applications of AI in clinical data workflows, combining traditional CDM knowledge with modern AI tools and techniques. This program prepares professionals to work in next-generation data management roles, where efficiency, accuracy, and innovation are key.

Course Name :  AI in Clinical Data Management
Course Code :  CDAI
Experience Level :  Intermediate to Advanced
Qualification :  Bachelor’s / Master’s in Life Sciences, Data Science, Statistics, Computer Science, or related fields
Student Category :  Clinical Data Managers, Data Analysts, SAS Programmers, Career Changers into AI-driven roles

Delivery Type

SIP – Self-Paced Online with Support

Duration & Delivery
  1. Course Duration: 6 weeks (110 hours)
  2. Format: Self-Paced Online with Support (narrated lessons, readings, quizzes, and role-based tasks).
Key Learning Objectives

   By completing this program, learners will:

  2. Understand how AI and machine learning apply to CDM processes.
  3. Explore AI-driven CRF/eCRF design and intelligent edit checks.
  4. Learn how AI supports automated query management and discrepancy resolution.
  5. Gain insights into predictive models for risk-based monitoring and trial quality.
  6. Discover AI tools that support data cleaning, coding (MedDRA/WHO-DD), and audit readiness.
  7. Learn about natural language processing (NLP) for automating adverse event narratives.
  8. Review real-world case studies of AI implementation in clinical trials and CDM.
Who Should Take This Course
  1. Clinical Data Managers preparing for AI-enabled roles.
  2. Data Analysts / SAS Programmers transitioning into AI-powered CDM.
  3. Clinical Research staff interested in automation and efficiency tools.
  4. Career changers with data/IT backgrounds entering pharma and CRO CDM teams.
Benefits & Outcomes
  1. Gain hands-on exposure to AI applications in clinical data management.
  2. Learn how to automate data cleaning, query handling, and coding.
  3. Improve career readiness for AI-integrated roles in pharma/CROs.
  4. Strengthen knowledge of regulatory and compliance requirements in AI adoption.
  5. Position yourself at the forefront of next-generation CDM practices.
Career Pathways: After completing this course.
  1. AI-Enabled Clinical Data Manager
  2. Clinical Data Scientist
  3. Risk-Based Monitoring Specialist
  4. AI Data Analyst in Pharma
  5. Digital Trial Operations Manager

Curriculum & Modules

Modules
1. Introduction to AI & Machine Learning in Clinical Data Management

2. AI-Driven CRF/eCRF Design & Intelligent Edit Checks

3. Automating Query Management & Discrepancy Resolution

4. Machine Learning for Data Validation & Risk Detection

5. Natural Language Processing (NLP) in AE Narratives & Medical History

6. AI in MedDRA & WHO-DD Coding Assistance

7. Predictive Analytics for Risk-Based Monitoring & Trial Oversight

8. AI Tools for Audit & Inspection Readiness in CDM

9. Case Studies – AI in CROs and Pharma CDM Teams

10. Future Trends – AI, Cloud Integration & Digital Trials
Getting in Touch:

For more information, please call us at +91 8925971788 / +91 8977943100 (WhatsApp)or email qpdc@qtechelearncenter.com. Our course specialists will reach out to you promptly to assist you in taking the next steps toward your career goals.