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Clinical Data Management (CDM)


Regulated industries, such as pharmaceutical, medical device, and life science industries, need clinical research studies to market new products. These studies are controlled by country- or region-specific regulations. The most common regulations are GLP, GCP, and CFR Part 11. But the list can be extensive.

Clinical data contains information for developing and sustaining software systems, databases, processes, procedures, training, and protocols. Clinical data management enables organizations to maintain data integrity throughout the duration of a clinical research study. Correct data management ensures that a dataset is accurate, secure, reliable, and ready for analysis.

Why is CDM important?


Clinical research studies provide crucial information. For example, results should demonstrate that a product is safe and meets requirements. Clinical data management provides:


  • Assurance of data quality
  • Accelerated development
  • Protection from data loss
  • Reduced expenses
  • Security
  • Complete and accurate collection of data
  • A clean dataset to support statistical analysis and reporting
  • A formatted dataset for optimal and timely usability
  • Assurance of data integrity and quality during database transfer
  • True representation of the trial in the study database

The five stages of CDM


Clinical data management consists of five stages, which span data collection, archiving, and presentation. The workflow starts when the CDM team generates a case report form (CRF) and ends when the database locks. The data manager executes quality checks and data cleaning throughout the workflow.








CRF design

Database design

Data mapping

Study conduct 

Study closeout


This stage comprises the design of the CRF, which guides data collection.

The database should have space for the study data.

This stage combines data that comes in various formats, which enables researchers to report continuously.

This stage includes activities associated with SAEs and potential events, which should be reviewed and corrected by the data manager.

Once a study is complete, the data manager should lock the database so that no one can change the data.


Adverse effect (AE) forms

Severe adverse effect (SAE) forms

Concomitant therapy forms

Eligibility screenings

Follow-up visits

Lab test forms

Medical histories

Physical exams and vitals


Status evaluations

Automated edit checks

Backend tables

Data stored in the
CDM system (CDMS)

Study-specific data entry fields and screens

Data entry assessment

Data inconsistency detection

Testing performed at the site

Data entry screens and programming testing using subject information

Testing and checks based on the list approved by the study sponsor

CRF tracking

Data entry or data transfer from
the CRF to the CDMS

Discrepancy management

Data coding

Data review and ongoing quality control

Data transfer

Data import protocols

Sponsor submissions

SAE reconciliation

Quality control

Database lock

Database maintenance and archiving

Final study report



Roles and responsibilities

Depending on the study, roles and responsibilities can differ.


Data manager
(project manager)

Supervises the CDM team throughout the process

Acts as a key player in discussions about data collection

Ensures the accuracy and integrity of the study data

Coordinates data management activities

Handles and verifies the data

Manages data validation

Oversees the application of quality control procedures

Takes responsibility for database locks

Database programmer
or designer

Performs the CRF annotation

Creates the study database

Designs the data entry screens

Programs the edit checks for data validation

Validates the edit checks with a dummy dataset

Medical coder

Codes variations, such as adverse events and medical histories

Clinical data coordinator

Designs the CRF

Creates the CRF instructions (CRF completion guidelines)

Creates the discrepancy protocols

Quality control associate

Checks the accuracy of data entry and performs data audits

Data entry associate

Tracks receipt of the CRF pages and enters the data into the database

Investigators and clinicians

Collect the data during the study using CRFs

Site and data personnel

Enter the data into the database following receipt of the CRF pages


Conduct statistical analysis of the study data

Medical writers

Prepare the study reports

Tools for CDM


Managing clinical data while keeping it secure and accessible is a challenge. Thankfully, using a CDM plan (DMP) or CDMS makes the task easier.


What is a DMP?


Clinical DMPs contain all of the work that is needed for a clinical research study. A comprehensive DMP should be generated and agreed upon by all parties. It should include milestones, deliverables, timelines, and industry-related data standards, such as The Clinical Data Acquisitions Standards Harmonization (CDASH). The CDASH specifies 16 standards for maintaining consistent data collection across studies. A DMP should be a living document so that it can be updated during a clinical research study. Organizations can make DMPs a part of their QMS documentation by using a controlled DMP template that fits their studies. A template can contain the following details:


  • Database design specifications
  • Data management plans
  • Database development checklists
  • Database validation plans
  • Risk analysis assessments
  • User acceptance test plans
  • Data processing guidelines
  • CRF reviews
  • Data cleaning guidelines
  • Validation reports


What is a CDMS?


Clinical data management systems, also known as clinical trial management systems (CTMS), are designed to help clinical research studies meet CDM requirements. A CDMS or CTMS allows for in-house planning, reporting, and tracking. Therefore, clinical research studies can collect more efficient, compliant, and successful results. Companies use these systems to collect, integrate, and validate data. A CDMS or CTMS offers the following advantages:


  • Build trust with regulatory authorities
  • Monitor data remotely
  • Incorporate artificial intelligence
  • Balance risk reduction and lead time
  • Use module-based programming, which allows users more functionality
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