What is Clinical Data Management (CDM)?

Clinical data management (CDM) plays an essential role in the data collection phase of clinical research. The process of collecting and managing research data is done in accordance with regulatory standards to obtain quality information that is complete and error-free; the goal is to gather as much of such data for analysis as possible.

The field of clinical data management (CDM) has come about due to demands from both the pharmaceutical industry and the regulatory authorities. As the drive to “fas-track” the development of pharmaceutical products continues to accelerate, regulatory entities have responded by requiring quality-assurance standards be met in collecting the data used in the drug evaluation process.

Two such standards created by the Clinical Data Interchange Standards Consortium (CDISC), are particularly vital to CDM: the Study Data Tabulation Model Implementation Guide for Human Clinical Trials (SDTMIG) and the Clinical Data Acquisition Standards Harmonization (CDASH). The former is currently mandated for use by the U.S. Food and Drug Administration (FDA). The latter created a standard format for collecting data across studies so that data submissions can be more easily traced and reviewed. Specialized tools (i.e., software applications) are used in CDM to create audit trails that allow discrepancies to be minimized even in large and complex clinical trials. Clinical data management systems (CDMS) are especially vital in trials conducted across medical centers in which an enormous amount of data is produced.

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Examples include Oracle Clinical, rave, eClinical suite, Clintrial, and Macro. CDMSs can be customized and may be tailor-made in the case of large, multinational pharmaceutical companies looking for tools that address the needs of their specific companies. Open source tools such as TrailDB, open CDMS, OpenClinica, and PhOSCo are freely available and can be just as effective. Set standards guide the work of CDM professionals because, as mentioned above, the information is used in the pharmaceutical industry to evaluate medicine. The Code of Federal Regulations (CFR), 21 CFR Part 11 provides the compliance standards to which CDM systems must adhere.

Clinical Data Management (CDM) Work

To maintain the integrity of data, the CDM process starts at the very beginning of a clinical trial, even before the study protocol is finalized. The CDM team designs a case report form (CRF) and defines the data fields to be utilized. CRFs specify the type of data to be collected, the units of measurement to be used, and CRF completion guidelines (i.e., instructions for filling in data). Variables are annotated using coded terms.

A data management plan (DMP) is then developed as a guide, including a description of the trial’s CDM activities. Databases are built to support CDM tasks with corresponding compliance tools. Testing is done before using the plan with actual clinical trial data. CFR tracking, data entry, validation, discrepancy management, medical coding, and database locking are subsequent steps in the process.

Case report forms may be used to collect data by paper or electronic means; however, as technology has continued to evolve, the trend towards electronic data collection has followed suit. Furthermore, remote data entry or e-CRF has been adopted by many pharmaceutical companies as a time-saving measure.

What Roles Are Involved in Clinical data management & Specializations

CDM requires a variety of roles and responsibilities of team members and take the form of:

Data manager – supervises the CDM process. Database programmer or designer – performs the CRF annotation, creates the study database, enables data validation, designs data entry screens and performs edit checks using dummy data.
Medical coder – codes variations such as adverse events and medical history.
Clinical data coordinator – designs the CRF, prepares the filling instructions, develops discrepancy protocolsQuality control associate – checks the accuracy of data entry and performs data audits.
Data entry associate – tracks the receipt of CRF pages and enters data into a database.

Clinical Data Management Coursework & Specializations

Certificate programs in CDM offer coursework in areas such as computational tools for clinical research, clinical trials management, applied biostatistics in clinical trials, and drug development from discovery to commercialization.

Master’s programs are focused on clinical research as a whole with the following specializations and coursework:


New product research and development
Regulatory compliance, ethics, and law
Biostatistics and data management
Clinical research management and safety surveillance
New therapeutic product business and strategic planning


Business processes and contemporary concerns in pharmaceutical R&D
Worldwide regulatory submissions
Introduction to clinical trials
Introduction to clinical pharmacology
Ethical issues in research
Medical device combination product regulation
The history of misconduct in biomedical research
Current issues in review boards
Current federal regulatory issues in biomedical research
Patient recruitment and informed consent
Pharmaceutical law
International regulatory affairs
Compliance and monitoring issues
Contemporary issues in human research protection
Quality assurance audits
Applications of clinical research biostatistics
Clinical data management
Designing the clinical trial
Health policy and economics
Clinical outsourcing

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Clinical Data Management – A Soft Hand Effort to Regulate the Clinical Study Results

Data Management (DM) is crucial for clinical research. Data Management is the process of collection, cleaning, and management of subject data in compliance with regulatory standards. The primary objective of DM processes is to provide high-quality data by keeping the number of errors and missing data as low as possible and thus gather maximum and accurate data for analysis. To meet this objective, best practices are adopted to ensure that data are complete, reliable, and processed correctly. This has been facilitated by the use of software applications that maintain an audit trail and provide easy identification and resolution of data discrepancies. Sophisticated innovations have enabled DM to handle large trials and ensure the data quality even in complex trials.

Data management start by entering patient real studydata into clinical database with the help of dataentry team and the entered data is then verified by the expert professionals. DataManagement flow is more intense during datavalidation, datareview and cleaning activities. During the data cleaning, validation checks are run against study data in clinical database, and any differences and discrepancies are highlighted by the programmed edit checks. Discrepancies are internally resolved by pre-defined obvious corrections, some are automatically dispatched to study sites for prompt resolutions. The study data manager close any resolved discrepancies in electronic datacapture (EDC) database that are not closed automatically by the EDC database after updation. Some discrepancies are source-verified and closed by study monitors. Audit trail is usually included in clinical database to maintain the reason for any updates made as result of processed discrepancies in the database.

Datamanagement also includes manual review of discrepancies which are unable to capture by programmed edit checks. Serious adverse event data, external party info such as laboratory information and interactive voice response system (IVRS) information are generally reviewed manually, and after that manual dataclarification forms (DCF) are created and referred to study sites for further resolution. Then DM personnel receives dataresolutions from the study site and which is then updated to the database after review.

Although quality control (QC) on study data processed, reviewed and cleaned of any error is done throughout the life cycle of clinical trial, it is one of the major activities of DM flow prior to clinical database lock. QC is done on all the data update and database system to ensure integrity and high quality of study infomation prior to its statistical analysis. After the database is locked, the final information is then transferred to statistician team. Quality control is thus essential to ensure the accuracy, completeness and uniformity of the processed study data and database system.

Another important step in clinical database management is the database development. Clinical software applications are designed to facilitate the clinicaldatamanagement tasks which render conducting clinical studies efficient. Usually, these tools are in compliance with the regulatory requirements and are simple to handle, yet they are to be scrutinized for the infomartion security. Thus, to ensure the datasecurity, system validation is done for user requirements, system specifications, and regulatory compliance, just before the implementation. Other study details including objectives, visits, intervals, sites, investigators, and patients are defined in the database and CRF layouts are designed for data entry. Before moving them in to the real datacapture, these entry screens are also tested with dummy info.