Clinical data management (CDM) ensures the collection, integration, and availability of high-quality data from clinical trials. It supports clinical research and analysis across different study types. CDM tools like CDMS help manage large amounts of multicenter trial data. Regulations like 21 CFR Part 11 require electronic records and validated systems to ensure accurate, reliable data. Guidelines from SCDM and CDISC provide standards for good CDM practices and data collection. CDM processes clinical research data from source documents through database entry, quality checking, analysis, and archiving to support regulatory approval and conclusions about clinical results.

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Clinical Data
Management
Reasons &
Requirements

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Table of Contents
Introduction……………………………
………………………… 3
Reasons of Clinical Data
Management……………………………
………………………. 4
Tools………………………………………
…………………………… 5
I. CDMS………………………………………
……......... 5
II. Commonly use CDM
Tools……………… 6
III. Use of
Systems………………………………….
7

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Regulations, Guidelines &
Standard’s……… 9
I. SCDM…………………………………………
……………… 11
II. CDISC…………………………………………
……………… 12
A.
SDTMIG……………………………………
…………… 13
B.
CDASH……………………………………
……………. 13
Relation to Clinical
Research………………………. 14

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Conclusion………………………………
…………………………. 16
Introduction
clinical data management (CDM) is a
critical process in clinical research,
which leads to generation of high-
quality, reliable, and statistically sound
data from clinical trials.
Clinical Data Management ensures
collection, integration and availability
of data at appropriate quality and cost.
It also supports the conduct,
management and analysis of studies
across the spectrum of clinical research
as defined by
the national institutes of health (NIH).

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The ultimate goal of CDM is to ensure
that conclusions drawn from research
are well supported by the data.
achieving this goal protects public
health and confidence in marketed
therapeutics.

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Reasons of Clinical Data
Management
1. Review & approval of new drugs by
regulatory agencies is dependent
upon a trust that clinical trial data
presented are of sufficient integrity
to ensure confidence in results &
presented by pharma company.
2. Important to obtaining that trust is
adherence to quality
standards & practices.
3. Companies must assure that all
staff involve in clinical research
are trained &
qualified to perform data
management task(s).

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Tools
I. CDMS
Many software tools are available
for data management, and these
are called Clinical Data
Management Systems (CDMS).
In multicentric trials, a CDMS has
become essential to handle the
huge amount of data. Most of
the CDMS used in
pharmaceutical companies are

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commercial, but a few open
source tools are available as well.

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II. Commonly Use CDM
Tools
Commonly used CDM tools are ORACLE
CLINICAL, CLINTRIAL, MACRO, RAVE, and
eClinical Suite.
In terms of functionality, these software
tools are more or less similar and there is no
significant advantage of one system over
the other.
These software tools are expensive and
need sophisticated Information Technology
infrastructure to function.
Some multinational pharmaceutical giants
use custom-made CDMS tools to suit their
operational needs and procedures.
Among the open source tools, the most
prominent ones are OpenClinica,

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openCDMS, TrialDB, and PhOSCo. These
CDM software are available free of cost and
are as good as their commercial
counterparts in terms of functionality.

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III. Use of Systems
In regulatory submission studies, maintaining
an audit trail of data management activities is of
paramount importance. These CDM tools
ensure the audit trail and help in the
management of discrepancies.
According to the roles and responsibilities,
multiple user IDs can be created with access
limitation to data entry, medical coding,
database designing, or quality check.
This ensures that each user can access only the
respective functionalities allotted to that user ID
and cannot make any other change in the
database. For responsibilities where changes are
permitted to be made in the data, the software
will record the change made, the user ID that

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made the change and the time and date of
change, for audit purposes (audit trail).
During a regulatory audit, the auditors can
verify the discrepancy management process; the
changes made and can confirm that no
unauthorized or false changes were made.

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Regulations,
Guidelines &
Standard’s
Since the pharmaceutical industry relies on
the electronically captured data for the
evaluation of medicines, there is a need to
follow good practices in CDM and maintain
standards in electronic data capture.
These electronic records have to comply
with a Code of Federal Regulations (CFR),
21 CFR Part 11. This regulation is
applicable to records in electronic format
that are created, modified, maintained,
archived, retrieved, or transmitted. This

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demands the use of validated systems to
ensure accuracy, reliability, and consistency
of data with the use of secure, computer-
generated, time-stamped audit trails to
independently record the date and time of
operator entries and actions that create,
modify, or delete electronic records.
Adequate procedures and controls should
be put in place to ensure the integrity,
authenticity, and confidentiality of data.
If data have to be submitted to regulatory
authorities, it should be entered and
processed in 21 CFR part 11-compliant
systems.
Most of the CDM systems available are like
this and pharmaceutical companies as well

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as contract research organizations ensure
this compliance.

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I. SCDM
Society for Clinical Data Management
(SCDM) publishes the Good Clinical Data
Management Practices (GCDMP)
guidelines, a document providing the
standards of good practice within CDM.
GCDMP was initially published in
September 2000 and has undergone several
revisions thereafter.
The July 2009 version is the currently
followed GCDMP document.
GCDMP provides guidance on the accepted
practices in CDM that are consistent with
regulatory practices. Addressed in 20
chapters, it covers the CDM process by

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highlighting the minimum standards and
best practices.

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II.CDISC
Clinical Data Interchange
Standards Consortium (CDISC), a
multidisciplinary non-profit
organization, has developed
standards to support acquisition,
exchange, submission, and archival of
clinical research data and metadata.
Metadata is the data of the data
entered. This includes data about the
individual who made the entry or a
change in the clinical data, the date
and time of entry/change and details
of the changes that have been made.

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A. SDTMIG
The SDTMIG standard describes the
details of model and standard
terminologies for the data and serves
as a guide to the organization.
B. CDASH
CDASH defines the basic standards
for the collection of data in a clinical
trial and enlists the basic data
information needed from a clinical,
regulatory and scientific perspective.

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Relation to Clinical
Research
1. Source data are generated, including
medical records, laboratory results, and
patient diaries, etc.
2. If paper Case Report Forms (CRFs)
are being used, the clinical site records
are transcribed onto the CRFs.
3. Data from the CRFs, as well as other
source data, are entered into the clinical
trial database. Electronic CRFs (eCRFs)
allow data to be entered directly into the
database from source documents. Data
from paper CRFs are often entered twice
and and reconciled in order to reduce
the error rate.

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4. The data are checked for accuracy,
quality, and completeness, and
problems are resolved. This often
involves queries to the clinical site.
5. The database is locked when the
data are considered final.
6. The data are reformatted for
reporting and analysis. Tables, listings,
and figures are generated.
7. The data are analyzed, and the
analysis results are reported. When
significant results are found, this step
may result in the generation of
additional tables, listings, or figures.
8. The results are integrated into high-
level documentation such as
Investigator’s Brochures (IBs) and
Clinical Study Reports (CSRs).

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9. The database and other study data
are archived.

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Conclusion
CDM has evolved in response to the
ever-increasing demand from
pharmaceutical companies to fast-track
the drug development process and from
the regulatory authorities to put the
quality systems in place to ensure
generation of high-quality data for
accurate drug evaluation. To meet the
expectations, there is a gradual shift
from the paper-based to the electronic
systems of data management.
CDM professionals should ensure the
standards for improving data quality.
The biggest challenge from the

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regulatory perspective would be the
standardization of data management
process across organizations, and
development of regulations to define
the procedures to be followed and the
data standards.
CDM is evolving to become a standard
based clinical research entity, by striking
a balance between the expectations
from and constraints in the existing
systems, driven by technological
developments and business demands.