1. To Derive The Most Efficient Software Used In
Clinical Data Management
Project report submitted for partial fulfillment of requirement
for the award of ACCR at Apollo Hospitals, New Delhi
Submitted by:
VARNIKA SRIVASTAVA
PREETI DAHIYA
KOMAL BHANOT
POOJA MISHRA
Under the supervision of
Institutional Guide Mrs. Sunita K. and Mrs. Rinku Dahiya
at
Apollo Hospitals Educational and Research Foundation(AHERF)
New Delhi, affiliated to
Anna University, Chennai
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2. ACKNOWLEDGEMENT
I have taken efforts in this project. However, it would not have been possible
without the kind support and help of many individuals and organizations. I would
like to extend my sincere thanks to all of them.
I am highly indebted to Mrs.sunita KuMari & Mrs.rinKu Dahiya for their
guidance and constant supervision as well as for providing necessary information
regarding the project & also for their support in completing the project.
I would like to express my gratitude towards my parents & member of AHERF for
their kind co-operation and encouragement which help me in completion of this
project.
I would like to express my special gratitude and thanks to industry persons Mohit
Sanyal,Praveen Shukla ,Prerit Burman & Rangnath Banerjee for giving me such
attention and time.
My thanks and appreciations also go to my colleague in developing the project
and people who have willingly helped me out with their abilities
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3. DECLARATION
I declare that the project entitled as “to derive the most efficient software used in clinical
data management”is my own work conducted under the supervision of Institutional guide Mrs
Sunita K. and Mrs.Rinku Dahiya, Apollo Hospitals Educational and Research Foundation
,New Delhi
I further declare that to the best of my knowledge the thesis does not contain any part of any
work which either has been submitted for the award of any degree/diploma in any university or
have been published anywhere without proper citation.
Varnika
Srivastava
Preeti Dahiya
Komal Bhanot
Pooja Mishra
INDEX
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4. Serial no Content Page no
1 Abbreviations 4
2 Abstract 5
3 Introduction 6-13
4 Aims and Objective 14
5 Review of Literature 15-23
6 Material and methodology 24-25
7 Results 26-28
8 conclusion 29
9 References 30 -31
10 Annexure 32-34
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5. Abbreviations
CDM Clinical data management
CRF Case report form
e- CRF Electronic case report form
DCF Data clariformation form
DVP Data validation process
SCDM Society for clinical data management
GCDMP Good clinical data management practices
CDASH Clinical Data Acquisition Standards Harmonization
CDISC Clinical Data Interchange Standards Consortium
SDTMIG Study Data Tabulation Model Implementation Guide for Human Clinical
Trials
EDC Electronic data capture
DMP Data management plan
CFR Code of Federal Regulation
CRA Clinical research associate
SEC Self-evident correction
MedRA Medical Dictionary for Regulatory Activities
WHO-DDE World Health Organization–Drug Dictionary Enhanced
WHO-ART World Health Organization-adverse reaction terminology
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6. An overview: Data management in clinical research
ABSTRACT
Clinical Data Management (CDM) is a critical phase in clinical research, which leads to
generation of high-quality, reliable, and statistically sound data from clinical trials. This helps to
produce a drastic reduction in time from drug development to marketing. Team members of
CDM are involved in all stages of clinical trial right from inception to completion. They should
have adequate process knowledge that helps maintain the quality standards of CDM processes.
Various procedures in CDM including Case Report Form (CRF) designing, CRF annotation,
database designing, data-entry, data validation, discrepancy management, medical coding, data
extraction, and database locking are assessed for quality at regular intervals during a trial. In the
present , there is an increased demand to improve the CDM standards to meet the regulatory
requirements and stay ahead of the competition by means of faster commercialization of
product. Additionally, it is becoming mandatory for companies to submit the data electronically.
CDM professionals should meet appropriate expectations and set standards for data quality and
also have a drive to adapt to the rapidly changing technology. This article highlights the
processes involved and provides the reader an overview of the tools and standards adopted as
well as the roles and responsibilities in CDM and comparision of different softwares.
KEY WORDS: Clinical data management systems, data management, e-CRF, good clinical
data management practices, validation
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7. Introduction
Clinical trial is intended to find answers to the research question by means of generating data for
proving or disproving a hypothesis.
Clinical data management is a relevant and important part of a clinical trial. Without identifying
the technical phases, we undertake some of the processes involved in CDM during our research
work. This article highlights the processes involved in CDM and gives the reader an overview
of how data is managed in clinical trials.
CDM is the process of collection, cleaning, and management of subject data in compliance with
regulatory standards. The primary objective of CDM processes is to provide high-quality data
by keeping the number of errors and missing data as low as possible and gather maximum data
for analysis.[1] 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[2] have enabled CDM to handle large trials and ensure
the data quality even in complex trials.
High-quality data should be absolutely accurate and suitable for statistical analysis. These
should meet the protocol-specified parameters and comply with the protocol requirements. This
implies that in case of a deviation, not meeting the protocol-specifications, we may think of
excluding the patient from the final database. It should be borne in mind that in some situations,
regulatory authorities may be interested in looking at such data. Similarly, missing data is also a
matter of concern for clinical researchers. High-quality data should have minimal or no misses.
But most importantly, high-quality data should possess only an arbitrarily ‘acceptable level of
variation’ that would not affect the conclusion of the study on statistical analysis. The data
should also meet the applicable regulatory requirements specified for data quality.
Tools for CDM
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 commercial,
but a few open source tools are available as well. 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. Additionally, 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, openCDMS, TrialDB, and PhOSCo.
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8. These CDM software are available free of cost and are as good as their commercial counterparts
in terms of functionality. These open source software can be downloaded from their respective
websites.
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 (explained later), 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
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.
Regulations, Guidelines, and Standards in CDM
Akin to other areas in clinical research, CDM has guidelines and standards that must be
followed. 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
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[3].
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 highlighting the minimum
standards and best practices.
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. Among the standards,
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9. two important ones are the Study Data Tabulation Model Implementation Guide for Human
Clinical Trials (SDTMIG) and the Clinical Data Acquisition Standards Harmonization
(CDASH) standards, available free of cost from the CDISC website (www.cdisc.org). The
SDTMIG standard[4] describes the details of model and standard terminologies for the data and
serves as a guide to the organization. CDASH v 1.1[5] 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.
The CDM Process
The CDM process, like a clinical trial, begins with the end in mind. This means that the whole
process is designed keeping the deliverable in view. As a clinical trial is designed to answer the
research question, the CDM process is designed to deliver an error-free, valid, and statistically
sound database. To meet this objective, the CDM process starts early, even before the
finalization of the study protocol.
Review And Finalization Of Study Documents
The protocol is reviewed from a database designing perspective, for clarity and consistency.
During this review, the CDM personnel will identify the data items to be collected and the
frequency of collection with respect to the visit schedule. A Case Report Form (CRF) is
designed by the CDM team, as this is the first step in translating the protocol-specific activities
into data being generated. The data fields should be clearly defined and be consistent
throughout. The type of data to be entered should be evident from the CRF . Similarly, the units
in which measurements have to be made should also be mentioned next to the data field. The
CRF should be concise, self-explanatory, and user-friendly (unless you are the one entering data
into the CRF). Along with the CRF, the filling instructions (called CRF Completion Guidelines)
should also be provided to study investigators for error-free data acquisition. CRF annotation is
done wherein the variable is named according to the SDTMIG or the conventions followed
internally. Annotations are coded terms used in CDM tools to indicate the variables in the study.
Based on these, a Data Management Plan (DMP) is developed. DMP document is a road map to
handle the data under foreseeable circumstances and describes the CDM activities to be
followed in the trial. The DMP describes the database design, data entry and data tracking
guidelines, quality control measures, SAE reconciliation guidelines, discrepancy management,
data transfer/extraction, and database locking guidelines. Along with the DMP, a Data
Validation Plan (DVP) containing all edit-checks to be performed and the calculations for
derived variables are also prepared. The edit check programs in the DVP help in cleaning up the
data by identifying the discrepancies.
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10. List of clinical data management activities
Database Designing
Databases are the clinical software applications, which are built to facilitate the CDM tasks to
carry out multiple studies.[6] Generally, these tools have built-in compliance with regulatory
requirements and are easy to use. “System validation” is conducted to ensure data security,
during which system specifications,[7] user requirements, and regulatory compliance are
evaluated before implementation. Study details like objectives, intervals, visits, investigators,
sites, and patients are defined in the database and CRF layouts are designed for data entry.
These entry screens are tested with dummy data before moving them to the real data capture.
Data Collection
Data collection is done using the CRF that may exist in the form of a paper or an electronic
version. The traditional method is to employ paper CRFs to collect the data responses, which are
translated to the database by means of data entry done in-house. These paper CRFs are filled up
by the investigator according to the completion guidelines. In the e-CRF-based CDM, the
investigator or a designee will be logging into the CDM system and entering the data directly at
the site. In e-CRF method, chances of errors are less, and the resolution of discrepancies
happens faster. Since pharmaceutical companies try to reduce the time taken for drug
development processes by enhancing the speed of processes involved, many pharmaceutical
companies are opting for e-CRF options (also called remote data entry).
CRF Tracking
The entries made in the CRF will be monitored by the Clinical Research Associate (CRA) for
completeness and filled up CRFs are retrieved and handed over to the CDM team. The CDM
team will track the retrieved CRFs and maintain their record. CRFs are tracked for missing
pages and illegible data manually to assure that the data are not lost. In case of missing or
illegible data, a clarification is obtained from the investigator and the issue is resolved.
Data Entry
Data entry takes place according to the guidelines prepared along with the DMP. This is
applicable only in the case of paper CRF retrieved from the sites. Usually, double data entry is
performed wherein the data is entered by two operators separately.[8] The second pass entry
(entry made by the second person) helps in verification and reconciliation by identifying the
transcription errors and discrepancies caused by illegible data. Moreover, double data entry
helps in getting a cleaner database compared to a single data entry. Earlier studies have shown
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11. that double data entry ensures better consistency with paper CRF as denoted by a lesser error
rate.[9]
Data Validation
Data validation is the process of testing the validity of data in accordance with the protocol
specifications. Edit check programs are written to identify the discrepancies in the entered data,
which are embedded in the database, to ensure data validity. These programs are written
according to the logic condition mentioned in the DVP. These edit check programs are initially
tested with dummy data containing discrepancies. Discrepancy is defined as a data point that
fails to pass a validation check. Discrepancy may be due to inconsistent data, missing data,
range checks, and deviations from the protocol. In e-CRF based studies, data validation process
will be run frequently for identifying discrepancies. These discrepancies will be resolved by
investigators after logging into the system. Ongoing quality control of data processing is
undertaken at regular intervals during the course of CDM. For example, if the inclusion criteria
specify that the age of the patient should be between 18 and 65 years (both inclusive), an edit
program will be written for two conditions viz. age <18 and >65. If for any patient, the condition
becomes TRUE, a discrepancy will be generated. These discrepancies will be highlighted in the
system and Data Clarification Forms (DCFs) can be generated. DCFs are documents containing
queries pertaining to the discrepancies identified.
Discrepancy Management
This is also called query resolution. Discrepancy management includes reviewing discrepancies,
investigating the reason, and resolving them with documentary proof or declaring them as
irresolvable. Discrepancy management helps in cleaning the data and gathers enough evidence
for the deviations observed in data. Almost all CDMS have a discrepancy database where all
discrepancies will be recorded and stored with audit trail.
Based on the types identified, discrepancies are either flagged to the investigator for clarification
or closed in-house by Self-Evident Corrections (SEC) without sending DCF to the site. The
most common SECs are obvious spelling errors. For discrepancies that require clarifications
from the investigator, DCFs will be sent to the site. The CDM tools help in the creation and
printing of DCFs. Investigators will write the resolution or explain the circumstances that led to
the discrepancy in data. When a resolution is provided by the investigator, the same will be
updated in the database. In case of e-CRFs, the investigator can access the discrepancies flagged
to him and will be able to provide the resolutions online.
Discrepancy management (DCF = Data clarification form, CRA = Clinical Research Associate,
SDV = Source document verification, SEC = Self-evident correction)
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12. The CDM team reviews all discrepancies at regular intervals to ensure that they have been
resolved. The resolved data discrepancies are recorded as ‘closed’. This means that those
validation failures are no longer considered to be active, and future data validation attempts on
the same data will not create a discrepancy for same data point. But closure of discrepancies is
not always possible. In some cases, the investigator will not be able to provide a resolution for
the discrepancy. Such discrepancies will be considered as ‘irresolvable’ and will be updated in
the discrepancy database.
Discrepancy management is the most critical activity in the CDM process. Being the vital
activity in cleaning up the data, utmost attention must be observed while handling the
discrepancies.
Medical Coding
Medical coding helps in identifying and properly classifying the medical terminologies
associated with the clinical trial. For classification of events, medical dictionaries available
online are used. Technically, this activity needs the knowledge of medical terminology,
understanding of disease entities, drugs used, and a basic knowledge of the pathological
processes involved. Functionally, it also requires knowledge about the structure of electronic
medical dictionaries and the hierarchy of classifications available in them. Adverse events
occurring during the study, prior to and concomitantly administered medications and pre-or co-
existing illnesses are coded using the available medical dictionaries. Commonly, Medical
Dictionary for Regulatory Activities (MedDRA) is used for the coding of adverse events as well
as other illnesses and World Health Organization–Drug Dictionary Enhanced (WHO-DDE) is
used for coding the medications. These dictionaries contain the respective classifications of
adverse events and drugs in proper classes. Other dictionaries are also available for use in data
management (eg, WHO-ART is a dictionary that deals with adverse reactions terminology).
Some pharmaceutical companies utilize customized dictionaries to suit their needs and meet
their standard operating procedures.
Medical coding helps in classifying reported medical terms on the CRF to standard dictionary
terms in order to achieve data consistency and avoid unnecessary duplication. For example, the
investigators may use different terms for the same adverse event, but it is important to code all
of them to a single standard code and maintain uniformity in the process. The right coding and
classification of adverse events and medication is crucial as an incorrect coding may lead to
masking of safety issues or highlight the wrong safety concerns related to the drug.
Database Locking
After a proper quality check and assurance, the final data validation is run. If there are no
discrepancies, the SAS datasets are finalized in consultation with the statistician. All data
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13. management activities should have been completed prior to database lock. To ensure this, a pre-
lock checklist is used and completion of all activities is confirmed. This is done as the database
cannot be changed in any manner after locking. Once the approval for locking is obtained from
all stakeholders, the database is locked and clean data is extracted for statistical analysis.
Generally, no modification in the database is possible. But in case of a critical issue or for other
important operational reasons, privileged users can modify the data even after the database is
locked. This, however, requires proper documentation and an audit trail has to be maintained
with sufficient justification for updating the locked database. Data extraction is done from the
final database after locking. This is followed by its archival.
Roles and Responsibilities in CDM
In a CDM team, different roles and responsibilities are attributed to the team members. The
minimum educational requirement for a team member in CDM should be graduation in life
science and knowledge of computer applications. Ideally, medical coders should be medical
graduates. However, in the industry, paramedical graduates are also recruited as medical coders.
Some key roles are essential to all CDM teams. The list of roles given below can be considered
as minimum requirements for a CDM team:
Data Manager
Database Programmer/Designer
Medical Coder
Clinical Data Coordinator
Quality Control Associate
Data Entry Associate
The data manager is responsible for supervising the entire CDM process. The data manager
prepares the DMP, approves the CDM procedures and all internal documents related to CDM
activities. Controlling and allocating the database access to team members is also the
responsibility of the data manager. The database programmer/designer performs the CRF
annotation, creates the study database, and programs the edit checks for data validation. He/she
is also responsible for designing of data entry screens in the database and validating the edit
checks with dummy data. The medical coder will do the coding for adverse events, medical
history, co-illnesses, and concomitant medication administered during the study. The clinical
data coordinator designs the CRF, prepares the CRF filling instructions, and is responsible for
developing the DVP and discrepancy management. All other CDM-related documents,
checklists, and guideline documents are prepared by the clinical data coordinator. The quality
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14. control associate checks the accuracy of data entry and conducts data audits.[10M] Sometimes,
there is a separate quality assurance person to conduct the audit on the data entered.
Additionally, the quality control associate verifies the documentation pertaining to the
procedures being followed. The data entry personnel will be tracking the receipt of CRF pages
and performs the data entry into the database.
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15. AIM and OBJECTIVE
AIM: - To derive the most efficient software used in clinical trial.
OBJECTIVE: - To compare efficiency of different software on the basis of literature data,
based on various parameters i.e. cost, time taken, reliability, security, user friendly and accuracy
etc.
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16. LITERATURE REVIEW
Review of literature
Increasing activity in the use of computers for acquisition, storage, and retrieval of medical
information has been stimulated by the growing complexity of medical care, and the needs for
standardization, quality control, and retrievability of clinical data. Criteria for the design of a
clinical data management system include flexibility in its interface with its environment, the
capability of handling variable length text string data, and of organizing it in tree-structured
files, the availability of this data to a multi-user environment, and the existence of a high-level
language facility for programming and development of the system. The scale and cost of the
computer configuration required to meet these demands must nevertheless permit gradual
expansion, modularity, and usually duplication of hardware. The MGH Utility Multi-
Programming System (MUMPS) is a compact time-sharing system on a medium-scale computer
dedicated to clinical data management applications. A novel system design based on a reentrant
high-level language interpreter has permitted the implementation of a highly responsive, flexible
system, both for research and development and for economical, reliable service operation.[12]
Evolution of CDM
A writing committee was formed that included a select group of 10 physicians who have been
involved in large-scale ACS clinical trials and other registries and who were recognized experts
in the field. Additionally, the writing committee included members who had expertise in
developing performance measures for patients with acute myocardial infarction. Finally, this
group included several international members to ensure balance in the selection of data elements
for the type of practice worldwide that would be reflected by the data elements and definitions
recommended in these standards. Toward that end, an informal collaboration with the European
Society of Cardiology (ESC) was established.
The subcommittee met several times over a period of 2 years to refine the data standards to their
present form. The overriding goals were to focus on important variables needed to assess the
characteristics of patients, their treatment with both medication and interventional therapies, and
their outcomes. In developing the list, the writing committee balanced completeness with length
and thus tried to be as concise as possible to facilitate use of these variables by others in an
actual registry or trial setting. Standardized definitions for each variable are provided. For these,
the writing committee again balanced greater specificity of definitions against what information
can readily and reliably be obtained from medical records to make these definitions functional in
the various real-world settings in which they may be used.
[13-15]
The document is presently divided into 3 sections:
•Introduction: A description of the methodology of developing the ACS Clinical Data Standards
and intended goals for their use.
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17. •Data Elements and Definitions: A listing of key data elements and definitions.
A multipurpose resource that maps common data fields between ACS core data elements and
other national/regional data registries, links ACS core data elements to relevant ACC/AHA
guidelines, and identifies potential uses for each core element.[16]
Methods
The REDCap project was developed to provide scientific research teams intuitive and reusable
tools for collecting, storing and disseminating project-specific clinical and translational research
data. The following key features were identified as critical components for supporting research
projects: (17) collaborative access to data across academic departments and institutions; user
authentication and role-based security; (19) intuitive electronic case report forms (CRFs); real-
time data validation, integrity checks and other mechanisms for ensuring data quality (e.g.
double-data entry options); data attribution and audit capabilities; protocol document storage
and sharingcentral data storage and backups data export functions for common statistical
packages; and data import functions to facilitate bulk import of data from other systems. Given
the quantity and diversity of research projects within academic medical centers, we also
determined two additional critical features for the REDCap project: a software generation cycle
sufficiently fast to accommodate multiple concurrent projects without the need for custom
project-specific programming; and a model capable of meeting disparate data collection needs
of projects across a wide array of scientific disciplines.
REDCap accomplishes key functions through use of a single study metadata table referenced by
presentation-level operational modules. Based on this abstracted programming model, studies
are developed in an efficient manner with little resource investment beyond the creation of a
single data dictionary. The concept of metadata-driven application development is well
established, so we realized early in the project that the critical factor for success would lie in
creating a simple workflow methodology allowing research teams to autonomously develop
study-related metadata in an efficient manner [19], [20] and [21]. In the following sections, we
describe the workflow process developed for REDCap metadata creation and provide a brief
overview of the user interface and underlying architecture.
Study Creation Workflow
Fig. 1 provides a schematic representation of the workflow methodology for building a REDCap
database for an individual study. The process begins with a request from the research team to
the Informatics Core (IC) for database services. A meeting is scheduled between the research
team and an IC representative for a formal REDCap demonstration. Key program features
(intuitive user interface, data security model, distributed work environment, data validation
procedures, statistical package-ready export features, and audit trail logging) are stressed during
the initial meeting in addition to providing project-specific data collection strategy consultation.
Researchers are given a Microsoft Excel spreadsheet file providing detailed instructions for
completing required metadata information (e.g. field name, end-user label, data type, data range,
etc) about each measurement in each case report form. They are asked to spend time over the
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18. next several days working with the spreadsheet template to define data elements for their
specific project, and then return the worksheet to the IC via electronic mail. The returned
worksheet is used to build and populate the study-specific database tables feeding a working
web-based electronic data collection (EDC) application for the project. A web link to the
prototype application is given to the research team along with instructions for testing and further
iteration of the metadata spreadsheet until the study data collection plan is complete. The system
is then placed in production status for study data collection. The workflow process typically
includes several members of the research group and allows the entire team to devise and test
every aspect of study data collection requirements before study initiation.
Fig. 1 Project Initiation Workflow
REDCap Project Initiation Workflow: Study-specific databases are created using data
dictionaries provided by the research team. After an initial demonstration, research teams use a
custom MS-Excel file template to provide project metadata. The informatics team uses this file
to create a prototype web application that researchers can test while revising their data
dictionary. Once consensus is reached by the research team on the entire data collection CRF
package, the application is moved to production status for study initiation.
User interface
The REDCap user interface provides an intuitive method to securely and accurately input data
relating to research studies. Fig. 2 shows a typical CRF view. Each form is accessible only to
users who have sufficient access privileges set by the research team. Forms contain field-
specific validation code sufficient to ensure strong data integrity. In addition to checking for
mandatory field type (e.g. numeric entry for systolic blood pressure), embedded functions also
check data ranges (e.g. 70–180 mmHg) and alert the end-user whenever entered data violates
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19. specified limits. Data fields may be populated using text fields or through embedded pull-down
boxes or radio buttons where the end-user is shown one value and a separate code is stored in
the database for later statistical analysis (e.g. 0 = No, 1 = Yes).
Fig. 2 REDCap Demo Database
REDCap Data Entry: Case report forms are accessible to users who have sufficient
access rights and contain field-specific client-side validation code sufficient to ensure
data integrity. In addition to checking for mandatory field type (e.g. numeric entry for
systolic blood pressure), validity functions also check data ranges (e.g. 70–180 mmHg)
and alert the end-user whenever entered data violates specified limits. CRF pages and
REDCap functional modules are accessible to end users by clicking links on the right-
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20. side application menu of each project.
Clickable project menu items are shown on the right side of the screen in Fig. 2. All menu items
in the Data Entry tab point to CRFs specific to the scientific project, while the Applications tab
contains menu items pointing to general REDCap functions. Researchers use the “Data Export
Tool” to push collected data out of the REDCap system for external analysis and may select
entire forms and/or individual fields for export. The module returns downloadable raw data files
(comma-delimited format) along with syntax script files used to automatically import data and
all context information (data labels, coded variable information) into common statistical
packages (SPSS, R/S+, SAS, Stata). The “Data Import Tool” module allows bulk upload of data
from existing files with automatic validation of data and audit trail creation similar to those
created when using CRF data entry methods. The “Data Comparison Tool” module provides a
mechanism to view and reconcile data for those studies employing double-data entry or blinded-
data entry methods. The “Data Logging” module provides users a view of all data transactions
for the duration of the project. The “File Repository” module allows end-users to store
individual supporting files for the project and retrieve wherever and whenever necessary. The
“Data Dictionary” module allows researchers to download a clean copy of the project metadata
during the iterative study data planning process and the “User Rights” module is used by project
administrators to add or remove research personnel and set individual security rights.
Different Softwares Used In Clinical Data Management
Open Clinica
The objective of OpenClinica is to harness the power of community thinking to help define a
common technology that can work effectively in diverse research settings
OpenClinica has its roots in Akaza Research, Inc., a bioinformatics company that began as a
startup idea around 2004[26]
and as an official business in May 2005.By then it had already
released a beta preview release and was eying a June 2005 release of an official version 1.0 of
their software, dubbed OpenClinica.[27]
Features of OpenClinica include:
• create studies/projects
• clinical data capture, normalization, and validation
• query management
• role-based security
• administrative tools
• reporting
• secure data sharing
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21. • automated and manual data interchange
• follows 21 CFR Part 11 guidelines
• modular and extensible [28]
Background
OpenClinica was born out of frustration with existing proprietary electronic data capture (EDC)
software that was inflexible, expensive, and difficult to consume. OpenClinica solved these
problems by giving customers:
• A way to quickly deploy powerful software for running a clinical study regardless of the
study's size, scope, language, budget, and other factors.
• The freedom from becoming locked-in to a single vendor
• The freedom to tailor an EDC solution to ones particular needs.
OpenClinica provides the industry with one of the more powerful software solutions for
capturing and managing clinical trial data. It allows you to build your own studies, design
electronic Case Report Forms (eCRF), and conduct a range of clinical data capture and clinical
data management functions.
Major Features
OpenClinica believes that flexible software built on 'open standards' and 'open architectures' can
best meet some of the most significant challenges facing clinical research today. It supports
Good Clinical Practice (GCP) quality standards and guidelines.
OpenClinica comes in both a Community & an Enterprise edition
The OpenClinica Community edition is freely available for all to use.
The OpenClinica Enterprise edition has additional capabilities and is commercially supported.
OpenClinica provides a range of consulting services related to setting up research studies and
conducting clinical trials, in addition to support services such as hosting, training, maintenance,
and tailored software enhancements.
Feature Highlights
• A robust and scalable technology infrastructure developed using the Java J2EE
framework and powerful PostgreSQL database.
• An intuitive interface for subject enrollment, clinical data capture, validation, and query
management.
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22. • As a web-based software solution, all users need is a PC, browser, and an Internet
connection.
• Tools for clinical data management, data cleansing, and site monitoring.
• Assistance in configuring study protocols: electronic Case Report Forms (eCRF), edit
checks/rules, users, and study event definitions, and more.
• Automated and manual capabilities for import, export, and data interchange data with
other systems in a variety of formats.
• Rules engine performing advanced validation of study data and define automated actions
within the system.
• Administrative tools for overall system oversight, auditing, configuration, and reporting.
• Security: setting up roles, access privileges, user authentication & passwords, electronic
signatures, SSL encryption, de-identification of protected personal data, and a
comprehensive set of auditing tools.(29)
2-DSGedc+
DSG’s award-winning eCaseLink software is the most advanced EDC solution in the industry.
eCaseLink 8.0 is a truly integrated eClinical solution that seamlessly combines CTMS, EDC,
IWRS and Safety into a single system, cutting costs and giving you control over your entire
study. Unlike other systems that attempt to force completely unrelated systems to work together,
eCaseLink was developed from the ground up as a truly integrated system whose parts work
effectively as a whole. eCaseLink helps you to see everything that’s happening with your study
in real time, at all times. This unmatched level of control helps you to make mid-study changes,
contain costs, improve safety and increase ROI.
eCaseLink provides the fastest start up, rapid database lock, and more rapid FDA submissions,
resulting in a greater ROI. eCaseLink features cross-panel edit checks that fire instantly upon
data entry, eliminating trial delays and need for time consuming server calls, making it easier for
your users to move through their data entry.
DSG's clinical trial software technology eCaseLink, is a unique proprietary, user friendly-
interface. eCaseLink sends instant data validation accelerating data collection and
ensuring consistent, high quality data in real time.
eCaseLink’s proprietary technology validates information field-by-field, instantly-without
connecting to the server or requiring client plug-ins. Instant validations decrease the number of
times the user must submit a form and the amount of bad data that must be queried after a
submission. When you save a page, you are saving a page of clean data. The result is cleaner
data and a rapid work flow.
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23. The Highest Site Acceptance Of Any EDC System On The Market
eCaseLink has the most user-friendly interface in the industry and our innovative data entry
screens are configurable to your look and feel for your eCRFs. The system is flexible and highly
configurable. It even adapts to a user’s individual workflow with SmartLinks, which
automatically adjust to the user interface creating a more efficient workflow for each user, based
on their unique workflow patterns. Users can easily access interactive reports at any time, view
a snapshot of queries, saved eCRFs, and enrollment, all using eCaseLink’s proprietary
dashboard from any screen — creating exceptional ease of use and high site acceptance, making
electronic data entry easy.
Clear Cost Advantage
DSG offers clients reasonable, straight-forward, cost effective pricing.
Rapid EDC Study Start-Ups
eCaseLink's page-based technology allows for rapid set-up, testing and deployment of the first
several visits of a study without delay, so there's no need to wait for the EDC team to create
validation edits. Library eCRFs are saved and re-used, affording faster trial set-up for future
studies. System development time lines including multiple third-party data integrations average
eight weeks.
Mid-Study Changes in Hours, Not Weeks
eCaseLink allows you to manage protocol amendments and any mid-study changes quickly and
seamlessly without any system downtime.
Local Lab Management System
eCaseLink enables data managers to effectively manage local labs and normal lab ranges, and
also provides interactive review of laboratory data.
Comprehensive eSourcing and Integration
Data integration functionality allows users to integrate and manage data from a wide variety of
industry sources in a single database with convenient dashboards for viewing and accessing data
including:
• Core, Central, Specialty Laboratory Data
• IVRS
• CTMS
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24. • ECG
• CT Scans & MRIs
• Topographies & Radiographics
• EMR
• Medical Device Output
• Safety Data
Complete Reporting
More than 40 standard reports are available as well as a library of configurable standard reports,
custom and ad-hoc reports. On-Demand data exports and full ad-hoc reporting enable real-time
data access and management.
Multi-language
eCaseLink multi-language capabilities include Japanese, Chinese and Western European
languages to support trials worldwide. This allows sponsors to run their clinical trial operations
on a single, global platform without needing to install different versions of the software for
different languages. Additionally, data managers and clinicians can work together
collaboratively using a single, centrally-managed global database regardless of geographic
location or language. (30)
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25. MATERIAL AND METHODOLOGY
Design & Method
Studies are designed using excel spreadsheet templates that allow a project manager to define
the required clinical parameters and validations for the study. The excel spreadsheets are then
upload to form the electronic case report forms (eCRFs). Studies and study visits are defined in
OpenClinica and the study visit data is captured by eCRFs assigned to that visit.
Studies are organised by study protocol and site,each with its own set of authorised users,
subjects, study visit definitions, and CRFs. This system allows sharing of resources across
studies in a secure manner.
This survey was based on various parameters i.e., cost, time taken, reliability, security, user
friendly and accuracy.Survey was conducted with a target of 50 employees in different
organization ,they are mainly from clinical Data Management background ,many opinoin and
many view came out during survey.Out of which Oracle Clinical was proved to be more user
friendly and promising.
Limitations:-
1-Due to issues with the implementation of hidden fields in repeating groups, we do not
recommend hiding individual fields with a repeating group of items in a CRF
2- There are problems with adding/ saving data on the last displayed row in a repeating group
whether the data item is a part of rules target or not. An example of an issue is during the
administrative editing phase; entering discrepancy note(s) on the last added row will cause the
discrepancy note(s) to get saved to the first row. Also, saving data on the last row of the
displayed repeating group that is a part of rules target triggers an alert that prevents user from
going to the next section.
s1-Straight forward data capture
2-Relatively uncomplicated studies, including CTimps (Clinical Trial of an Investigational
Medicinal Product)
3-Allows for easy implementation of features such as data queries and double data entry
Data can be extracted for analysis and can be downloaded in various formats including Excel
and SPSS (Statistical Package for the Social Sciences)
4-Easy to set up with even with basic IT skills as the Case Report Forms are built using Excel
spreadsheets
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26. • To compare the efficiency of different softwares on the basis of literature survey and
data collection based on various parameters
Methods used in data collection are based on questionnaire prepared for survey.
 Questionnaire (feed back form)
 Telephonic survey
 Through web sites : humlog site
: Linked in
Outcome of survey
The use of Clinical Data Management Systems (CDMS) has become essential in clinical trials to
handle the increasing amount of data that must be collected and analyzed. With a CDMS trial
data are captured at investigator sites with "electronic Case Report Forms". So there are
different –different software used by various experts of CDM based on cost ,efficacy etc or as
per company norms.
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27. RESULTS
The data was collected from 50 different people of CDM background. The following Results
were found.
On the basis of Popularity: Out of 50, 19 were using Oracle Clinica, being the most popular
software for CDM, second popular being Rave (11) and Inform (9) and least popular was Open
Clinica (4).
Fig. 3: Comparison on the basis of their popularity
2. On the basis of Cost: The costliest software is Oracle Clinica, its cost is in Billions.
Whereas the other softwares are relatively cheap, Rave and Inform are available in Millions.
Open clinica is free of cost.
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28. Fig 4 Comparison on the basis of cost
3. On the Basis of Speed: The Oracle Clinica is the fastest software among the others
Fig 5: on the basis of speed
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29. 4. On the Basis of User-Friendly: Oracle Clinica is most user-friendly as it is easy understood and easy to
use. Whereas others are not.
Fig 6 : On the basis of user friendly
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30. CONCLUSION
The most efficient software used in clinical Trial was found to be Oracle Clinica. Approx. 40%
of companies are using this software because it is User- friendly and very fast in speed.
Moreover, this software is mostly used because of its high security and accuracy.
Because of all these advantages, this software is High in Cost in comparison to other softwares
like Open Clinica which is Free of cost but being very slow and insecure.
Overall Oracle Clinica is most efficient software in Clinical Data Management.
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31. REFERENCES
1. Gerritsen MG, Sartorius OE, vd Veen FM, Meester GT. Data management in multi-center
clinical trials and the role of a nation-wide computer network. A 5 year evaluation. Proc Annu
Symp Comput Appl Med Care.1993:659–62.
2. Lu Z, Su J. Clinical data management: Current status, challenges, and future directions from
industry perspectives. Open Access J Clin Trials. 2010;2:93–105.
3. CFR - Code of Federal Regulations Title 21 [Internet] Maryland: Food and Drug
Administration. [Updated 2010 Apr 4; Cited 2011 Mar 1]. Available from:4. Study Data
Tabulation Model [Internet] Texas: Clinical Data Interchange Standards Consortium. c2011.
[Updated 2007 Jul; Cited 2011 Mar 1].
5. CDASH [Internet] Texas: Clinical Data Interchange Standards Consortium. c2011. [Updated
2011 Jan; Cited 2011 Mar 1].
6. Fegan GW, Lang TA. PLoS Med. 2008;5:e6.
7. Kuchinke W, Ohmann C, Yang Q, Salas N, Lauritsen J, Gueyffier F, et al. Heterogeneity
prevails: The state of clinical trial data management in Europe - results of a survey of ECRIN
centres. Trials. 2010;11:79.
8. Cummings J, Masten J. Customized dual data entry for computerized data analysis. Qual
Assur. 1994;3:300–3.
9. Reynolds-Haertle RA, McBride R. Single vs. double data entry in CAST. Control Clin Trials.
1992;13:487–94.
10. Ottevanger PB, Therasse P, van de Velde C, Bernier J, van Krieken H, Grol R, et al. Quality
assurance in clinical trials. Crit Rev Oncol Hematol. 2003;47:213–35.
11. Haux R, Knaup P, Leiner F. On educating about medical data management - the other side
of the electronic health record. Methods Inf Med. 2007;46:74–9.
12Design and implementation of a clinical data management system R.A. Greenes, A.N.
Pappalardo, C.W. Marble, G.Octo Barnett
13 Weintraub W.S., McKay C.R., Riner R.N.; The American College of Cardiology National
Database. progress and challenges. American College of Cardiology Database Committee. J Am
Coll Cardiol. 29 1997:459-465.
14 Rogers W.J., Canto J.G., Lambrew C.T.; Temporal trends in the treatment of over 1.5
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32. million patients with myocardial infarction in the US from 1990 through 1999. the National
Registry of Myocardial Infarction 1, 2 and 3. J Am Coll Cardiol. 36 2000:2056-2063.
15 Granger C.B.; Strategies of patient care in acute coronary syndromes. rationale for the
Global Registry of Acute Coronary Events (GRACE) registry. Am J Cardiol. 86 2000:4M-9M.
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33. Annexure I
FEEDBACK FORM
Please fill the respective fields for Clinical Data Management survey based project.
1. Name of Individual: ________________
2. Name of Organization: ________________
3. Name of Software: ________________
4. Version No. : ________________
5. Speed:
a) Very Fast
b) Fast
c) Slow
6. Reliability:
a) More
b) Less
c) Intermediate
7. Software mode of data entry
a) EDC only
b) Paper only
c) EDC and Paper both
8. Software Cost in
a) Billion
b) Million
c) Free
9. 21 CFR part 11 compliant software
a) Yes
b) No
c) Don’t Know
10. Software installation takes
a) Minutes
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34. b) Hours
c) Weeks
11. User friendly
a) More
b) Less
c) Moderate
12. Validated System
a) Yes
b) No
c) Don’t know
13. Chance of Error in software
a) Very Less
b) Less
c) Moderate
14. Does your software allow for multiple technologies including internet and paper
based?
a) Yes
b) No
c) Don’t know
15. How are queries tracked?
a) System tracking
b) Cognos report
c) Open query report
16. Does your software provide true record level locking?
a) Yes
b) No
c) Don’t know
17. User documentation
a) IP, PQ, OQ
b) Operational Qualification
c) DEI
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35. 18. Need Specialised Hardware
a) Yes
b) No
c) Probably
19. CRF filling time
a) < 1 minute
b) 1-2 minute
c) < 5 minute
20. How secure your software is?
a) More Secured
b) Medium Secured
c) Less Secured
21. How accurate your software is?
a) More accurate
b) Medium accurate
c) Less accurate
22. How much satisfied performance does your software provide?
a) Highly Satisfied
b) Moderately Satisfied
c) Less Satisfied
23. Which software would you like to switch?
a) Medidata Rave
b) Inform
c) Oracle Clinical
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