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 SRIVASTAVAPREETI DAHIYA

KOMAL BHANOTPOOJA MISHRA

Under the supervision of Institutional Guide Mrs. Sunita K. and Mrs. Rinku Dahiya

atApollo Hospitals Educational and Research Foundation(AHERF)

New Delhi, affiliated to

Anna University, Chennai

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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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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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Serial no Content Page no1 Abbreviations 42 Abstract 53 Introduction 6-134 Aims and Objective 145 Review of Literature 15-236 Material and methodology 24-257 Results 26-28

8 conclusion 29

9 References 30 -31

10 Annexure 32-34

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Abbreviations

CDM Clinical data managementCRF Case report form

e- CRF Electronic case report formDCF Data clariformation formDVP Data validation process

SCDM Society for clinical data managementGCDMP Good clinical data management practicesCDASH Clinical Data Acquisition Standards HarmonizationCDISC Clinical Data Interchange Standards Consortium

SDTMIG Study Data Tabulation Model Implementation Guide for Human Clinical Trials

EDC Electronic data captureDMP Data management planCFR Code of Federal RegulationCRA Clinical research associateSEC Self-evident correction

MedRA Medical Dictionary for Regulatory ActivitiesWHO-DDE World Health Organization–Drug Dictionary EnhancedWHO-ART World Health Organization-adverse reaction terminology

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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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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. These CDM

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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, two important ones are the Study Data Tabulation Model Implementation Guide for Human Clinical Trials (SDTMIG) and the Clinical Data Acquisition Standards Harmonization (CDASH)

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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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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 that double data entry ensures better consistency with paper CRF as denoted by a lesser error rate.[9]

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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)

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

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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 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.

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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 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

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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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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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LITERATURE REVIEW

Review of literatureIncreasing 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 CDMA 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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•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]

MethodsThe 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 WorkflowFig. 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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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 interfaceThe 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 specified

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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-side application menu of each project.

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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 ClinicaThe objective of OpenClinica is to harness the power of community thinking to help define a common technology that can work effectively in diverse research settingsOpenClinica 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

automated and manual data interchange

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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. 

As a web-based software solution, all users need is a PC, browser, and an Internet connection.

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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.

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

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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

ECG

CT Scans & MRIs

Topographies & Radiographics

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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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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 entryData 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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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 surveyThe 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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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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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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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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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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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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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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Annexure I

FEEDBACK FORMPlease 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 Fastb) Fast

c) Slow

6. Reliability:a) Moreb) Lessc) Intermediate

7. Software mode of data entrya) EDC onlyb) Paper onlyc) EDC and Paper both

8. Software Cost ina) Billionb) Millionc) Free

9. 21 CFR part 11 compliant softwarea) Yesb) Noc) Don’t Know

10. Software installation takesa) Minutes

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b) Hoursc) Weeks

11. User friendlya) Moreb) Lessc) 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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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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