Clinical Trial Data Management Software: A Review of the Technical Features

2019 ◽  
Vol 14 (3) ◽  
pp. 160-172 ◽  
Author(s):  
Aynaz Nourani ◽  
Haleh Ayatollahi ◽  
Masoud Solaymani Dodaran
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems ◽  
Technical Features

Background:Data management is an important, complex and multidimensional process in clinical trials. The execution of this process is very difficult and expensive without the use of information technology. A clinical data management system is software that is vastly used for managing the data generated in clinical trials. The objective of this study was to review the technical features of clinical trial data management systems.Methods:Related articles were identified by searching databases, such as Web of Science, Scopus, Science Direct, ProQuest, Ovid and PubMed. All of the research papers related to clinical data management systems which were published between 2007 and 2017 (n=19) were included in the study.Results:Most of the clinical data management systems were web-based systems developed based on the needs of a specific clinical trial in the shortest possible time. The SQL Server and MySQL databases were used in the development of the systems. These systems did not fully support the process of clinical data management. In addition, most of the systems lacked flexibility and extensibility for system development.Conclusion:It seems that most of the systems used in the research centers were weak in terms of supporting the process of data management and managing clinical trial's workflow. Therefore, more attention should be paid to design a more complete, usable, and high quality data management system for clinical trials. More studies are suggested to identify the features of the successful systems used in clinical trials.

A Review of Clinical Data Management Systems Used in Clinical Trials

2019 ◽  
Vol 14 (1) ◽  
pp. 10-23 ◽  
Author(s):  
Aynaz Nourani ◽  
Haleh Ayatollahi ◽  
Masoud Solaymani Dodaran
Keyword(s):  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Data Management System ◽  
Management Systems ◽  
Management Process ◽  
Data Management Systems ◽  
Ovid Medline ◽  
Research Findings ◽  
Accuracy Of Results

Background:A clinical data management system is a software supporting the data management process in clinical trials. In this system, the effective support of clinical data management dimensions leads to the increased accuracy of results and prevention of diversion in clinical trials. The aim of this review article was to investigate the dimensions of data management in clinical data management systems.Methods:This study was conducted in 2017. The used databases included Web of Science, Scopus, Science Direct, ProQuest, Ovid Medline and PubMed. The search was conducted over a period of 10 years from 2007 to 2017. The initial number of studies was 101 reaching 19 in the final stage. The final studies were described and compared in terms of the year, country and dimensions of the clinical data management process in clinical trials.Results:The research findings indicated that none of the systems completely supported the data management dimensions in clinical trials. Although these systems were developed for supporting the clinical data management process, they were similar to electronic data capture systems in many cases. The most significant dimensions of data management in such systems were data collection or entry, report, validation, and security maintenance.Conclusion:Seemingly, not sufficient attention has been paid to automate all dimensions of the clinical data management process in clinical trials. However, these systems could take positive steps towards changing the manual processes of clinical data management to electronic processes.

scholarly journals CLINICAL DATA MANAGEMENT IMPORTANCE IN CLINICAL RESEARCH

2016 ◽  
pp. 59
Author(s):  
Deepa Murugesan ◽  
Ranganath Banerjee ◽  
Gopal Ramesh Kumar
Keyword(s):  
Clinical Trial ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Drug Approval ◽  
Trial Data ◽  
Data Capture ◽  
Data Management System ◽  
Pharmaceutical Companies ◽  
Management Tools

<p>ABSTRACT<br />Over the last few decades, most of the pharmaceutical companies and research sponsors are facing a lot of challenges in clinical research for their<br />new drug approval. The sponsor research needs a high-quality data report for getting new drug approval from Food and Drug Administration for their<br />medical products. Clinical trial data are important for the drug and medical device development processing pharmaceutical companies to examine<br />and evaluate the efficacy and safety of the new medical product in human volunteers. The results of the clinical trial studies generate the most<br />valuable data and in recent years; there has been massive development in the field of clinical trials. A good clinical data management system reduces<br />the duration of the study and cost of drug development. Further a well-designed case report form (CRF) assists data collection and make facilitates<br />data management and statistical analysis. Nowadays, the electronic data capture (EDC) is very beneficial in data collection. EDC helps to speed up the<br />clinical trial process and reduces the duration, errors and make the work easy in the data management system. This article highlights the importance<br />of data management processes involved in the clinical trial and provides an overview of the clinical trial data management tools. The study concluded<br />that data management tools play a key role in the clinical trial and well-designed CRFs reduces the errors and save the time of the clinical trials and<br />facilitates the drug discovery and development.<br />Keywords: Pharmaceutical, Clinical trial, Clinical data management, Data capture.</p>

scholarly journals Data Validation and Verification Using Blockchain in a Clinical Trial for Breast Cancer: Regulatory Sandbox (Preprint)

2020 ◽  
Author(s):  
Tomonobu Hirano ◽  
Tomomitsu Motohashi ◽  
Kosuke Okumura ◽  
Kentaro Takajo ◽  
Taiyo Kuroki ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Research Process ◽  
Current Data ◽  
Data Management System ◽  
Blockchain Technology

BACKGROUND The integrity of data in a clinical trial is essential, but the current data management process is too complex and highly labor-intensive. As a result, clinical trials are prone to consuming a lot of budget and time, and there is a risk for human-induced error and data falsification. Blockchain technology has the potential to address some of these challenges. OBJECTIVE The aim of the study was to validate a system that enables the security of medical data in a clinical trial using blockchain technology. METHODS We have developed a blockchain-based data management system for clinical trials and tested the system through a clinical trial for breast cancer. The project was conducted to demonstrate clinical data management using blockchain technology under the regulatory sandbox enabled by the Japanese Cabinet Office. RESULTS We verified and validated the data in the clinical trial using the validation protocol and tested its resilience to data tampering. The robustness of the system was also proven by survival with zero downtime for clinical data registration during a Amazon Web Services disruption event in the Tokyo region on August 23, 2019. CONCLUSIONS We show that our system can improve clinical trial data management, enhance trust in the clinical research process, and ease regulator burden. The system will contribute to the sustainability of health care services through the optimization of cost for clinical trials.

82 Internet security in clinical trial data management systems

1997 ◽  
Vol 18 (3) ◽  
pp. S92 ◽  
Author(s):  
Jeffrey P. Martin ◽  
Patrick Beighley ◽  
David C. Hiriak ◽  
Eugene D. Spadafore ◽  
Kimberly C. Beringer
Keyword(s):  
Clinical Trial ◽  
Data Management ◽  
Clinical Trial Data ◽  
Internet Security ◽  
Trial Data ◽  
Management Systems ◽  
Data Management Systems

scholarly journals Data Validation and Verification Using Blockchain in a Clinical Trial for Breast Cancer: Regulatory Sandbox

10.2196/18938 ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. e18938
Author(s):  
Tomonobu Hirano ◽  
Tomomitsu Motohashi ◽  
Kosuke Okumura ◽  
Kentaro Takajo ◽  
Taiyo Kuroki ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Research Process ◽  
Current Data ◽  
Data Management System ◽  
Blockchain Technology

Background The integrity of data in a clinical trial is essential, but the current data management process is too complex and highly labor-intensive. As a result, clinical trials are prone to consuming a lot of budget and time, and there is a risk for human-induced error and data falsification. Blockchain technology has the potential to address some of these challenges. Objective The aim of the study was to validate a system that enables the security of medical data in a clinical trial using blockchain technology. Methods We have developed a blockchain-based data management system for clinical trials and tested the system through a clinical trial for breast cancer. The project was conducted to demonstrate clinical data management using blockchain technology under the regulatory sandbox enabled by the Japanese Cabinet Office. Results We verified and validated the data in the clinical trial using the validation protocol and tested its resilience to data tampering. The robustness of the system was also proven by survival with zero downtime for clinical data registration during a Amazon Web Services disruption event in the Tokyo region on August 23, 2019. Conclusions We show that our system can improve clinical trial data management, enhance trust in the clinical research process, and ease regulator burden. The system will contribute to the sustainability of health care services through the optimization of cost for clinical trials.

The development of CLINDATA, a clinical trial data management system

1981 ◽  
Vol 3 (3) ◽  
pp. 129-136 ◽  
Author(s):  
T. Ravenscroft ◽  
D.E. Smith
Keyword(s):  
Clinical Trial ◽  
Data Management ◽  
Management System ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Data Management System ◽  
Long Term Follow Up ◽  
On Line

The paper describes the design and implementation of a clinical trial data management system at the Wellcome Research Laboratories. Based on an IBM 3031 computer, the system provides the capability for on-line data input, search ing and comprehensive data analysis. The database also performs an adverse reaction reporting function and provides for long term follow-up of patients.

scholarly journals The MSOAC approach to developing performance outcomes to measure and monitor multiple sclerosis disability

2017 ◽  
Vol 24 (11) ◽  
pp. 1469-1484 ◽  
Author(s):  
Nicholas G LaRocca ◽  
Lynn D Hudson ◽  
Richard Rudick ◽  
Dagmar Amtmann ◽  
Laura Balcer ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Clinical Data ◽  
Functional Performance ◽  
Clinical Trial Data ◽  
Performance Outcomes ◽  
Trial Data ◽  
Regulatory Agencies ◽  
Data Interchange

Background: The Multiple Sclerosis Outcome Assessments Consortium (MSOAC) was formed by the National MS Society to develop improved measures of multiple sclerosis (MS)-related disability. Objectives: (1) To assess the current literature and available data on functional performance outcome measures (PerfOs) and (2) to determine suitability of using PerfOs to quantify MS disability in MS clinical trials. Methods: (1) Identify disability dimensions common in MS; (2) conduct a comprehensive literature review of measures for those dimensions; (3) develop an MS Clinical Data Interchange Standards Consortium (CDISC) data standard; (4) create a database of standardized, pooled clinical trial data; (5) analyze the pooled data to assess psychometric properties of candidate measures; and (6) work with regulatory agencies to use the measures as primary or secondary outcomes in MS clinical trials. Conclusion: Considerable data exist supporting measures of the functional domains ambulation, manual dexterity, vision, and cognition. A CDISC standard for MS ( http://www.cdisc.org/therapeutic#MS ) was published, allowing pooling of clinical trial data. MSOAC member organizations contributed clinical data from 16 trials, including 14,370 subjects. Data from placebo-arm subjects are available to qualified researchers. This integrated, standardized dataset is being analyzed to support qualification of disability endpoints by regulatory agencies.

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