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.

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.

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 Clinical Trial Data Management in Environmental Health Tailored for an African Setting

2020 ◽  
Vol 17 (2) ◽  
pp. 402
Author(s):  
Patricia Nicole Albers ◽  
Caradee Yael Wright
Keyword(s):  
Public Health ◽  
Clinical Trial ◽  
Data Management ◽  
Environmental Health ◽  
Meteorological Data ◽  
Clinical Trial Data ◽  
Patient Data ◽  
Trial Data ◽  
Management Tool

Clinical trial data management tools are widely available—some free to access and others relatively expensive, particularly for low- and middle-income countries. Such tools also do not always permit adaptation for local conditions nor include options to capture environmental and meteorological data. In the context of climate change and pressing environmental health threats, more studies that aim to assess the impacts of environmental change on public health are being carried out. Here, using freely available software, we tailor-made a clinical trial data management tool that managed all aspects of an intervention-based clinical trial to assess the impact of personal solar ultraviolet radiation exposure on vaccine effectiveness. Data captured and associated procedures included patient data, scheduling, reporting, analysis and data management. Moreover, patient enrolment, recruitment, follow-up and decision-making in response to patient data were managed. Given the multidisciplinary study approach, the tool also managed all environmental and meteorological data for the rural African study site. Application of the tool ensured efficient communication between rural sites, a relatively high overall participant response rate (87%) and minimal loss to follow-up. This study suggests that it is possible to tailor-make a clinical trial data management tool for environmental and public health studies.

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