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.

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.

On Educating about Medical Data Management

2007 ◽  
Vol 46 (01) ◽  
pp. 74-79 ◽  
Author(s):  
P. Knaup ◽  
F. Leiner ◽  
R. Haux
Keyword(s):  
Data Management ◽  
Clinical Data ◽  
Medical Data ◽  
Patient Data ◽  
Management Systems ◽  
Multiple Use ◽  
Data Management Systems ◽  
Medical Coding ◽  
Medical Data Management ◽  
Coding Systems

Summary Objectives: To summarize background, challenges, objectives, and methods for the usability of patient data, in particular with respect to their multiple use, and to point out how to lecture medical data management. Methods: Analyzing the literature, providing an example based on Simpson’s paradox and summarizing research and education in the field of medical data management, respectively health information management (in German: Medizinische Dokumentation). Results: For the multiple use of patient data, three main categories of use can be identified: patientoriented (or casuistic) analysis, patient-group reporting, and analysis for clinical studies. A so-called documentation protocol, related to study plans in clinical trials, supports the multiple use of data from the electronic health record in order to obtain valid, interpretable results. Lectures on medical data management may contain modules on introduction, basic concepts of clinical data management and coding systems, important medical coding systems (e.g. ICD, SNOMED, TNM, UMLS), typical medical documentation systems (e.g. on patient records, clinical and epidemiological registers), utilization of clinical data management systems, planning of medical coding systems and of clinical data management systems, hospital information systems and the electronic patient record, and on data management in clinical studies. Conclusion: Usability, the ultimate goal of recording and managing patient data, requires, besides technical considerations, in addition appropriate methodology on medical data management, especially if data is intended to be used for multiple purposes, e.g. for patient care and quality management and clinical research. Medical data management should be taught in health and biomedical informatics programs.

scholarly journals A Coding-Free Software Framework for Developing Lightweight Web Data Management Systems

2020 ◽  
Vol 10 (3) ◽  
pp. 865
Author(s):  
Can Yang ◽  
Shiying Pan ◽  
Runmin Li ◽  
Yu Liu ◽  
Lizhang Peng
Keyword(s):  
Data Management ◽  
Management System ◽  
System Development ◽  
Data Management System ◽  
Management Systems ◽  
Web Data ◽  
Data Management Systems ◽  
Construction Methods ◽  
Free Construction ◽  
Web Data Management

Increasingly more enterprises are intending to deploy data management systems in the cloud. However, the complexity of software development significantly increases both time and learning costs of data management system development. In this paper, we investigate the coding-free construction of a data management system based on Software-as-a-Service (SaaS) architecture, in which a practical application platform and a set of construction methods are proposed. Specifically, by extracting the common features of data management systems, we design a universal web platform to quickly generate and publish customized system instances. Then, we propose a method to develop a lightweight data management system using a specific requirements table in a spreadsheet. The corresponding platform maps the requirements table into a system instance by parsing the table model and implementing the objective system in the running stage. Finally, we implement the proposed framework and deploy it on the web. The empirical results demonstrate the feasibility and availability of the coding-free method for developing lightweight web data management systems.

scholarly journals THESE MODELS NEED ENTERPRISE DATA MANAGEMENT!

10.28945/3651 ◽  
2017 ◽  
Vol 6 ◽  
pp. 01
Author(s):  
Jay Hoecker ◽  
Debbie Bernal ◽  
Alex Brito ◽  
Arda Ergonen ◽  
Richard Stiftinger
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Management System ◽  
Current Data ◽  
Scientific Models ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems ◽  
Technology Platform

The current data management systems for the life cycle of scientific models needed an upgrade. What technology platform offered the best option for an Enterprise Data Management system?

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