Data management in diabetes clinical trials: a qualitative study

Abstract

Background Clinical trials play an important role in expanding the knowledge of diabetes prevention, diagnosis, and treatment, and data management is one of the main issues in clinical trials. Lack of appropriate planning for data management in clinical trials may negatively influence achieving the desired results. The aim of this study was to explore data management processes in diabetes clinical trials in three research institutes in Iran. Method This was a qualitative study conducted in 2019. In this study, data were collected through in-depth semi-structured interviews with 16 researchers in three endocrinology and metabolism research institutes. To analyze data, the method of thematic analysis was used. Results The five themes that emerged from data analysis included (1) clinical trial data collection, (2) technologies used in data management, (3) data security and confidentiality management, (4) data quality management, and (5) data management standards. In general, the findings indicated that no clear and standard process was used for data management in diabetes clinical trials, and each research center executed its own methods and processes. Conclusion According to the results, the common methods of data management in diabetes clinical trials included a set of paper-based processes. It seems that using information technology can help facilitate data management processes in a variety of clinical trials, including diabetes clinical trials.

Considerations for establishing and maintaining international research collaboration: the example of chemotherapy-induced peripheral neurotoxicity (CIPN)—a white paper

“…The most important tasks in the clinical data management process are designing and annotating CRFs, creating the database, entering and validating the data, managing inconsistencies and resolving data disputes, medical coding, data extraction, database locking, documenting data management processes, and providing data security [ 34 ]. The most important step to ensure reliable and high-quality data is validation of the data immediately after it is collected by means of database queries and record screening by an expert reviewer to detect missing data points, data entry errors, and data inconsistencies.…”

Data management system for diabetes clinical trials: a pre-post evaluation study

“…Data management of clinical trials is a complex process that can be facilitated by using information and communication technologies (ICT) [35]. Clinical data management systems are technologies that can play an effective role in clinical data management, especially in multicenter clinical trials [32].…”

“…Clinical data management systems are technologies that can play an effective role in clinical data management, especially in multicenter clinical trials [32]. These systems support various aspects of data management, reduce financial and manpower costs, and facilitate data collection and management by eliminating manual processes and reducing workload [32,35,36]. However, many systems have not been properly evaluated in terms of quality, usability, and impact [21,[37][38][39][40][41][42].…”

A data management plan for the NESHIE observational study

“…Data management is important in any biomedical research project and facilitates the generation of high-quality and reliable data ( Nourani et al, 2022 ). Broadly, a data management plan (DMP) may have the following benefits ( Fadlelmola et al, 2021 ): 1) it protects the research participants and the project team; 2) it allows compliance with local data protection policies and legislation; 3) it maintains FAIR content; 4) it enables research that is transparent; and 5) it allows compliance with funder requirements.…”

“…Nevertheless, the data management process not only consists of creating study-associated documents such as data sheets or case report forms (CRFs) and consent forms, but also involves training of the research team, creating databases, capturing and validating data, managing data discrepancies, resolving data disagreements, describing the processes of data coding and extraction, access control, recording the data management process, and providing security throughout the duration of a research project ( Nourani et al, 2022 ). When working with databases, electronic data management systems require sufficient hardware, software, communication technologies, policies/guidelines for data collection, quality control of data, and security in order to be operational ( Nourani et al, 2022 ).…”