Use of the data system for field management of a clinical study conducted in Kolkata, India

Park, Ju Yeon; Kim, Deok Ryun; Haldar, Bisakha; Mallick, Aiyel Haque; Kim, Soon Ae; Dey, Ayan; Nandy, Ranjan K.; Paul, Dilip K.; Choudhury, Saugata; Sahoo, Shushama; Wierzba, Thomas F.; Sur, Dipika; Kanungo, Suman; Ali, Mohammad; Manna, Byomkesh

doi:10.1186/s13104-015-1767-7

Cited by 9 publications

(10 citation statements)

References 12 publications

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“…In terms of usability, most of the problems were related to the user interface design or users' misunderstandings of the terms used in the interface. Although, in many similar studies, the think-aloud method has not been used to evaluate the system prototype [ 23 , 28 , 35 , 40 ], we used this method to solve the usability issues and improve user satisfaction and system acceptance [ 59 – 61 ]. The think-aloud method is a reliable subjective measure of user experience.…”

Section: Discussionmentioning

confidence: 99%

A Clinical Data Management System for Diabetes Clinical Trials

Nourani

Ayatollahi

Solaymani‐Dodaran

2022

Journal of Healthcare Engineering

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Background. The use of novel medications and methods to prevent, diagnose, treat, and manage diabetes requires confirmation of safety and efficacy in a well-designed study prior to widespread adoption. Diabetes clinical trials are the studies that examine these issues. The aim of the present study was to develop a web-based system for data management in diabetes clinical trials. Methods. The present research was a mixed-methods study conducted in 2019. To identify the required data elements and functions to develop the system, 60 researchers completed a questionnaire. The designed system was evaluated using two methods. The usability of the system was initially evaluated by a group of researchers (n = 6) using the think-aloud method, and after system improvement, the system functions were evaluated by other researchers (n = 30) using a questionnaire. Results. The main data elements which were required to develop a case report form included “study data,” “participant’s personal data,” and “clinical data.” The functional requirements of the system were “managing the study,” “creating case report forms,” “data management,” “data quality control,” and “data security and confidentiality.” After using the system, researchers rated the system functions at a “good” level (6.3 ± 0.73) on a seven-point Likert scale. Conclusion. Given the complexity of the data management processes in diabetes clinical trials and the widespread use of information technologies in research, the use of clinical data management systems in diabetes clinical trials seems inevitable. The system developed in the current study can facilitate and improve the process of creating and managing case report forms as well as collecting data in diabetes clinical trials.

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Section: Discussionmentioning

confidence: 99%

A Clinical Data Management System for Diabetes Clinical Trials

Nourani

Ayatollahi

Solaymani‐Dodaran

2022

Journal of Healthcare Engineering

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“…Similarly, Ohmann et al highlighted the limitations of hardware and software technologies available in the research centers [ 28 ]. In other studies, poor communication infrastructure has been addressed as a problem for clinical trials data management in developing countries [ 15 , 18 ]. As conducting diabetes clinical trials is important and a large part of clinical trials is devoted to diabetes, it seems that developing a diabetes clinical data management system can be useful for the researchers to collect, store and disseminate data in a well-defined format [ 39 , 40 ].…”

Section: Discussionmentioning

confidence: 99%

“…Electronic clinical data management requires adequate hardware, software, and communication technologies as well as settings, data collection policies, and controlling data quality and security [ 15 , 16 ]. Although many strategies and interventions have been proposed to reduce mistakes in the process of clinical data collection and management, it seems that there are still many other factors that need to be taken into account.…”

Section: Introductionmentioning

confidence: 99%

“…Although many strategies and interventions have been proposed to reduce mistakes in the process of clinical data collection and management, it seems that there are still many other factors that need to be taken into account. Some strategies have only focused on developing and using standards, training the staff, monitoring the data, and designing intelligent case report forms [ 16 ], and numerous studies have addressed the strengths and weaknesses of research centers in conducting data management and their compliance with clinical trial standards [ 15 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

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Data management in diabetes clinical trials: a qualitative study

Nourani

Ayatollahi

Dodaran

2022

Trials

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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.

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“…The review included 20 articles (16%) that addressed various aspects of data management, including best practices, use of data standards, and integration of multi-media data. Practical guidance for data management addressed a variety of topics, including managing data in the field, improving management workflow, and using databases and registries to structure the data [61][62][63][64][65]. Additional guidance for practice covers using mobile technology for data collection, reusing data from electronic health records, and graphical methods to explore such data collections [66][67][68].…”

Section: Managing Study Datamentioning

confidence: 99%

Clinical Research Informatics: Supporting the Research Study Lifecycle

Johnson¹

2017

Yearb Med Inform

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SummaryObjectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years [2015][2016]. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the "research study lifecycle", with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.

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Use of the data system for field management of a clinical study conducted in Kolkata, India

Cited by 9 publications

References 12 publications

A Clinical Data Management System for Diabetes Clinical Trials

A Clinical Data Management System for Diabetes Clinical Trials

Data management in diabetes clinical trials: a qualitative study

Clinical Research Informatics: Supporting the Research Study Lifecycle

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