scholarly journals Research Development Using REDCap Software

2021 ◽  
Vol 27 (4) ◽  
pp. 341-349
Author(s):  
Klauss Kleydmann Sabino Garcia ◽  
Amanda Amaral Abrahão
Keyword(s):  
Clinical Research ◽  
Data Storage ◽  
Web Application ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Research Development ◽  
Electronic Data ◽  
Survey Instruments ◽  
Conducting Research ◽  
Audit Trails

Objectives: High-quality clinical research is dependent on adequate design, methodology, and data collection. The utilization of electronic data capture (EDC) systems is recommended to optimize research data through proper management. This paper’s objective is to present the procedures of REDCap (Research Electronic Data Capture), which supports research development, and to promote the utilization of this software among the scientific community.Methods: REDCap’s web application version 10.4.1 released on 2021 (Vanderbilt University) is an EDC system suitable for clinical research development. This paper describes how to join the REDCap consortium and presents how to develop survey instruments and use them to collect and analyze data.Results: Since REDCap is a web application that stimulates knowledge-sharing among the scientific community, its development is not finished and it is constantly receiving updates to improve the system. REDCap’s tools provide access control, audit trails, and data security to the research team.Conclusions: REDCap is a web application that can facilitate clinical research development, mainly in health fields, and reduce the costs of conducting research. Its tools allow researchers to make the best use of EDC components, such as data storage.

scholarly journals An Electronic Data Capture Framework (ConnEDCt) for Global and Public Health Research: Design and Implementation

10.2196/18580 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e18580 ◽  
Author(s):  
Caleb J Ruth ◽  
Samantha Lee Huey ◽  
Jesse T Krisher ◽  
Amy Fothergill ◽  
Bryan M Gannon ◽  
...  
Keyword(s):  
Clinical Research ◽  
Data Storage ◽  
Data Entry ◽  
Public Health Research ◽  
Ease Of Use ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Cross Sectional ◽  
Clinical Surveillance

Background When we were unable to identify an electronic data capture (EDC) package that supported our requirements for clinical research in resource-limited regions, we set out to build our own reusable EDC framework. We needed to capture data when offline, synchronize data on demand, and enforce strict eligibility requirements and complex longitudinal protocols. Based on previous experience, the geographical areas in which we conduct our research often have unreliable, slow internet access that would make web-based EDC platforms impractical. We were unwilling to fall back on paper-based data capture as we wanted other benefits of EDC. Therefore, we decided to build our own reusable software platform. In this paper, we describe our customizable EDC framework and highlight how we have used it in our ongoing surveillance programs, clinic-based cross-sectional studies, and randomized controlled trials (RCTs) in various settings in India and Ecuador. Objective This paper describes the creation of a mobile framework to support complex clinical research protocols in a variety of settings including clinical, surveillance, and RCTs. Methods We developed ConnEDCt, a mobile EDC framework for iOS devices and personal computers, using Claris FileMaker software for electronic data capture and data storage. Results ConnEDCt was tested in the field in our clinical, surveillance, and clinical trial research contexts in India and Ecuador and continuously refined for ease of use and optimization, including specific user roles; simultaneous synchronization across multiple locations; complex randomization schemes and informed consent processes; and collecting diverse types of data (laboratory, growth measurements, sociodemographic, health history, dietary recall and feeding practices, environmental exposures, and biological specimen collection). Conclusions ConnEDCt is customizable, with regulatory-compliant security, data synchronization, and other useful features for data collection in a variety of settings and study designs. Furthermore, ConnEDCt is user friendly and lowers the risks for errors in data entry because of real time error checking and protocol enforcement.

scholarly journals An Electronic Data Capture Framework (ConnEDCt) for Global and Public Health Research: Design and Implementation (Preprint)

2020 ◽  
Author(s):  
Caleb J Ruth ◽  
Samantha Lee Huey ◽  
Jesse T Krisher ◽  
Amy Fothergill ◽  
Bryan M Gannon ◽  
...  
Keyword(s):  
Clinical Research ◽  
Data Storage ◽  
Data Entry ◽  
Public Health Research ◽  
Ease Of Use ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Cross Sectional ◽  
Clinical Surveillance

BACKGROUND When we were unable to identify an electronic data capture (EDC) package that supported our requirements for clinical research in resource-limited regions, we set out to build our own reusable EDC framework. We needed to capture data when offline, synchronize data on demand, and enforce strict eligibility requirements and complex longitudinal protocols. Based on previous experience, the geographical areas in which we conduct our research often have unreliable, slow internet access that would make web-based EDC platforms impractical. We were unwilling to fall back on paper-based data capture as we wanted other benefits of EDC. Therefore, we decided to build our own reusable software platform. In this paper, we describe our customizable EDC framework and highlight how we have used it in our ongoing surveillance programs, clinic-based cross-sectional studies, and randomized controlled trials (RCTs) in various settings in India and Ecuador. OBJECTIVE This paper describes the creation of a mobile framework to support complex clinical research protocols in a variety of settings including clinical, surveillance, and RCTs. METHODS We developed ConnEDCt, a mobile EDC framework for iOS devices and personal computers, using Claris FileMaker software for electronic data capture and data storage. RESULTS ConnEDCt was tested in the field in our clinical, surveillance, and clinical trial research contexts in India and Ecuador and continuously refined for ease of use and optimization, including specific user roles; simultaneous synchronization across multiple locations; complex randomization schemes and informed consent processes; and collecting diverse types of data (laboratory, growth measurements, sociodemographic, health history, dietary recall and feeding practices, environmental exposures, and biological specimen collection). CONCLUSIONS ConnEDCt is customizable, with regulatory-compliant security, data synchronization, and other useful features for data collection in a variety of settings and study designs. Furthermore, ConnEDCt is user friendly and lowers the risks for errors in data entry because of real time error checking and protocol enforcement.

Possible Combinations of Electronic Data Capture and Randomization Systems

2014 ◽  
Vol 53 (03) ◽  
pp. 202-207 ◽  
Author(s):  
M. Haag ◽  
L. R. Pilz ◽  
D. Schrimpf
Keyword(s):  
Open Source ◽  
Data Storage ◽  
Data Entry ◽  
Data Capture ◽  
Electronic Systems ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Training Costs ◽  
Pros And Cons ◽  
General Data

SummaryBackground: Clinical trials (CT) are in a wider sense experiments to prove and establish clinical benefit of treatments. Nowadays electronic data capture systems (EDCS) are used more often bringing a better data management and higher data quality into clinical practice. Also electronic systems for the randomization are used to assign the patients to the treatments.Objectives: If the mentioned randomization system (RS) and EDCS are used, possibly identical data are collected in both, especially by stratified randomization. This separated data storage may lead to data inconsistency and in general data samples have to be aligned. The article discusses solutions to combine RS and EDCS. In detail one approach is realized and introduced.Methods: Different possible settings of combination of EDCS and RS are determined and the pros and cons for each solution are worked out. For the combination of two independent applications the necessary interfaces for the communication are defined. Thereby, existing standards are considered. An example realization is implemented with the help of open-source applications and state-of-the-art software development procedures.Results: Three possibilities of separate usage or combination of EDCS and RS are pre -sented and assessed: i) the complete independent usage of both systems; ii) realization of one system with both functions; and iii) two separate systems, which communicate via defined interfaces. In addition a realization of our preferred approach, the combination of both systems, is introduced using the open source tools RANDI2 and Open-Clinica.Conclusion: The advantage of a flexible independent development of EDCS and RS is shown based on the fact that these tool are very different featured. In our opinion the combination of both systems via defined interfaces fulfills the requirements of randomization and electronic data capture and is feasible in practice. In addition, the use of such a setting can reduce the training costs and the error-prone duplicated data entry.

scholarly journals Assessment of the Chad guinea worm surveillance information system: A pivotal foundation for eradication

2021 ◽  
Vol 15 (8) ◽  
pp. e0009675
Author(s):  
Saugat Karki ◽  
Adam Weiss ◽  
Jina Dcruz ◽  
Dorothy Hunt ◽  
Brandon Haigood ◽  
...  
Keyword(s):  
Information System ◽  
Data Management ◽  
Data Storage ◽  
Management Practices ◽  
Information Value ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Guinea Worm ◽  
Electronic Data Capture System

Background In the absence of a vaccine or pharmacological treatment, prevention and control of Guinea worm disease is dependent on timely identification and containment of cases to interrupt transmission. The Chad Guinea Worm Eradication Program (CGWEP) surveillance system detects and monitors Guinea worm disease in both humans and animals. Although Guinea worm cases in humans has declined, the discovery of canine infections in dogs in Chad has posed a significant challenge to eradication efforts. A foundational information system that supports the surveillance activities with modern data management practices is needed to support continued program efficacy. Methods We sought to assess the current CGWEP surveillance and information system to identify gaps and redundancies and propose system improvements. We reviewed documentation, consulted with subject matter experts and stakeholders, inventoried datasets to map data elements and information flow, and mapped data management processes. We used the Information Value Cycle (IVC) and Data-Information System-Context (DISC) frameworks to help understand the information generated and identify gaps. Results Findings from this study identified areas for improvement, including the need for consolidation of forms that capture the same demographic variables, which could be accomplished with an electronic data capture system. Further, the mental models (conceptual frameworks) IVC and DISC highlighted the need for more detailed, standardized workflows specifically related to information management. Conclusions Based on these findings, we proposed a four-phased roadmap for centralizing data systems and transitioning to an electronic data capture system. These included: development of a data governance plan, transition to electronic data entry and centralized data storage, transition to a relational database, and cloud-based integration. The method and outcome of this assessment could be used by other neglected tropical disease programs looking to transition to modern electronic data capture systems.

scholarly journals Use of research electronic data capture (REDCap) in a COVID-19 randomized controlled trial: a practical example

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sina Kianersi ◽  
Maya Luetke ◽  
Christina Ludema ◽  
Alexander Valenzuela ◽  
Molly Rosenberg
Keyword(s):  
Data Collection ◽  
Web Application ◽  
Controlled Trial ◽  
Antibody Test ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Protective Behavior ◽  
Electronic Data ◽  
Randomized Controlled ◽  
Longitudinal Data Collection

Abstract Background Randomized controlled trials (RCT) are considered the ideal design for evaluating the efficacy of interventions. However, conducting a successful RCT has technological and logistical challenges. Defects in randomization processes (e.g., allocation sequence concealment) and flawed masking could bias an RCT’s findings. Moreover, investigators need to address other logistics common to all study designs, such as study invitations, eligibility screening, consenting procedure, and data confidentiality protocols. Research Electronic Data Capture (REDCap) is a secure, browser-based web application widely used by researchers for survey data collection. REDCap offers unique features that can be used to conduct rigorous RCTs. Methods In September and November 2020, we conducted a parallel group RCT among Indiana University Bloomington (IUB) undergraduate students to understand if receiving the results of a SARS-CoV-2 antibody test changed the students’ self-reported protective behavior against coronavirus disease 2019 (COVID-19). In the current report, we discuss how we used REDCap to conduct the different components of this RCT. We further share our REDCap project XML file and instructional videos that investigators can use when designing and conducting their RCTs. Results We reported on the different features that REDCap offers to complete various parts of a large RCT, including sending study invitations and recruitment, eligibility screening, consenting procedures, lab visit appointment and reminders, data collection and confidentiality, randomization, blinding of treatment arm assignment, returning test results, and follow-up surveys. Conclusions REDCap offers powerful tools for longitudinal data collection and conduct of rigorous and successful RCTs. Investigators can make use of this electronic data capturing system to successfully complete their RCTs. Trial registration The RCT was prospectively (before completing data collection) registered at ClinicalTrials.gov; registration number: NCT04620798, date of registration: November 9, 2020.

scholarly journals DICOM for Clinical Research: PACS-Integrated Electronic Data Capture in Multi-Center Trials

2015 ◽  
Vol 28 (5) ◽  
pp. 558-566 ◽  
Author(s):  
Daniel Haak ◽  
Charles-E. Page ◽  
Sebastian Reinartz ◽  
Thilo Krüger ◽  
Thomas M. Deserno
Keyword(s):  
Clinical Research ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data

scholarly journals The experience of the clinical trial center at Alessandria Hospital regarding the activation of COVID-19 clinical trials during the pandemic

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Annalisa Roveta ◽  
Fabio Giacchero ◽  
Carolina Pelazza ◽  
Serena Penpa ◽  
Costanza Massarino ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Data Collection ◽  
Clinical Research ◽  
Fast Track ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Web Based ◽  
Taking Charge

Objective: The aim is to evaluate the speed in the activation of Covid-19 clinical trials at SS. Antonio e Biagio e Cesare Arrigo Hospital of Alessandria during the pandemic. Methods: Data collection related to the activation and the conduction of clinical trials was managed using a database created through a web-based platform REDCap (Research Electronic Data Capture). Results: 32 studies were activated in the period between March 23 and July 31, 2020. An average time of 14 days elapsed between taking charge of the request and the issuance of the authorization act. Conclusions: During the emergency it was possible to activate the trials quickly thanks to fast-track procedures, optimizing COVID-19 clinical research.

Future Developments of Medical Informatics from the Viewpoint of Networked Clinical Research

2009 ◽  
Vol 48 (01) ◽  
pp. 45-54 ◽  
Author(s):  
W. Kuchinke ◽  
C. Ohmann
Keyword(s):  
Clinical Research ◽  
Electronic Health Record ◽  
Open Source ◽  
Medical Informatics ◽  
Data Capture ◽  
Health Record ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Future Developments ◽  
Electronic Health

Summary Objectives: To be prepared for future developments, such as enabling support of rapid innovation transfer and personalized medicine concepts, interoperability of basic research, clinical research and medical care is essential. It is the objective of our paper to give an overview of developments, indicate problem areas and to specify future requirements. Methods: In this paper recent and ongoing large-scaled activities related to interoper-ability and integration of networked clinical research are described and evaluated. The following main topics are covered: necessity for general IT-conception, open source/open community approach, acceptance of eSource in clinical research, interoperability of the electronic health record and electronic data capture and harmonization and bridging of standards for technical and semantic inter-operability. Results: National infrastructures and programmes have been set up to provide general IT-conceptions to guide planning and development of software tools (e.g. TMF, ca BIG, NIHR). The concept of open research described by transparency achieved through open access, open data, open communication and open source software is becoming more and more important in clinical research infrastructure development (e.g. ca BIG, ePCRN). Meanwhile visions and rules for using eSource in clinical research are available, with the potential to improve interoperability between the electronic health record and electronic data capture (e.g. CDISC e SDI, eClinical Forum/PhRMA EDC/eSource Taskforce). Several groups have formulated user requirements, use cases and technical frameworks to advance these issues (e.g. NHIN Slipstream-project, EHR/CR-project, IHE). In order to achieve technical and semantic interoperability, existing standards (e.g. CDISC) have to be harmonized and bridged. Major consortia have been formed to provide semantical inter-operability (e.g. HL7 RCRIM under joint leadership of HL7, CDISC and FDA, or BRIDG covering CDISC, HL7, FDA, NCI) and to provide core sets of data collection fields (CDASH). Conclusions: The essential tasks for medical informatics within the next ten years will now be the development and implementation of encompassing IT conceptions, strong support of the open community and open source approach, the acceptance of eSource in clinical research, the uncompromising continuity of standardization and bridging of technical standards and the widespread use of electronic health record systems.

scholarly journals Research Electronic Data Capture (REDCap) electronic Informed Consent Form (eICF) is compliant and feasible in a clinical research setting

2015 ◽  
Vol 2 (3) ◽  
pp. 51 ◽  
Author(s):  
Matthew J. Frelich ◽  
Matthew E. Bosler ◽  
Jon C. Gould
Keyword(s):  
Informed Consent ◽  
Clinical Research ◽  
Data Capture ◽  
Consent Form ◽  
Single Subject ◽  
Electronic Data Capture ◽  
Research Setting ◽  
Electronic Data ◽  
Informed Consent Form ◽  
Nasa Tlx

<p class="abstract"><strong>Background:</strong> Electronic consent for research has shown success in clinical trial models, but has not been rigorously evaluated as an alternative to conventional paper consent.  We sought to design a 21 CFR Part 11 compliant iPad-based electronic Informed Consent Form (eICF) with Research Electronic Data Capture (REDCap). As a secondary aim, we sought to compare subject workload between eICF and paper consent groups.</p><p class="abstract"><strong>Methods:</strong> This is a prospective, randomized study of subjects who completed an iPad-based eICF versus paper consent for research. The eICF was designed with REDCap and presented on an iPad. Subject workload was measured with the NASA Task Load Index (NASA-TLX) and subjective feedback in regards to consent process was collected.</p><p class="abstract"><strong>Results:</strong> A total of 116 subjects were screened for consent. Of which, 51 (44%) subjects provided informed consent and completed all study related procedures. Twenty-five (49%) eICF and 26 (51%) paper consents were completed.  The eICF group rated a significantly greater preference to use the eICF for future research studies (6.4±1.5) compared to the paper consent group (5.0±1.9), p&lt;0.01. There were no significant differences in NASA-TLX Weighted Scale or Total-TLX Scores between groups. One error resulted in the eICF group due to an inadvertent submission by a single subject.</p><p class="abstract"><strong>Conclusion:</strong> In summary, we have demonstrated that an iPad-based eICF designed with REDCap is both 21 CFR Part 11 compliant and feasible in the clinical research setting.  The eICF does not appear to be more technically difficult or demanding than conventional paper consent.</p>

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