Evaluation of Data Entry Errors and Data Changes to an Electronic Data Capture Clinical Trial Database

PURPOSE New technology might pose problems for older patients with cancer. This study sought to understand how a trial in older patients with cancer (Alliance A171603) was successful in capturing electronic patient-reported data. METHODS Study personnel were invited via e-mail to participate in semistructured phone interviews, which were audio-recorded and qualitatively analyzed. RESULTS Twenty-four study personnel from the 10 sites were interviewed; three themes emerged. The first was that successful patient-reported electronic data capture shifted work toward patients and toward study personnel at the beginning of the study. One interviewee explained, “I mean it kind of lost all advantages…by being extremely laborious.” Study personnel described how they ensured electronic devices were charged, wireless internet access was up and running, and login codes were available. The second theme was related to the first and dealt with data filtering. Study personnel described high involvement in data gathering; for example, one interviewee described, “I answered on the iPad, whatever they said. They didn't even want to use it at all.” A third theme dealt with advantages of electronic data entry, such as prompt data availability at study completion. Surprisingly, some remarks described how electronic devices brought people together, “Some of the patients, you know, it just gave them a chance to kinda talk about, you know, what was going on.” CONCLUSION High rates of capture of patient-reported electronic data were viewed favorably but occurred in exchange for increased effort from patients and study personnel and in exchange for data that were not always patient-reported in the strictest sense.

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OS 26-03 MOBILE DEVICE BASED ELECTRONIC DATA CAPTURE SYSTEM IN PHARMACEUTICAL CLINICAL TRIAL

Journal of Hypertension ◽

10.1097/01.hjh.0000500558.64425.f9 ◽

2016 ◽

Vol 34 (Supplement 1) ◽

pp. e247

Author(s):

Jing Zhang ◽

Lei Sun ◽

Yu Liu ◽

Hongyi Wang ◽

Ningling Sun ◽

...

Keyword(s):

Clinical Trial ◽

Mobile Device ◽

Data Capture ◽

Electronic Data Capture ◽

Electronic Data ◽

Electronic Data Capture System

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Possible Combinations of Electronic Data Capture and Randomization Systems

Methods of Information in Medicine ◽

10.3414/me13-01-0074 ◽

2014 ◽

Vol 53 (03) ◽

pp. 202-207 ◽

Cited By ~ 7

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.

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

Journal of Medical Internet Research ◽

10.2196/18580 ◽

2020 ◽

Vol 22 (8) ◽

pp. e18580 ◽

Cited By ~ 1

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.

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Use of electronic data capture in a clinical trial on infant feeding

European Journal of Clinical Nutrition ◽

10.1038/ejcn.2012.141 ◽

2012 ◽

Vol 66 (12) ◽

pp. 1342-1343 ◽

Cited By ~ 2

Author(s):

I Pawellek ◽

T Richardsen ◽

D Oberle ◽

V Grote ◽

B Koletzko

Keyword(s):

Clinical Trial ◽

Infant Feeding ◽

Data Capture ◽

Electronic Data Capture ◽

Electronic Data

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

Working Paper of Public Health ◽

10.4081/wpph.2020.9248 ◽

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.

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P4-157: Adcs electronic data capture: Collaborative development and management of clinical trial databases

Alzheimer s & Dementia ◽

10.1016/j.jalz.2013.05.1548 ◽

2013 ◽

Vol 9 ◽

pp. P763-P764

Author(s):

Gustavo Jimenez-Maggiora ◽

Ronald Thomas ◽

Stefania Bruschi ◽

Hongmei Qiu ◽

Phuoc Hong ◽

...

Keyword(s):

Clinical Trial ◽

Data Capture ◽

Electronic Data Capture ◽

Electronic Data ◽

Collaborative Development ◽

Development And Management

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Capturing a Stata Dataset and Releasing it into REDcap

The Stata Journal Promoting communications on statistics and Stata ◽

10.1177/1536867x1701700108 ◽

2017 ◽

Vol 17 (1) ◽

pp. 130-138

Author(s):

Seth T. Lirette ◽

Samantha R. Seals ◽

Chad Blackshear ◽

Warren May

Keyword(s):

Data Collection ◽

Data Entry ◽

Data Capture ◽

Data Dictionary ◽

Electronic Data Capture ◽

Application Research ◽

Electronic Data ◽

Web Based ◽

Internet Connection ◽

Technology Advances

With technology advances, researchers can now capture data using web-based applications. One such application, Research Electronic Data Capture (REDCap), allows for data entry from any computer with an Internet connection. As the use of REDCap has increased in popularity, we have observed the need to easily create data dictionaries and data collection instruments for REDCap. The command presented in this article, redcapture, demonstrates one method to create a REDCap-ready data dictionary using a loaded Stata dataset, illustrated by examples of starting from an existing dataset or completely starting from scratch.

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