OpenEDC: an open-source, standard-compliant, and mobile electronic data capture system for medical research (Preprint)

2021 ◽  
Author(s):  
Leonard Greulich ◽  
Stefan Hegselmann ◽  
Martin Dugas
Keyword(s):  
Data Collection ◽  
Medical Research ◽  
Open Source ◽  
Clinical Data ◽  
Data Exchange ◽  
Data Capture ◽  
Digital Data ◽  
Quality Data ◽  
Electronic Data Capture ◽  
Electronic Data

BACKGROUND Medical research and machine learning for healthcare depend on high-quality data. Electronic data capture (EDC) systems are widely adopted for metadata-driven digital data collection. However, many systems use proprietary and incompatible formats that inhibit clinical data exchange and metadata reuse. In addition, configuration and financial requirements of typical EDC systems frequently prevent small-scale studies to profit from their eminent benefits. OBJECTIVE The goal was to develop and publish an open-source EDC system that addresses the aforementioned issues. We planned applicability of the system in a wide range of research projects. METHODS We conducted a literature-based requirements analysis to identify academic and regulatory demands towards digital data collection. After designing and implementing OpenEDC, we performed a usability evaluation to obtain feedback from users. RESULTS We identified 20 frequently stated requirements towards EDC. According to the ISO/IEC 25010 norm, we categorized the requirements into functional suitability, availability, compatibility, usability, and security. We developed OpenEDC based on the regulatory-compliant Clinical Data Interchange Standards Consortium Operational Data Model standard. Mobile device support enables the collection of patient-reported outcomes. OpenEDC is publicly available and released under the MIT open-source license. CONCLUSIONS Adopting an established standard without modifications supports metadata reuse and clinical data exchange but it limits item layouts. OpenEDC is a standalone web application that can be used without setup or configuration. This should foster compatibility of medical research and open science. OpenEDC is targeted at observational and translational research studies by clinician scientists.

scholarly journals Innovating to enhance clinical data management using non-commercial and open source solutions across a multi-center network supporting inpatient pediatric care and research in Kenya

2015 ◽  
Vol 23 (1) ◽  
pp. 184-192 ◽  
Author(s):  
Timothy Tuti ◽  
Michael Bitok ◽  
Chris Paton ◽  
Boniface Makone ◽  
Lucas Malla ◽  
...  
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Open Source ◽  
Clinical Data ◽  
Data Capture ◽  
Quality Data ◽  
Pediatric Care ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Report Generation

Abstract Objective To share approaches and innovations adopted to deliver a relatively inexpensive clinical data management (CDM) framework within a low-income setting that aims to deliver quality pediatric data useful for supporting research, strengthening the information culture and informing improvement efforts in local clinical practice. Materials and methods The authors implemented a CDM framework to support a Clinical Information Network (CIN) using Research Electronic Data Capture (REDCap), a noncommercial software solution designed for rapid development and deployment of electronic data capture tools. It was used for collection of standardized data from case records of multiple hospitals’ pediatric wards. R, an open-source statistical language, was used for data quality enhancement, analysis, and report generation for the hospitals. Results In the first year of CIN, the authors have developed innovative solutions to support the implementation of a secure, rapid pediatric data collection system spanning 14 hospital sites with stringent data quality checks. Data have been collated on over 37 000 admission episodes, with considerable improvement in clinical documentation of admissions observed. Using meta-programming techniques in R, coupled with branching logic, randomization, data lookup, and Application Programming Interface (API) features offered by REDCap, CDM tasks were configured and automated to ensure quality data was delivered for clinical improvement and research use. Conclusion A low-cost clinically focused but geographically dispersed quality CDM (Clinical Data Management) in a long-term, multi-site, and real world context can be achieved and sustained and challenges can be overcome through thoughtful design and implementation of open-source tools for handling data and supporting research.

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.

Validation of the Oral Survey-B System for Electronic Data Capture in National Oral Health Surveys

2016 ◽  
Vol 50 (3) ◽  
pp. 288-294 ◽  
Author(s):  
J.C. Carvalho ◽  
D. Declerck ◽  
E. De Vos ◽  
J. Kellen ◽  
J.P. Van Nieuwenhuysen ◽  
...  
Keyword(s):  
Oral Health ◽  
Clinical Data ◽  
Health Surveys ◽  
Interrater Reliability ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Validation Process ◽  
Electronic Data ◽  
Kappa Value ◽  
Time Savings

The aims of the present study were to incorporate and to validate the electronic capture of participant-related outcomes into the Oral Survey-B System, which was originally developed for the electronic capture of clinical data. The validation process compared the performances of electronic and handwritten data captures. The hypothesis of noninferiority would be established if participants performed electronic data capture of the questionnaire survey with an effectiveness of at least 95% of that of handwritten data capture. In this multicenter, randomized, one-period crossover study design, participants (n = 261) were allocated to start with either electronic or handwritten data capture. The incorporation of the electronic self-completed questionnaire into the Oral Survey-B System was successful. The validation of the electronic questionnaire was performed by participants aged from 18 to 75 years. The interrater reliability of participants performing electronic and handwritten data capture of nonclinical assessments per questionnaire and per entry showed a kappa value of 0.72 (95% CI: 0.53-0.94). The noninferiority of electronic data capture in relation to that of the handwritten data capture and transfer was shown (p < 0.0001; 95% CI: 1.47-2.99). In conclusion, the electronic capture of participant-related outcomes with the Oral Survey-B System, originally designed for capture of clinical data, was validated. The electronic data capture was accurate and limited the number of errors. The participants were able to perform electronic data capture effectively, supporting its implementation in further National Oral Health Surveys. With the consideration of participant preference and time savings, this could lead to the implementation of electronic data capture worldwide in National Oral Health Surveys.

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 Open-source electronic data capture system offered increased accuracy and cost-effectiveness compared with paper methods in Africa

2014 ◽  
Vol 67 (12) ◽  
pp. 1358-1363 ◽  
Author(s):  
David G. Dillon ◽  
Fraser Pirie ◽  
Stephen Rice ◽  
Cristina Pomilla ◽  
Manjinder S. Sandhu ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Open Source ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Electronic Data Capture System

scholarly journals Electronic Data Capture for Registries and Clinical Trials in Orthopaedic Surgery: Open Source versus Commercial Systems

2010 ◽  
Vol 468 (10) ◽  
pp. 2664-2671 ◽  
Author(s):  
Jatin Shah ◽  
Dimple Rajgor ◽  
Shreyasee Pradhan ◽  
Mariana McCready ◽  
Amrapali Zaveri ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Open Source ◽  
Orthopaedic Surgery ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data

scholarly journals Electronic data collection in epidemiological research

2016 ◽  
Vol 07 (03) ◽  
pp. 672-681 ◽  
Author(s):  
Aluísio Barros ◽  
Cauane Blumenberg
Keyword(s):  
Data Collection ◽  
Data Quality ◽  
Birth Cohort ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Epidemiological Research ◽  
Electronic Data Collection ◽  
And Control

SummaryThis paper describes the use of Research Electronic Data Capture (REDCap) to conduct one of the follow-up waves of the 2004 Pelotas birth cohort. The aim is to point out the advantages and limitations of using this electronic data capture environment to collect data and control every step of a longitudinal epidemiological research, specially in terms of time savings and data quality.We used REDCap as the main tool to support the conduction of a birth cohort follow-up. By exploiting several REDCap features, we managed to schedule assessments, collect data, and control the study workflow. To enhance data quality, we developed specific reports and field validations to depict inconsistencies in real time.Using REDCap it was possible to investigate more variables without significant increases on the data collection time, when comparing to a previous birth cohort follow-up. In addition, better data quality was achieved since negligible out of range errors and no validation or missing inconsistencies were identified after applying over 7,000 interviews.Adopting electronic data capture solutions, such as REDCap, in epidemiological research can bring several advantages over traditional paper-based data collection methods. In favor of improving their features, more research groups should migrate from paper to electronic-based epidemiological research.Citation: Blumenberg C, Barros AJD. Electronic data collection in epidemiological research: The use of REDCap in the Pelotas birth cohorts

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.

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