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 Procedure to integrate i2b2 and REDCap: a case study at ICSM

2017 ◽  
Author(s):  
Valentina Tibollo ◽  
Mauro Bucalo ◽  
Danila Vella ◽  
Morena Stuppia ◽  
Nicola Barbarini ◽  
...  
Keyword(s):  
Data Warehouse ◽  
Clinical Data ◽  
General Procedure ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Web Based ◽  
Research Activities ◽  
Successful Approach

REDCap (Research Electronic Data Capture) is one of the most popular web-based applications to support data capture for research studies and registries. i2b2 (Informatics for Integrating Biology and the Bedside) is a widely adopted data warehouse to re-use clinical data for research purposes. A general procedure able to integrate these solutions could facilitate research activities in several institutions. Starting from the principles adopted by the SEINE approach, one of the most successful approach designed to i2b2-REDCap integration, we proposed a general and flexible ETL (Extract Transform and Load) procedure for synchronizing an i2b2 project with a REDCap study.

scholarly journals Procedure to integrate i2b2 and REDCap: a case study at ICSM

2017 ◽  
Author(s):  
Valentina Tibollo ◽  
Mauro Bucalo ◽  
Danila Vella ◽  
Morena Stuppia ◽  
Nicola Barbarini ◽  
...  
Keyword(s):  
Data Warehouse ◽  
Clinical Data ◽  
General Procedure ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Web Based ◽  
Research Activities ◽  
Successful Approach

REDCap (Research Electronic Data Capture) is one of the most popular web-based applications to support data capture for research studies and registries. i2b2 (Informatics for Integrating Biology and the Bedside) is a widely adopted data warehouse to re-use clinical data for research purposes. A general procedure able to integrate these solutions could facilitate research activities in several institutions. Starting from the principles adopted by the SEINE approach, one of the most successful approach designed to i2b2-REDCap integration, we proposed a general and flexible ETL (Extract Transform and Load) procedure for synchronizing an i2b2 project with a REDCap study.

OP16.04: User satisfaction with the Clinical Data Miner electronic data capture software

2014 ◽  
Vol 44 (S1) ◽  
pp. 100-100
Author(s):  
A. Installé ◽  
T. Van den Bosch ◽  
T. Bourne ◽  
B. De Moor ◽  
D. Timmerman
Keyword(s):  
Clinical Data ◽  
User Satisfaction ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data

scholarly journals OC17.06: Clinical Data Miner (CDM)-a web-based electronic data capture framework for multi-centric studies with imaging modalities

2011 ◽  
Vol 38 (S1) ◽  
pp. 33-33
Author(s):  
A. Installe ◽  
T. Van den Bosch ◽  
D. Van Schoubroeck ◽  
J. Heymans ◽  
L. Zannoni ◽  
...  
Keyword(s):  
Clinical Data ◽  
Data Capture ◽  
Imaging Modalities ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Web Based

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.

scholarly journals 238. Novel Way to Evaluate Antibiotic Use Appropriateness: Moving Towards the “Never Event” Classification by Electronic Algorithm

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S119-S120
Author(s):  
Twisha S Patel ◽  
Lindsay A Petty ◽  
Jiajun Liu ◽  
Marc H Scheetz ◽  
Nicholas Mercuro ◽  
...  
Keyword(s):  
Urine Culture ◽  
Asymptomatic Bacteriuria ◽  
Antibiotic Use ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
False Positivity ◽  
Never Event ◽  
Manual Review

Abstract Background Antibiotic use is commonly tracked electronically by antimicrobial stewardship programs (ASPs). Traditionally, evaluating the appropriateness of antibiotic use requires time- and labor-intensive manual review of each drug order. A drug-specific “appropriateness” algorithm applied electronically would improve the efficiency of ASPs. We thus created an antibiotic “never event” (NE) algorithm to evaluate vancomycin use, and sought to determine the performance characteristics of the electronic data capture strategy. Methods An antibiotic NE algorithm was developed to characterize vancomycin use (Figure) at a large academic institution (1/2016–8/2019). Patients were electronically classified according to the NE algorithm using data abstracted from their electronic health record. Type 1 NEs, defined as continued use of vancomycin after a vancomycin non-susceptible pathogen was identified, were the focus of this analysis. Type 1 NEs identified by automated data capture were reviewed manually for accuracy by either an infectious diseases (ID) physician or an ID pharmacist. The positive predictive value (PPV) of the electronic data capture was determined. Antibiotic Never Event (NE) Algorithm to Characterize Vancomycin Use Results A total of 38,774 unique cases of vancomycin use were available for screening. Of these, 0.6% (n=225) had a vancomycin non-susceptible pathogen identified, and 12.4% (28/225) were classified as a Type 1 NE by automated data capture. All 28 cases included vancomycin-resistant Enterococcus spp (VRE). Upon manual review, 11 cases were determined to be true positives resulting in a PPV of 39.3%. Reasons for the 17 false positives are given in Table 1. Asymptomatic bacteriuria (ASB) due to VRE in scenarios where vancomycin was being appropriately used to treat a concomitant vancomycin-susceptible infection was the most common reason for false positivity, accounting for 64.7% of false positive cases. After removing urine culture source (n=15) from the algorithm, PPV improved to 53.8%. Conclusion An automated vancomycin NE algorithm identified 28 Type 1 NEs with a PPV of 39%. ASB was the most common cause of false positivity and removing urine culture as a source from the algorithm improved PPV. Future directions include evaluating Type 2 NEs (Figure) and prospective, real-time application of the algorithm. Disclosures Marc H. Scheetz, PharmD, MSc, Merck and Co. (Grant/Research Support)

How Did a Multi-Institutional Trial Show Feasibility of Electronic Data Capture in Older Patients With Cancer? Results From a Multi-Institutional Qualitative Study (Alliance A171902)

2021 ◽  
pp. 442-449
Author(s):  
Nichole A. Martin ◽  
Elizabeth S. Harlos ◽  
Kathryn D. Cook ◽  
Jennifer M. O'Connor ◽  
Andrew Dodge ◽  
...  
Keyword(s):  
Older Patients ◽  
Electronic Devices ◽  
Data Entry ◽  
Data Capture ◽  
Data Availability ◽  
Electronic Data Capture ◽  
Wireless Internet ◽  
Electronic Data ◽  
Patients With Cancer ◽  
Patient Reported

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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