scholarly journals Clinical Data Capture Document

2020 ◽  
Author(s):  
Keyword(s):  
Clinical Data ◽  
Data Capture

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 Core Data Elements in Acute Myeloid Leukemia: A Unified Medical Language System–Based Semantic Analysis and Experts’ Review (Preprint)

2019 ◽  
Author(s):  
Christian Holz ◽  
Torsten Kessler ◽  
Martin Dugas ◽  
Julian Varghese
Keyword(s):  
Acute Myeloid Leukemia ◽  
Clinical Data ◽  
Myeloid Leukemia ◽  
Data Capture ◽  
Large Set ◽  
Unified Medical Language System ◽  
Medical Language ◽  
Data Elements ◽  
Medical Concepts ◽  
Acute Myeloid

BACKGROUND For cancer domains such as acute myeloid leukemia (AML), a large set of data elements is obtained from different institutions with heterogeneous data definitions within one patient course. The lack of clinical data harmonization impedes cross-institutional electronic data exchange and future meta-analyses. OBJECTIVE This study aimed to identify and harmonize a semantic core of common data elements (CDEs) in clinical routine and research documentation, based on a systematic metadata analysis of existing documentation models. METHODS Lists of relevant data items were collected and reviewed by hematologists from two university hospitals regarding routine documentation and several case report forms of clinical trials for AML. In addition, existing registries and international recommendations were included. Data items were coded to medical concepts via the Unified Medical Language System (UMLS) by a physician and reviewed by another physician. On the basis of the coded concepts, the data sources were analyzed for concept overlaps and identification of most frequent concepts. The most frequent concepts were then implemented as data elements in the standardized format of the Operational Data Model by the Clinical Data Interchange Standards Consortium. RESULTS A total of 3265 medical concepts were identified, of which 1414 were unique. Among the 1414 unique medical concepts, the 50 most frequent ones cover 26.98% of all concept occurrences within the collected AML documentation. The top 100 concepts represent 39.48% of all concepts’ occurrences. Implementation of CDEs is available on a European research infrastructure and can be downloaded in different formats for reuse in different electronic data capture systems. CONCLUSIONS Information management is a complex process for research-intense disease entities as AML that is associated with a large set of lab-based diagnostics and different treatment options. Our systematic UMLS-based analysis revealed the existence of a core data set and an exemplary reusable implementation for harmonized data capture is available on an established metadata repository.

scholarly journals CLINICAL DATA MANAGEMENT IMPORTANCE IN CLINICAL RESEARCH

2016 ◽  
pp. 59
Author(s):  
Deepa Murugesan ◽  
Ranganath Banerjee ◽  
Gopal Ramesh Kumar
Keyword(s):  
Clinical Trial ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Drug Approval ◽  
Trial Data ◽  
Data Capture ◽  
Data Management System ◽  
Pharmaceutical Companies ◽  
Management Tools

<p>ABSTRACT<br />Over the last few decades, most of the pharmaceutical companies and research sponsors are facing a lot of challenges in clinical research for their<br />new drug approval. The sponsor research needs a high-quality data report for getting new drug approval from Food and Drug Administration for their<br />medical products. Clinical trial data are important for the drug and medical device development processing pharmaceutical companies to examine<br />and evaluate the efficacy and safety of the new medical product in human volunteers. The results of the clinical trial studies generate the most<br />valuable data and in recent years; there has been massive development in the field of clinical trials. A good clinical data management system reduces<br />the duration of the study and cost of drug development. Further a well-designed case report form (CRF) assists data collection and make facilitates<br />data management and statistical analysis. Nowadays, the electronic data capture (EDC) is very beneficial in data collection. EDC helps to speed up the<br />clinical trial process and reduces the duration, errors and make the work easy in the data management system. This article highlights the importance<br />of data management processes involved in the clinical trial and provides an overview of the clinical trial data management tools. The study concluded<br />that data management tools play a key role in the clinical trial and well-designed CRFs reduces the errors and save the time of the clinical trials and<br />facilitates the drug discovery and development.<br />Keywords: Pharmaceutical, Clinical trial, Clinical data management, Data capture.</p>

scholarly journals The future state of clinical data capture and documentation: a report from AMIA's 2011 Policy Meeting

2013 ◽  
Vol 20 (1) ◽  
pp. 134-140 ◽  
Author(s):  
C. M. Cusack ◽  
G. Hripcsak ◽  
M. Bloomrosen ◽  
S. T. Rosenbloom ◽  
C. A. Weaver ◽  
...  
Keyword(s):  
Clinical Data ◽  
Data Capture ◽  
Future State ◽  
The Future

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.

scholarly journals Automating Electronic Clinical Data Capture for Quality Improvement and Research: The CERTAIN Validation Project of Real World Evidence

2018 ◽  
Vol 6 (1) ◽  
pp. 8
Author(s):  
Emily Beth Devine ◽  
Erik Van Eaton ◽  
Megan E. Zadworny ◽  
Rebecca Symons ◽  
Allison Devlin ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Clinical Data ◽  
Real World ◽  
Data Capture ◽  
Real World Evidence
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