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

J Clin Epidemiol

International Health Research Group, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, United Kingdom; Genetic Epidemiology Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1HH, United Kingdom. Electronic address:

Published: December 2014

Objectives: Existing electronic data capture options are often financially unfeasible in resource-poor settings or difficult to support technically in the field. To help facilitate large-scale multicenter studies in sub-Saharan Africa, the African Partnership for Chronic Disease Research (APCDR) has developed an open-source electronic questionnaire (EQ).

Study Design And Setting: To assess its relative validity, we compared the EQ against traditional pen-and-paper methods using 200 randomized interviews conducted in an ongoing type 2 diabetes case-control study in South Africa.

Results: During its 3-month validation, the EQ had a lower frequency of errors (EQ, 0.17 errors per 100 questions; paper, 0.73 errors per 100 questions; P-value ≤0.001), and a lower monetary cost per correctly entered question, compared with the pen-and-paper method. We found no marked difference in the average duration of the interview between methods (EQ, 5.4 minutes; paper, 5.6 minutes).

Conclusion: This validation study suggests that the EQ may offer increased accuracy, similar interview duration, and increased cost-effectiveness compared with paper-based data collection methods. The APCDR EQ software is freely available (https://github.com/apcdr/questionnaire).

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4271740PMC
http://dx.doi.org/10.1016/j.jclinepi.2014.06.012DOI Listing

Publication Analysis

Top Keywords

open-source electronic
8
electronic data
8
data capture
8
increased accuracy
8
cost-effectiveness compared
8
errors 100
8
100 questions
8
capture system
4
system offered
4
offered increased
4

Similar Publications

Objectives: Cost-effectiveness analysis (CEA) is an accepted approach to evaluate cancer screening programmes. CEA estimates partially depend on modelling methods and assumptions used. Understanding common practice when modelling cancer relies on complete, accessible descriptions of prior work.

View Article and Find Full Text PDF

PathwayPilot: A User-Friendly Tool for Visualizing and Navigating Metabolic Pathways.

Mol Cell Proteomics

January 2025

VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium; Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.

Metaproteomics, the study of collective proteomes in environmental communities, plays a crucial role in understanding microbial functionalities affecting ecosystems and human health. Pathway analysis offers structured insights into the biochemical processes within these communities. However, no existing tool effectively combines pathway analysis with peptide- or protein-level data.

View Article and Find Full Text PDF

Humanitarian medical response to natural and human-made disasters can be complicated by high clinician, staff, and patient turnover. While electronic medical records are being scaled up globally, their use remains limited in humanitarian response settings. The Fast Electronic Medical Record (fEMR) system is an open-source electronic health record system specifically designed for use in resource-limited settings and humanitarian crises.

View Article and Find Full Text PDF

Capacitive dielectric temperature sensors based on polydimethylsiloxane (PDMS) loaded with 10 vol% of inexpensive, commercially-available conductive fillers including copper, graphite, and milled carbon fiber (PDMS-CF) powders are reported. The sensors are tested in the range of 20-110 °C and from 0.5 to 200 MHz, with enhanced sensitivity from 20 to 60 °C, and a relative response of 85.

View Article and Find Full Text PDF

Scalable information extraction from free text electronic health records using large language models.

BMC Med Res Methodol

January 2025

Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030-R, Boston, MA, 02120, USA.

Background: A vast amount of potentially useful information such as description of patient symptoms, family, and social history is recorded as free-text notes in electronic health records (EHRs) but is difficult to reliably extract at scale, limiting their utility in research. This study aims to assess whether an "out of the box" implementation of open-source large language models (LLMs) without any fine-tuning can accurately extract social determinants of health (SDoH) data from free-text clinical notes.

Methods: We conducted a cross-sectional study using EHR data from the Mass General Brigham (MGB) system, analyzing free-text notes for SDoH information.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!