Observational studyInternational journal of medical informatics2017
A comparative effectiveness study of eSource used for data capture for a clinical research registry.
Observational study in International journal of medical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- Effectiveness of remote risk-based monitoring and potential benefits for combination with direct data capture.Trials · 2024Trial
- Advancing Clinical Trial Efficiency and Data Accuracy through Direct EHR-to-EDC Integration.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Systematic data capture reduces the need for source data verification: exploratory analysis from a phase 2 multicenter randomized controlled platform trial.Communications medicine · 2025Article
- Evaluating the Impact of Electronic Health Record to Electronic Data Capture Technology on Workflow Efficiency: a Site Perspective.JAMIA open · 2025Article
- Evaluating the positive predictive value of code-based identification of cirrhosis and its complications utilizing GPT-4.Hepatology (Baltimore, Md.) · 2025Article
- Utility of automated data transfer for cancer clinical trials and considerations for implementation.ESMO real world data and digital oncology · 2025Article
- Automating Data Entry from Electronic Health Record to Electronic Data Capture Using a Trusted Cloud-Based Application in Multisite Cancer Clinical Trials.Journal of the Society for Clinical Data Management · 2025Article
- Lessons learned from setting up a hospital-based national registry for venous thromboembolic disorders in India.Journal of family medicine and primary care · 2024Article
- Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data.Nuklearmedizin. Nuclear medicine · 2023Review
- Integrating REDCap patient-reported outcomes with the HealtheIntent population health platform: proof of concept.JAMIA open · 2023Article
- Infrastructure tools to support an effective Radiation Oncology Learning Health System.Journal of applied clinical medical physics · 2023Article
- Perceptions of chief clinical information officers on the state of electronic health records systems interoperability in NHS England: a qualitative interview study.BMC medical informatics and decision making · 2023Article
- TransFAIR study: a European multicentre experimental comparison of EHR2EDC technology to the usual manual method for eCRF data collection.BMJ health & care informatics · 2023Article
- Decentralized research technology use in multicenter clinical research studies based at U.S. academic research centers.Journal of clinical and translational science · 2023Article
- Evaluating automated electronic case report form data entry from electronic health records.Journal of clinical and translational science · 2023Article
- Collaborative Program to Evaluate Real World Data for Use in Clinical Studies and Regulatory Decision Making.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Electronic Source Data Transcription for Electronic Case Report Forms in China: Validation of the Electronic Source Record Tool in a Real-world Ophthalmology Study.JMIR formative research · 2022Article
- Evaluation of the clinical application effect of eSource record tools for clinical research.BMC medical informatics and decision making · 2022Article
- Applying computable phenotypes within a common data model to identify heart failure patients for an implantable cardiac device registry.International journal of cardiology. Heart & vasculature · 2022Article
- Comparing automated vs. manual data collection for COVID-specific medications from electronic health records.International journal of medical informatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
objectiveThis pilot study compared eSource-enabled versus traditional manual data transcription (non-eSource methods) for the collection of clinical registry information. The primary study objective was to compare the time spent completing registry forms using eSource versus non-eSource methods The secondary objectives were to compare data quality associated with these two data capture methods and the flexibility of the workflows. This study directly addressed fundamental questions relating to eSource adoption: what time-savings can be realized, and to what extent does eSource improve data quality. MATERIALS AND
methodsThe study used time and motion methods to compare eSource versus non-eSource data capture workflows for a single center OB/GYN registry. Direct observation by industrial engineers using specialized computer software captured keystrokes, mouse clicks and video recordings of the study team in their normal work environment completing real-time data collection.
resultsThe overall average data capture time was reduced with eSource versus non-eSource methods (difference, 151s per case; eSource, 1603s; non-eSource, 1754s; p=0.051). The average data capture time for the demographic data was reduced (difference, 79s per case; eSource, 133s; non-eSource, 213s; p<0.001). This represents a 37% time reduction (95% confidence interval 27% to 47%). eSourced data field transcription errors were also reduced (eSource, 0%; non-eSource, 9%).
conclusionThe use of eSource versus traditional data transcription was associated with a significant reduction in data entry time and data quality errors. Further studies in other settings are needed to validate these results.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.