ArticleJournal of clinical and translational science2023
Evaluating automated electronic case report form data entry from electronic health records.
Article in Journal of clinical and translational science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Observational
- Feasibility assessment of an EHR-integrated research platform for prospective data collection in community oncology practice.npj health systems · 2026Article
- Leveraging Machine Learning and Robotic Process Automation to Identify and Convert Unstructured Colonoscopy Results Into Actionable Data: Proof-of-Concept Study.JMIR medical informatics · 2025Article
- Statistical Methods to Adjust for Treatment Switching in Real-World Clinical Studies: A Scoping Review and Descriptive Comparison.Clinical pharmacology and therapeutics · 2025Article
- The AI and Automation in Anaesthesia (TRIPLE-A) Perioperative Database in Eastern Denmark 2017-: A Platform for Epidemiology, AI-Driven Prediction, Quality Control and Automated Data Retrieval.Acta anaesthesiologica Scandinavica · 2025Article
- Article
- 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 National Heart, Lung, and Blood Institute Covid-19 Clinical Trials.NEJM evidence · 2024Article
- Clinical Research Informatics: a Decade-in-Review.Yearbook of medical informatics · 2024Review
- Applying Artificial Intelligence in Pediatric Clinical Trials: Potential Impacts and Obstacles.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2024Article
- A pragmatic methodology to extract anesthetic and physiological data from the electronic health record.Paediatric anaesthesia · 2024Article
- An Overview of Data Management in Human Subjects Research.Respiratory care · 2024Review
- Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data.Nuklearmedizin. Nuclear medicine · 2023Review
- Electronic health record data quality variability across a multistate clinical research network.Journal of clinical and translational science · 2023Article
- Using artificial intelligence to improve public health: a narrative review.Frontiers in public health · 2023Review
- Decentralized clinical trials in the trial innovation network: Value, strategies, and lessons learned.Journal of clinical and translational science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
Background: Many clinical trials leverage real-world data. Typically, these data are manually abstracted from electronic health records (EHRs) and entered into electronic case report forms (CRFs), a time and labor-intensive process that is also error-prone and may miss information. Automated transfer of data from EHRs to eCRFs has the potential to reduce data abstraction and entry burden as well as improve data quality and safety. Methods: We conducted a test of automated EHR-to-CRF data transfer for 40 participants in a clinical trial of hospitalized COVID-19 patients. We determined which coordinator-entered data could be automated from the EHR (coverage), and the frequency with which the values from the automated EHR feed and values entered by study personnel for the actual study matched exactly (concordance). Results: The automated EHR feed populated 10,081/11,952 (84%) coordinator-completed values. For fields where both the automation and study personnel provided data, the values matched exactly 89% of the time. Highest concordance was for daily lab results (94%), which also required the most personnel resources (30 minutes per participant). In a detailed analysis of 196 instances where personnel and automation entered values differed, both a study coordinator and a data analyst agreed that 152 (78%) instances were a result of data entry error. Conclusions: An automated EHR feed has the potential to significantly decrease study personnel effort while improving the accuracy of CRF data.
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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.