ArticleJAMIA open2024
Integrated electronic health record tools to access real-world data in oncology research.
Article in JAMIA open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
- An Integrative Review of Computational Methods Applied to Biomarkers, Psychological Metrics, and Behavioral Signals for Early Cancer Risk Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Challenges in Automating Extraction of Real-World Radiographic Images and Adverse Events: Lessons From the ICAREdata Initiative.JCO clinical cancer informatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: The Integrating Clinical Trials and Real-World Endpoints (ICAREdata) project aimed to demonstrate that electronic health record (EHR) data, expressed in a standard structured format, can be extracted and transmitted to contribute to clinical research. Using the minimal Common Oncology Data Elements (mCODE), we collected standardized oncology outcome data from EHRs across 10 clinical sites and 15 trials. This report details and assesses the ICAREdata technical implementation and offers recommendations to benefit future projects with similar goals. Materials and Methods: In the ICAREdata project, we implemented tools to collect structured clinical outcome data within EHRs, then extract and transmit the mCODE-formatted data to a secure cloud storage platform. Using the socio-technical model of health information technology, we systematically assessed the technical implementations in this multi-institutional project. Results: We evaluated the ICAREdata method across the 8 inter-related dimensions of the socio-technical model. For each dimension, we identified challenges and developed recommendations that implementers of future initiatives can leverage. Discussion: The ICAREdata project successfully demonstrated the feasibility of using data standards for structured data capture and transmission in clinical trials. The lessons learned from this project can accelerate the success of similar initiatives using standards-based real-world data (RWD) capture and transmission for research. Conclusion: The ICAREdata project represents a step towards a future where researchers can access high-quality, standardized RWD leading to advances in research and improved care delivery.
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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.